UNIVERSITÁ DIPISA
DOTTORATO DI RICERCA ININGEGNERIA DELL’INFORMAZIONE
U
NIVERSAL
F
ILTERED
M
ULTICARRIER
(UFMC):
F
ROM THEORY TO PRACTICE
DOCTORALTHESIS
Author
Carmine Vitiello
Tutor (s)
Prof. Marco Luise
Reviewer (s)
Prof. Marc Moeneclaey, Prof. Florian Kaltenberger
The Coordinator of the PhD Program
Prof. Marco Luise
Pisa, September 2016 Cycle XXIX
Summary
T
HISthesis focuses on the development of the Universal Filtered Multicarrier(UFMC) waveform, proposed as a candidate to 5G physical layer. Unlike OFDM, this waveform groups a certain number of subcarriers into a subband, performing a signal processing and applying a filtering operation on that. In this way, UFMC provides a further degree of freedom in terms of flexibility, improving the robustness against frequency and time misalignments. Given that no resource allocation technique has been found in literature, different resource allocation strategies are proposed for further improving the UFMC performance, maximizing a figure of merit as goodput in short packet communications and assuming both perfect and imperfect synchroniza-tion. These methods are able to select the best transmission parameters, namely code rate, modulation, power and number of transmitted multicarrier symbols, using an it-erative greedy algorithm and exploiting the granularity of the waveform. Moreover, the UFMC waveform has been implemented thanks by an open-source development framework called OpenAirInterface, over the PUSCH LTE channel, just substituting OFDM modulator with a reduced-complexity UFMC one, and including a timing syn-chronization block for enabling the standard PUSCH receiver to receive UFMC signal, therefore proving the coexistence between the waveforms. The performance in terms of computational complexity and block error rate prove the qualities of the UFMC on the typical application scenarios, especially when coarse synchronization is assumed.Sommario
I
Nquesta tesi viene descritto lo sviluppo della forma d’onda chiamata Universal Fil-tered Multicarrier (UFMC), tra le maggiori candidate a diventare il livello fisico della prossima generazione di rete cellulare 5G. A differenza dell’OFDM, questa forma d’onda raggruppa un certo numero di sottoportanti in una sottobanda, dove il segnale viene elaborato e filtrato. In questo modo, l’UFMC riesce a fornire un ulteriore grado di libertà in termini di flessibilità, oltre che migliorare la robustezza del segna-le contro disallineamenti temporali e frequenziali. Dato che in segna-letteratura non viene menzionata nessuna tecnica di allocazione delle risorse applicata all’UFMC, sono state proposte diverse strategie capaci di migliorare ulteriormente le prestazioni della forma d’onda, massimizzando un parametro di qualità chiamato goodput, in comunicazioni con pacchetti di piccola dimensione, sia in caso di perfetta che imperfetta sincronizza-zione. Questi metodi si basano sulla scelta dei migliori parametri di trasmissione, rap-presentati dal tasso di codifica, modulazione, potenza e numero di simboli multicarrier trasmessi, usando un algoritmo iterativo greedy e sfruttando la granularità della forma d’onda. Inoltre, viene mostrata un implementazione dell’UFMC tramite l’utilizzo di un framework di sviluppo open-source chiamato OpenAirInterface. L’implementazione è avvenuta sul canale dati uplink PUSCH dello standard LTE, sostituendo il modulatore OFDM con un modulatore UFMC a bassa complessità computazionale mentre al ricevi-tore è stato utilizzato il classico riceviricevi-tore PUSCH OFDM, semplicemente aggiungendo un blocco di sincronizazione temporale in maniera tale da permettere la ricezione del segnale UFMC. I risultati ottenuti in termini di block error rate e di complessità com-putazionale hanno dimostrato e confermato le qualità della forma d’onda UFCM nello scenario di applicazione tipico, specialmente in caso di sincronizzazione non perfetta.List of publications
International Journals
1. Arreghini, F., Vitiello, C., Luise, M., Manco A., Bacci G., Falzarano M. (2016). An Approach to T&E of Military SDR Platforms and Waveforms: the LANCERS Lab. Journal of Signal Processing Systems. (Vol. 83, pp.93-111). Springer US.
International Conferences/Workshops with Peer Review
1. Vitiello C., Pfletschinger S. and Luise M. (2013, June). Decoding options for trellis codes in the two-way relay channel. 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC). (pp.380-384), IEEE 2. Pfletschinger S., Vitiello C. and Navarro M. (2014 June). Decoding options for the symmetric and asymmetric turbo-coded two-way relay channel. EuCNC 2014, European Conference on Networks and Communications, (Vol. 23 pp.26), IEEE 3. Vitiello C., Bacci G., Arreghini F. and Luise M. Low-cost fully-software
wave-forms for tactical communications. WinnCom Europe 2014, Wireless Innovation Forum European Conference. Wireless Innovation Forum
4. Arreghini F., Vitiello C., Luise M., Manco A., Bacci G., Falzarano M. (2014, November). An approach to T&E of military SDR platforms and waveforms: the LANCERS lab. WinnCom Europe 2014, Wireless Innovation Forum European Conference. Wireless Innovation Forum.
5. Kaltenberger F., Knopp R., Vitiello C., Danneberg M. and Festag A. (2015, Oc-tober). Experimental analysis of 5G candidate waveforms and their coexistence with 4G systems. JNCW 2015, Joint NEWCOM/COST Workshop on Wireless Communications
6. Knopp R., Kaltenberger F., Vitiello C. and Luise, M. (2016, July). Universal filtered multicarrier for machine type communications in 5G. EuCNC 2016, Eu-ropean Conference on Networks and Communications. IEEE
7. Del Fiorentino P., Vitiello C., Lottici V., Giannetti F. and Luise M. (2016, Septem-ber). A robust resource allocation algorithm for packet BIC-UFMC 5G wireless communications. EUSIPCO.IEEE
8. Vitiello C., Del Fiorentino P., Debels E., Lottici V., Giannetti F., Luise M. and M. Moeneclaey M.(2016, September). Two-Step Resource Allocation for BIC-UFMC Wireless Communications ISWCS 2016, International Symposium on Wire-less Communication System.IEEE
9. Del Fiorentino P., Vitiello C., Debels E., Van Hecke J., Lottici V., Giannetti F., Luise M. and M. Moeneclaey M.(2016, December). Resource allocation in short packets BIC-UFMC transmission for Internet of Things. Globecom 2016. IEEE 10. Debels E., Del Fiorentino P., Vitiello C., Van Hecke J., Giannetti F., Luise M.,
Lot-tici V. and M. Moeneclaey M.(2016, November). Adaptive modulation and coding for BIC-UFMC and BIC-OFDM systems taking CFO into account. Communica-tions and Vehicular Technology in the Benelux (SCVT), 2016 IEEE Symposium on. IEEE
Others
1. Arreghini, F., Vitiello C., Luise M., Manco A., Bacci G., Falzarano M. (2016, May). Test and evaluation of military SDR platforms and waveforms: Initial out-comes from the laboratory funded by the Italian Ministry of Defense. NATO IST 123 symposium cognitive radio and future networks. NATO
2. Deliverable 2 - Project LANCERS - Technical report
3. Deliverable D11.2 of the European project NEWCOM # - External Research Re-port - Fundamental issues and preliminary results of N# JRAs on opRe-portunistic and cooperative communications
4. Deliverable D23.4 of the European project NEWCOM # - External Research Re-port - Final reRe-port on tools and their integration on the experimental setups 5. Deliverable D35.3 of the European project NEWCOM # - External Research
Re-port - ReRe-port on third-year mobility and awards
6. Deliverable of the ESA Project “GNSS-SIS SW tool” - User Manual 7. Deliverable 1 - Project LICOLA - Technical report
8. Deliverable 2 - Project LICOLA - Technical report 9. Deliverable 3 - Project LICOLA - Technical report
List of Abbreviations
κESM κ-effective SNR mapping. 30–33 4G Forth generation. 5, 6
5G Fifth generation. 4, 61 ACK acknowledgement. 27 ADC analog-digital convertor. 10 AGP average goodput. 36, 42, 57
AIC active interference cancellation. 20 ALU Alcatel-Lucent. XV, 65, 72–74
AMC adaptive modulation and coding. 24, 33, 35 ARQ automatic repeat request. 27
AWGN additive white gaussian noise. 31 BA bit allocation. 25, 26
BCCH broadcast control channel. 8 BCH broadcast channel. 8
BER bit error rate. 27, 28, 30
BIC Bit Interleaved Coded. 24, 25, 30, 38 BIC-OFDM Bit Interleaved Coded - OFDM. XV, 27,
30, 33, 35, 42, 46, 57, 58
BIC-UFMC Bit Interleaved Coded - UFMC. XV, 38, 39, 42, 43, 46, 57, 58
BIOS binary-input output-symmetric. 30 BL bit loading. 33
BLER Block Error Rate. XV, 69, 71, 72, 75, 76 BPSK binary phase-shift keying. 31
CCDF complementary cumulative distributive function. 54
CESM capacity effective SNR mapping. 29
CFO Carrier Frequency Offset. 7, 10, 21, 55, 57, 76
CGF cumulant generating function. 30 CIR channel impulse response. 18 CoMP coordinated multi-point. 10 CP cyclic prefix. 8
CP-OFDM Cyclic Prefix-Orthogonal Frequency Divi-sion Multiplexing. 4
CRC cyclic redundancy check. 25, 27
CSI channel state information. 27, 28, 33, 40, 48, 55
DAC digital-analog convertor. 10
DCT dominated convergence theorem. 31 DL-SCH downlink shared channel. 9
DRS demodulation reference signal. 68, 69, 75 EESM exponential effective SNR mapping. 29, 30 EGP expected goodput. 28, 32–36, 40–42 eNB E-UTRAN Node B. 8, 61, 63
ESM effective SNR mapping. 28
f-OFDM Filtered Orthogonal Frequency Division Multiplexing. 12, 13, 16
FBMC Filter Bank Multicarrier. 11, 13, 16 FEC forward error correction. 25
FFT Fast Fourier Transform. 4, 7, 26 FIR finite impulse response. 20
GFDM Generalized Frequency Division Multi-plexing. 11, 15, 67
GP goodput. 27, 28, 36, 37 HD High Definition. 4, 5
HPA High Power Amplifier. 53, 54 IBI Inter band Interference. 7, 57
ICI Intercarrier Interference. 7, 10, 18, 21, 43, 56, 57, 69
IFFT Inverse Fast Fourier Transform. 7, 26 IoT Internet of Things. 4, 6, 8, 9, 47, 55, 61, 73 ISI Intersymbol Interference. 7, 18, 43
KKT Karush–Kuhn–Tucker. 34, 37, 86, 87 LDD Lagrange dual decomposition. 33, 34
LESM logarithmic effective SNR mapping. 30 LLR log-likelihood ratio. 30, 33
LPE link performance evaluation. 28, 31, 32 LRA link resource adaptation. 27, 28
LTE Long-Term Evolution. 4, 7, 8, 10, 61, 62 LUT lookup table. 32
M2M Machine-To-Machine. 61 MAC media access control. 25
MCS modulation and coding schemes. 62, 70 MGF moment generating function. 30, 31 MIB master information block. 8
MIESM mutual information effective SNR map-ping. 29, 30
MIMO Multiple-Input Multiple-Output. 7 MSE mean square error. 19, 20, 43 MTC Machine-Type-Communications. 7 NPR nearly perfect reconstruction. 14 OAI OpenAirInterface. 63, 68, 69, 74
OFDM Orthogonal Frequency Division Multiplex-ing. 7, 10, 11, 16, 24, 25, 27, 28, 61, 62, 67, 70, 71
OFDMA Orthogonal Frequency-Division Multiple Access. 5, 7
OOB Out-Of-Band. 10, 15, 16, 66
OP optimization problem. 28, 33–35, 40, 41, 50
OPA-OBA optimal power allocation and optimal bit al-location. 52, 57–59
OPA-UBA optimal power allocation and uniform bit allocation. XIV, XV, 37, 38, 42, 46, 52, 57, 58
OQAM Offset QAM. 15, 16
PA power allocation. 25–27, 33–35, 40, 86, 87 PAPR peak-to-average power ratio. 10, 53
PBCH physical broadcast channel. 8 PCI physical cell id. 8
PDU protocol data units. 25, 27 PEP pairwise error probability. 30, 31
PER packet error rate. 27–29, 31, 32, 34, 39, 40 PHICH physical hybrid ARQ indicator channel. 8 PPN Polyphase Network. 14
PSS Primary Synchronization Signal. 8 PUCCH physical uplink control channel. 8 PUSCH physical uplink shared channel. 8, 62 QAM quadrature amplitude modulation. 31 RA resource allocation. 24, 27, 33, 35, 39, 42,
57
RA-RNTI random access radio network temporary identity. 9
RACH random-access channel. 8 RB resource block. 62, 75 RE resource element. 62 RLC radio link control. 25, 27 RRC radio resource control. 9 RTT Round-Trip Time. 5
SC-FDMA Single-Carrier Frequency-Division Multi-ple Access. 7, 16, 61, 62, 66, 67
SC-UFMC Single-Carrier Universal Filtered Multicar-rier. 66, 70
SDLR Signal to in-band Distortion plus out-of-band Leakage Ratio. 20
SDR Software-Defined Radio. 63 SFN system frame number. 8 SIB system information block. 8
SIMD Single Instruction Multiple Data. 63, 74 SLL side lobe level. 20, 22, 64
SLR Signal to out-of-band Leakage Ratio. 20 SNR Signal to Noise Ratio. 27, 28, 30, 31, 33,
34, 42, 48, 57
SQNR signal-to-quantization noise ratio. 10 SRS sounding reference signal. 8
SSS Secondary Synchronization Signal. 8 TM transmission mode. 27, 28, 32–35, 37, 41 TMSI temporary mobile subscriber identity. 9 TO Timing Offset. 7
TP transmission parameter. 27, 33, 50 UBL uniform bit loading. 35
UE User Equipment. 8, 61, 63
UFMC Universal Filtered Multicarrier. 16, 24, 38, 55, 61, 62, 70, 72
UPA-UBA uniform power allocation and uniform bit allocation. XIV, XV, 37, 38, 42, 46, 52, 57, 58 WF water-filling. 34, 37, 42 ZC Zadoff-Chu. 8 ZF zero forcing. 19 ZL zero loading. 45
Contents
List of Abbreviations VII
List of Figures XIV
List of Tables XVI
1 Introduction 1
1.1 Motivation . . . 1
1.2 Main Contribution . . . 2
1.3 Outline . . . 2
2 The 5G physical layer 4 2.1 Introduction . . . 4
2.2 5G requirements . . . 4
2.2.1 Data Rate . . . 5
2.2.2 Latency . . . 5
2.2.3 Ultra-High Reliability and Availability . . . 6
2.2.4 Very Low Device Cost and Energy Consuption . . . 6
2.2.5 Massive System Capacity . . . 6
2.3 4G drawbacks . . . 7
3 Waveforms 11 3.1 An overview on the main candidates to 5G physical layer . . . 11
3.2 Orthogonal Frequency Division Multiplexing . . . 11
3.2.1 Filtered-OFDM . . . 12
3.3 Filter Bank Multicarrier . . . 13
3.4 Generalized Frequency Division Multiplexing . . . 15
3.5 Universal Filtered MultiCarrier . . . 16
4 Resource Allocation on UFMC 24
4.1 Introduction to Resource Allocation . . . 24
4.2 Resource Allocation on multicarrier system . . . 24
4.2.1 Figure of merit and Effective SNR mapping (ESM) . . . 27
4.2.2 Link performance evaluation . . . 28
4.2.3 GP-based RA in BIC-OFDM systems with perfect CSI . . . 33
4.2.4 Performance of GP-based resource allocation on BIC-OFDM . . 35
4.3 Resource Allocation on Universal Filtered Multicarrier . . . 38
4.3.1 Adapted GP-based Resource Allocation for BIC-UFCM . . . 40
4.3.2 Iterative GP-based Resource Allocation for BIC-UFCM . . . 43
4.3.3 GP-based Resource Allocation for BIC-UFMC short packet com-munication . . . 47
4.4 Effect of Resource allocation on Peak-to-Average Power Ratio . . . 52
4.5 Resource allocation in presence of Carrier Frequency Offset . . . 55
4.5.1 Application of the RA strategies . . . 57
5 UFMC Implementation 61 5.1 Introduction to UFMC implementation . . . 61
5.2 System model of UFMC . . . 61
5.3 OpenAirInterface . . . 63
5.4 Implementation and evaluation of the performance . . . 63
5.4.1 Computational Complexity . . . 70 5.4.2 Timing estimation . . . 73 5.4.3 Frequency misalignments . . . 76 6 Conclusions 81 6.1 Perspectives . . . 82 A Appendix A 84 B Appendix B 86 B.1 Proof of Proposition 1 . . . 86
B.2 Proof of Proposition 2 and 4 . . . 87
B.3 Proof of Proposition 5 . . . 87
List of Figures
2.1 Latency goal on 5G system . . . 6
2.2 RACH procedure . . . 9
3.1 Waveform classification . . . 12
3.2 OFDM waveform . . . 12
3.3 f-OFDM waveform . . . 13
3.4 original scheme of FBMC waveform . . . 13
3.5 PPN-FFT FBMC waveform . . . 14
3.6 GFDM transmitter . . . 15
3.7 frequency-time diagram for OFDM (a), SC-FDMA (b) and GFDM (c) . 16 3.8 UFMC waveform . . . 17
3.9 Elongation of multicarrier symbols before filtering, after filtering and after passing through a multipath channel . . . 18
3.10 Filter shape in time(a) and frequency(b) domain . . . 20
3.11 Comparison between OFDM and UFMC spectra . . . 21
3.12 Subband UFMC spectra . . . 21
3.13 Time-frequency efficiency of UFMC, OFDM and FBMC . . . 23
4.1 BIC-OFDM scheme . . . 25
4.2 Generic link performance model . . . 29
4.3 κESM Link Performance Prediction scheme . . . 32
4.4 Multi-level water-filling interpretation . . . 35
4.5 AGP(a) and EGP(b) of the BIC-OFDM vs SNR . . . 37
4.6 AGP and EGP comparison for uniform power allocation and uniform bit allocation (UPA-UBA) and optimal power allocation and uniform bit allocation (OPA-UBA) . . . 38
4.7 RA on BIC-UFMC system using different adjustment factor βκESM. . . 39
4.8 AGP and EGP comparisons between OPA-UBA and UPA-UBA resource allocation strategies . . . 42
4.9 AGP and EGP comparisons between OPA-UBA BIC-UFMC and UPA-UBA BIC-OFDM resource allocation strategies . . . 43
4.11 Optimal region . . . 48
4.12 AGP and EGP comparisons between OPA-OBA with Greedy algorithm and UPA-UBA resource allocation strategies over BIC-UFMC . . . 48
4.13 AGP and EGP comparisons between OPA-OBA with Greedy algorithm and OPA-UBA resource allocation strategies over BIC-UFMC . . . 49
4.14 AGP and EGP comparisons between OPA-OBA BIC-UFMC with Greedy algorithm and UPA-UBA BIC-OFDM resource allocation strategies . . 49
4.15 Comparison between OPA-OBA, OPA-UBA and UPA-UBA strategies . 53 4.16 PAPR vs SNR . . . 54
4.17 CCDF vs δ . . . 55
4.18 AGP in case of OPA-UBA and UPA-UBA in BIC-UFMC vs CFO at SNR=10dB . . . 58
4.19 EGP-based RA for BIC-UFMC and BIC-OFDM at SNR= 36 dB . . . . 58
4.20 Comparison of the RA strategies for different CFO (SNR = 28 dB) . . . 59
4.21 Comparison between BIC-UFMC and BIC-OFDM when OPA-OBA RA is applied for different CFO (SNR = 28 dB) . . . 60
5.1 SC-FDMA and SC-UFMC transmitter . . . 62
5.2 SC-FDMA modulator . . . 63
5.3 Overview of OAI implementation . . . 64
5.4 Classical SC-UFMC modulator . . . 64
5.5 ALU reduced complexity transmitter . . . 65
5.6 Reduced complexity transmitter . . . 66
5.7 SC-UFMC spectrum(blue) and SC-FDMA spectrum(red) for different IDFT size . . . 67
5.8 Screenshot of the spectrum analyser comparing the spectra of the differ-ent waveforms: OFDM (red), SC-FDMA (blue), SC-UFMC (pink) and GFDM(green) . . . 68
5.9 LTE-adapted receiver . . . 69
5.10 Elongation of SC-UFMC multicarrier symbol and correspondent receiv-ing window . . . 69
5.11 Uplink LTE subframe . . . 70
5.12 BLER in AWGN channel . . . 71
5.13 BLER in flat Rayleigh channel . . . 72
5.14 Complexity of OFDM(black), classical(green), reduced-complexity ALU scheme(blue) and proposed(red) UFMC modulators . . . 74
5.15 Timing estimation for different values of delay δ: 37(a), 38(b), 39(c) and 40(d) . . . 75
5.16 Timing estimation boxplot . . . 76
5.17 BLER performance comparison between SC-UFMC(green δ = 35, cyan δ = 36, blue δ = 38, magenta δ = 40), timing synchronized SC-UFMC(red) and SC-FDMA(black) . . . 77
5.18 Delay estimation when RBs number increases . . . 78
5.19 BLER when RBs number increases . . . 78
5.20 SC-UFMC BLER vs SNR in case of different CFO . . . 79
5.21 SC-FDMA BLER vs SNR in case of different CFO . . . 79
List of Tables
2.1 5G requirements . . . 7
3.1 Spectral efficiency of UFMC with different SLL attenuation and filter length . . . 23
4.1 EGP-based RA pseudo-code for BIC-OFDM with UBL . . . 36
4.2 System parameters . . . 37
4.3 EGP-based RA . . . 41
4.4 RA strategy . . . 47
4.5 RA pseudo-code . . . 52
CHAPTER
1
Introduction
1.1
MotivationNowadays, the era of the fifth generation wireless mobile network is coming, promising one of the biggest revolution in the world of telecommunications. Indeed, new genera-tion is not only synonym of a much faster extension of the actual mobile network but it will represent a new concept of the mobile network in terms kind of entities, spectrum and network management, communication paradigms and so on. 5G denomination includes many of the most recent technologies, signal processing techniques and appli-cations, which promise to change the life of the users. One of the main driver of the new generation is represented by Internet of Things (IoT), where for the first time will be en-abled the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Furthermore, IoT entities will be everywhere, bringing the technology into any moment and any place of our life, starting from the daily rou-tine, such as the domestic appliances or home automation, up to more futuristic appli-cations until now inapplicable, such as telemedicine, secure vehicular communication and so on. For this reason, the new mobile generation cannot be a simple extension of the previous one, and the whole stack of the mobile communication network would be re-thought from scratch. In particular, the physical layer would guarantee the sup-port and the coexistence of several applications with different requirements. Even if OFDM presents a lot of strong aspects, it shows several drawbacks, starting from the high out-of-band emissions, which don’t allow a good exploitation of the spectrum, to its sensitivity to time and frequency misalignments. This last aspect will become really important in 5G: IoT entites indeed would avoid to waste time and energy performing a classical attach procedure to the base station, therefore the communication paradigm with coarse synchronization or asynchronous proves crucial. For this reason, a wide range of new waveforms has been developed and proposed as the new physical layer
of 5G mobile network. Given that these new candidates are quite new, few solutions in terms of optimization methods for the selection of the best transmission parameters have been proposed for further boosting the performance and for improving the weak aspects in some application scenario.
1.2
Main ContributionThis thesis is focused on the development of a new waveform called "Universal Filtered Multicarrier ", which represents one of the best candidate for the physical layer of 5G, because it is designed to provide the flexibility required for future applications. UFMC can be exploited in a scenario potentially asynchronous, characterized by the exchange of small amount of data, identified with the uplink communications of current LTE. In order to have a fair comparison with OFDM or its single carrier version SC-FDMA, the UFMC waveform parameters are assumed as similar as possible with LTE 10 MHz channelization. In this context, the contributions of this thesis can be resumed as follows:
• An advance modulation scheme, such as Bit Interleaved Coded (BIC) modulation, has been applied to UFMC modulation in order to guarantee several degrees of freedom for the optimization of the waveform;
• Classical multicarrier resource allocation methods have been adapted and applied, choosing the best transmission parameters and exploiting iterative methods; • A novel resource allocation strategy is derived, focused on the case of short packet
transmission;
• The performance of the resource allocation methods are shown when frequency misalignments occur;
• UFMC waveform transmitter and receiver scheme are proposed and integrated over the PUSCH channel of classical LTE, proving the coexistence between UFMC and OFDM;
• Computation complexity performance of the proposed scheme are highlighted and compared with classical UFMC and OFDM schemes;
• The performance of UFMC are illustrated in case of both time and frequency misalignments.
1.3
OutlineThe remainder of this thesis is structured as follows.
In the chapter 1, an overview on the main 5G requirements is given, taking into account the needs of the new technologies and applications. We then explore the 4G and OFDM drawbacks, focusing on the problems related to the physical layer and synchronization. In the chapter 2, OFDM and the main proposed waveforms as 5G candidates are char-acterized, discussing about strong and weak aspects of each one. Furthermore, an an-alytical description of UFMC followed by a brief comparison in terms of time and frequency efficiency is given.
In the chapter 3, we focus on the resource allocation methods applied to multicar-rier system, thanks by the application of the Bit Interleaved Coded (BIC) modulation. Here, an overview of the link estimation methods and the effective SNR mappings is discussed. We then study the optimization problem and its relative solution, giving an idea about the performance when the classical method is applied to OFDM. We then introduce several strategies of resource allocation exploiting the UFMC characteristics and and focusing on the typical application scenario. Finally, the performance of the various techniques in case of imperfect synchronization is shown.
In the chapter 4, we introduce a real implementation of UFMC over an open-source development tool called OpenAirInterface, proving the coexistence between OFDM and UFMC on PUSCH channel of LTE. We propose a novel transmitter scheme, which targets to reduce the computational complexity when few resource blocks are trans-mitted, while, at the receiver side, we adapt the classical PUSCH receiver to receive and demodulate the UFMC signal efficiently. We compare the performance in terms of computational complexity of our proposed scheme with classical scheme, a reduced-complexity scheme found in literature and OFDM. We then show the performance of the UFMC in case of both perfect and imperfect synchronization.
CHAPTER
2
The 5G physical layer
2.1
IntroductionRecently emerging trends are changing traffic characteristics of the actual mobile com-munication technology: Internet of Things (IoT) focuses on increasing spectral effi-ciency for sporadic short packet transmission, in the meanwhile Tactile Internet targets to low latency communications, both foreseeing coarse synchronization procedures in order to prevent wasting of energy and saving battery duration. On the other hand, giga-bit wireless connectivity requests quick downloads of great amount of data, needing of very high data rate [1]. These applications represent the main drivers of the new Fifth generation (5G) wireless mobile networks. In fact actual technology, such as Long-Term Evolution (LTE), provides wireless data connectivity accomplishing with impor-tant qualities in terms of high rate and reliability but it is not designed to manage large amount of data, typical of HD video, and it is not efficient for sporadic short packet communications [2]. Especially its architecture, based on Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM), provides an optimal robustness against fading combined with an easy FFT-based receiver but it doesn’t fulfil certain 5G re-quirements in terms of spectral efficiency, latency, synchronization and so on. For these reasons, new physical layer waveform have been proposed as candidate of new gener-ation, showing different features and accomplishing the new requirements in several ways.
2.2
5G requirementsFirst of all, it is better to clarify that 5G will be an heterogeneous system: it groups different applications with different requirements which will be difficult to satisfy si-multaneously. For example, very high-rate applications, such as streaming HD video
may have relaxed latency and reliability compared to driverless cars, where latency and reliability are paramount but lower data rates can be tolerated [3]. These specifications are focused on the increment of data rate, as already happened in each mobile net-work evolution, reliability, and density of devices, and the reduction of latency, energy consumption as well as costs. In the follows paragraphs, the 5G requirements will be discussed individually.
2.2.1 Data Rate
The main driver of each historical evolution in the wireless mobile networks has al-ways been the need of higher data rate and, also in 5G case, it represents one of the most important factors. As already happened in 4G, high bandwidth data connections have generated a great amount of traffic by sharing of internet contents, such as stream-ing videos, everywhere and at any time, and, from the business point of view, by usstream-ing cloud services. The advent of emerging data-intensive services, such as virtual and aug-mented reality, 3D and ultra-HD video, requires quick downloads of increasingly large amounts of data. The meaning of the data rate increment is conceivable in different ways:
Aggregate data rate : the total amount of data the network can serve. It will increase by a factor 1000 from 4G to 5G, up to 1 Tbit/s/km2.
Edge rate : it represent the worst rate that a user can be expected to receive when it is at the edge of the network. The goal for the 5G edge rate is 100 Mbit/s to as much as 1Gbit/s, which represents an increment of 100x with respect to the current 4G system.
Peak rate : it is defined as the maximum data rate achievable by user under any con-ceivable network configuration. Its goal is 10Gbit/s but its meaning is more mar-keting than engineering.
2.2.2 Latency
The reduction of the Round-Trip Time (RTT) is probably the main challenge for the 5G network. Future application scenarios, such as Tactile Internet or virtual and aug-mented reality, require an ultra-low latency between sensors and actuators, on the order of about 1ms, which matches with the human tactile sense. So, it will be possible to design new real-time interactive system. In order to have an idea of the latency, it can be useful to calculate a time budget: the information acquired from a sensor are pro-cessed and transmitted via communication infrastructure to a control server/network; the information is processed and eventually retransmitted via the communication in-frastructure back to the actuator. The processing of mobile transmitter and receiver can be considered at least of 300µs while from the physical layer point of view, this latency budget is traduced in 100 µs, as shown in Fig. 2.1 [4]. Current 4G RTT is on the order of about 15 ms and this latency is given by a 1ms subframe time, composed by OFDMA symbols with duration around of 70µs, with necessary overheads for resource allocation and access and it is sufficient for the current applications. In order to achieve this requirement, every element of the communication and control chain must be
opti-mized, redesigning the subframe structure, the synchronization, the resource allocation and the access procedures.
Figure 2.1: Latency goal on 5G system. Image from [4]
2.2.3 Ultra-High Reliability and Availability
For critical services, such as the control of critical infrastructure and traffic safety, where the system capacity may not be the crucial point, a connectivity with certain characteristics, such as specific maximum latency, should not merely be typically avail-able. Rather, loss of connectivity and deviation from quality of service requirements must be extremely rare. Reliability refers to the probability to guarantee a required functionality/performance under certain conditions for a given time interval. The spe-cific reliability requirements differ for several type of services and applications. For example, some industrial applications might need to guarantee successful packet deliv-ery within 1 ms with a probability higher than 99.9999 percent, or a failure rate around 10−6, which corresponds to merely 3.17 seconds of outage per year [4]. Current wire-less systems are designed around the perception that a link with 3 % outage is a good link, so far from the 5G requirement. Furthermore, an high reliability will also have a positive impact onto delay and channel occupation since less retransmissions will be needed.
2.2.4 Very Low Device Cost and Energy Consuption
Moving to 5G, costs and energy consumption will, ideally, decrease, but at least they should not increase on a per-link basis. Since the per-link data rates being offered will be increased by about 100x, this means that the joules per bit and cost per bit will need to fall by at least 100x. Many technological solutions may guarantee the money saving: mmWave spectrum could be cheaper than current one, small cells should be cheaper and more power efficient than macrocells. Furthermore, the new network would guarantee the connection with very low cost sensors with a battery life of several years without recharging.
2.2.5 Massive System Capacity
IoT applications have been defined as the killer applications of the 4G mobile net-works [2], due to the massive connectivity of machines with other machines, referred
to as Machine-Type-Communications (MTC). It has been supposed hundreds of enti-ties, such as sensors, actuator and similar items, will be placed on the same mobile de-vice [2], increasing traffic demands dramatically without compromising security. Each device will generate and consume very small amount of data sporadically, and the net-work will changes its way to manage and provide connection to everyone.
Overall requirements are resumed in the Tab. 2.1.
Parameters Value Latency in the air link < 0.1 ms Latency end-to-end (device to core) < 1 ms
Connection density 100x compared with LTE Aggregate data rate 1Tbit/s/km2
Edge rate 100 Mbit/s Peak throughput (downlink) per connection 10Gbit/s
System spectral efficiency 10bit/s/Hz/cell Energy efficiency > 90% improvement over LTE
Table 2.1: 5G requirements
2.3
4G drawbacksTwo separate waveforms are used on current 4G system: Orthogonal Frequency-Division Multiple Access (OFDMA) in the downlink and Single-Carrier Frequency-Division Multiple Access (SC-FDMA) in the uplink. Both waveforms exploit OFDM being a multicarrier transmission technique widely adopted because of many advantages that it offers:
1. Robustness against fading effects;
2. The usage of simple and efficient modulation and demodulation stages based on IFFT and FFT algorithms, respectively;
3. A fast equalization through a scalar gain applicable per subcarrier;
4. The possibility to exploit Multiple-Input Multiple-Output (MIMO) communica-tion technique and adaptive modulacommunica-tion scheme for maximizing bandwidth effi-ciency or transmission rate.
OFDM is quite suitable for delivering high-rate traffic to high-end devices, such as smartphone or tablet, but, as already mentioned in the previous section, it shows sev-eral drawbacks which don’t allow its utilization for jumping to the next generation of wireless mobile network. Here, only physical layer drawbacks will be discussed. Actually, LTE foresees a bulky synchronization procedure which is designed to meet orthogonal constraint of OFDM. Indeed, it is well known that OFDM systems are very sensitive when Carrier Frequency Offset (CFO) and errors in sample timing, called Timing Offset (TO), occur. In their presence, interference appears, by way of Intercar-rier Interference (ICI), Intersymbol Interference (ISI) and Inter band Interference (IBI), creating disastrous effects. Analytically , they add a new term to the transmitted signal, given by the queues of the remaining subcarriers symbols, leading to loose the benefits
of the modulation.
As already mentioned in the previous chapter, IoT devices generate small amount of traffic sporadically and therefore, after a data exchange, they need to go into the idle state as soon as possible, in order to save battery power. This behaviour is called fast dormancy [1] [2]. On the contrary, LTE synchronization procedure is not efficient for a short transmission because the overhead is greater than the traffic amount. For connecting a generic User Equipment (UE) to a proper E-UTRAN Node B (eNB), it is necessary UE will tune by turning to different frequency channels depending upon which bands it is supporting. In order to detect the synchronization signal, UE needs to get the data with a sequence of specific resource elements accurately. To accurately extract the data from a specific resource element, UE needs to know the exact symbol boundary (starting sample and ending sample of an OFDM symbol), which is obtained exploiting the property of the cyclic prefix (CP). Assuming that it is currently tuned to a specific band/channel, UE first finds the Primary Synchronization Signal (PSS), which is a Zadoff-Chu (ZC) sequence [5], located in the last OFDM symbol of first time slot of the first subframe (subframe 0) of radio frame, so that the UE can estimate the CFO and the OFDM symbol timing, enabling the synchronization on subframe level. The PSS is repeated in subframe 5 which means UE is synchronized on 5ms basis since each subframe is 1ms. Furthermore, the beginning of an LTE radio frame (BOF) must be found to allow any communication. The Secondary Synchronization Signal (SSS) can be used to identify the physical cell id (PCI), which is needed to register the UE with the base station. SSS symbols are also located in the same subframe of PSS but in the symbol before PSS. The very first step for UE to gain the initial access to the network after completing initial cell synchronization is to read the master information block (MIB) on broadcast control channel (BCCH), broadcast channel (BCH) and physical broadcast channel (PBCH). Resource elements used by MIB are the first 4 OFDM sym-bols of second slot of first subframe of a radio frame. On frequency domain it occupies 72 subcarriers. MIB carries very little but most important information for UE initial access, such as downlink channel bandwidth in terms of broadcast channels (BCHs), physical hybrid ARQ indicator channel (PHICH) configuration (duration and resource), system frame number (SFN) and so on. After initial cell synchronization and reading MIB, UE will proceed to read system information blocks (SIBs) to obtain important cell access related parameters. SIB1 broadcasts common information to all UEs in the cell related to cell access parameters and information related to scheduling of other SIBs. SIB1 is broadcasted in the fifth subframe in the SFN for which SFN8= 0, while
the repeated copies are sent in the fifth subframe for which SFN2 = 0. After initial
cell synchronization process is completed, UE will read MIB, which contains impor-tant information regarding downlink cell bandwidth, PHICH configuration and SFN. Then, UE can read SIB1 and SIB2 to obtain useful informations related to cell access, SIB scheduling and radio resource configuration SIB2 carries radio resource configu-ration information, which is common for all UEs. SIB2 information can be divided in following subcategories RACH related parameters, such as idle mode paging configu-rations,physical uplink control channel (PUCCH) and physical uplink shared channel (PUSCH) configurations, uplink power control and sounding reference signal (SRS) configurations, uplink carrier frequency / bandwidth, cell barring information. The physical random-access channel (PRACH) procedure starts when UE needs to send its
request over the shared medium. There are two possibilities at this stage: contention basedor non contention based random access procedure. The first one is similar to the ALOHA access protocol, where UEs in the same area (same cell) send the requests to the base station, including the possibility of collision among the requests coming from various other UEs. Such random access procedure is called contention based random access procedure. In the second one, network can inform UE to use a unique identity to prevent its request from colliding with requests coming from other UEs. Focusing on contention based procedure, depicted in Fig. 2.2, UE starts the communication sending a PRACH preamble, selecting one of the 64 available PRACH preambles and identi-fying itself by a random access radio network temporary identity (RA-RNTI), derived from the time slot number in which the preamble is sent. Here, if UE does not receive any response from the network, it increases its power in fixed step and sends PRACH preamble again. eNB reply to a PRACH preamble sends Random Access Response to UE on downlink shared channel (DL-SCH) addressed to RA-RNTI calculated from the timeslot in which preamble was sent, where temporary cell-RNTI (another iden-tity to UE), Timing Advance value (for compensating the delay) and Uplink Grant Resource (initial resource for uplink shared channel (UL-SCH)) are stored. Then UE sends a radio resource control (RRC) connection request using UL-SCH, transmitting its identity (temporary mobile subscriber identity (TMSI) or a random value if UE is connecting for the very first time to network) and the connection establishment cause. Finally, the connection is established from eNB sending a message of RRC connec-tion setup, containing the new C-RNTI which will be used for the further communica-tion. The full synchronization and cell identification procedure needs to be complete
Figure 2.2: RACH procedure
as fast as possible. However, the procedure just described can happen hundreds times a day, leading to significant increasing of overhead signalling and causing network con-gestion, especially for the IoT case, where a multitude of devices potentially have an access to the network. Consequently, relaxed synchronization schemes have been con-sidered to limit the amount of required signalling. A second aspect associated to the
synchronism is related to the usage of coordinated multi-point (CoMP) communication techniques, which is proposed in LTE in order to mitigate intercell interference and improve system performance especially for cell-edge users [6]. In multi-cell wireless networks, CoMP achieves these objectives through cooperation of multiple geographi-cally separated base stations, where the cooperation can be considered in transmission of data in downlink or reception of users signals in uplink. One of the critical issues in CoMP-OFDM systems is their sensitivity to multiple CFOs between terminals and base stations. The frequency offset can be caused by either Doppler shift resulting from terminals mobility or by oscillator frequency mismatch between a transmitter and a receiver. Multiple CFOs in CoMP-OFDM systems destroy the orthogonality between OFDM subcarriers and causes ICI at the receiver which leads to significant system per-formance degradation.
Another weak aspect of the OFDM waveform is represented by the high sidelobe levels of the spectrum due to the rectangular shaping of the temporal signal that extend over a wide frequency band. For this reason, in LTE, around 10% of the allocated bandwidth is reserved as guard interval, in order to prevent any kind of overlapping among adjacent spectra. Furthermore the Out-Of-Band (OOB) emission doesn’t allow the utilization of opportunistic adjacent blank space in the spectrum, exploiting the medium in inef-ficient way. So, cognitive radio could still be inapplicable because interference occurs inband due to the queues of adjacent users. Moreover, other typical OFDM drawbacks are still opened, such as peak-to-average power ratio (PAPR): it can have high values in the time domain since many subcarrier components are added via an IFFT operation. It decreases the signal-to-quantization noise ratio (SQNR) of the analog-digital convertor (ADC) and digital-analog convertor (DAC) while degrading the efficiency of the power amplifier in the transmitter. Lastly, spectral and time efficiency of OFDM could be further improve.
CHAPTER
3
Waveforms
3.1
An overview on the main candidates to 5G physical layerA large number of waveforms have been proposed as physical layer of new wireless mobile generation. These waveforms target to maintain the benefits of OFDM, improv-ing its weaknesses and achievimprov-ing 5G requirements. Furthermore, the interoperability with OFDM would be a prerequisite of the proposed waveform, in order to guarantee a continuity and a soft transfer from 4G to 5G. They can be classified in three main families, as shown in Fig. 3.1:
•Orthogonal Frequency Division Multiplexing (OFDM) •Generalized Frequency Division Multiplexing (GFDM) •Filter Bank Multicarrier (FBMC)
From these families is it possible to derive different waveforms, with different features and suitable for several applications. Before describing in deep UFMC and in order to have just a fair comparison, a short description of the main waveforms has been provided.
3.2
Orthogonal Frequency Division MultiplexingOFDM was introduced around the 1960 by R.Chang of Bell labs in order to get sev-eral independent channels over a large bandwidth. In order to get overlapping of these channels but avoiding the arising of the interference, orthogonality is necessary and it is achieved dividing the frequency selective channel into a certain number of parallel frequency flat subchannels. From 60s, a lot of improvements have been provided and nowadays OFDM represents one of most used waveform. In Fig. 3.2, the processing
Figure 3.1: Waveform classification
of a single multicarrier symbol, composed by N complex symbols, is depicted. The transmitted multicarrier symbol S is divided into subcarriers thanks by a serial to par-allel block, so each symbols occupies a subcarrier. A conversion in time domain is performed by a NFFT point IDFT, where virtual subcarriers are inserted if necessary,
obtaining x. After a further parallel to series block, a cyclic prefix is added at the be-ginning of the multicarrier symbol, just copying last NCPsymbols, obtaining y. At the
receiver side, after the removing of the cyclic prefix from the beginning of the vector r, a NFFT-FFT has been performed, followed by an equalization in frequency domain,
obtaining the estimated symbol bS.
Figure 3.2: OFDM waveform
3.2.1 Filtered-OFDM
Filtered Orthogonal Frequency Division Multiplexing(f-OFDM) is a version of classi-cal OFDM filtered by using a filter after the insertion of the cyclic prefix, as shown in
Fig. 3.3. In f-OFDM [7], the filter length is allowed to exceed the cyclic prefix length
Figure 3.3: f-OFDM waveform
in order to achieve a better balance between the frequency and time localization. In particular, the extended filter length bestows the flexibility to design a filter that of-fers a desirable frequency localization as well as an acceptable pass-band distortion for bandwidths as narrow as a few tens of OFDM subcarriers. On the other hand, using soft truncation, the filter can be designed such that the ISI incurred due to the extended filter length is very limited since the main energy of the filter is confined within its main lobe in time domain. Furthermore, the classical pulse-shaped OFDM performs pulse-shaping per subcarrier by applying time-windowing on each CP-OFDM symbol in order to improve spectrum localization. In contrast, f-OFDM achieves better spec-trum localization by suppressing the out-of-band emission. The two techniques can coexist, i.e. f-OFDM can be applied on a pulse-shaped CP-OFDM to further improve the performance. Filtering exploits soft truncation of a filter. Considering a filter with rectangular frequency response as the prototype filter, i.e. sinc impulse response pi(n),
with an appropriate bandwidth, where a Hanning time-windowing mask q(n) has been applied on the impulse response of pi(n), i.e. f i(n) = pi(n) · q(n) and then shift the
filter in frequency to be centered at the desired frequency. The windowing mask has smooth transitions to zero on its both ends so that it avoids abrupt jumps at the be-ginning and end of the truncated filter, and hence, avoids the frequency spillover in the truncated filter. The windowing provides a reasonable time-localization in the trun-cated filter’s impulse response, and thus, keeps the induced ISI in the resulting f-OFDM signal within an acceptable limit.
3.3
Filter Bank MulticarrierFilter Bank Multicarrier(FBMC) is based on the principle of filtering per subcarriers. As shown in Fig. 3.4, each multicarrier symbol has been split on subcarrier where a
Figure 3.4: original scheme of FBMC waveform
filtering operation is performed, moving the output to the proper frequency. The synthe-sis filter bank is composed of all the parallel transmit filters and the analysynthe-sis filter bank consists in all the matched receive filters. This scheme can further represent OFDM
modulation, except for the cyclic prefix insertion, as described in [8]. Transmitted sig-nal can be written as: The transmitted sigsig-nal can be written as:
y(t) =X
n
X
k∈κ
Sk[n]pT(t − nT )ej2π(t−nT )fk (3.1)
while the input of the multicarrier modulator is: Sk(t) =
X
k=0
Sk[n]δ(t − nT ) (3.2)
where Sk[n] are the subcarrier data symbols, k is a subcarrier index, and T is the
sym-bol time spacing. The difference between OFDM and FBMC lies in the choice of T and the transmitter and receiver prototype filters pT(t) and pR(t), respectively. In a
conventional OFDM, pT(t) is a rectangular pulse of amplitude one and width T. The
receiver prototype filter pR(t) is also a rectangular pulse of amplitude one, but its width
is reduced to TFFT < T , where TFFT = 1/B, and B is the frequency spacing between
subcarriers. In FBMC systems that are designed for maximum bandwidth efficiency, T = TFFT= 1/B, however, the durations of pT(t) and pR(t) are greater than T .
Typi-cally filter lengths of three or four times the symbol length are used. Hence, in FBMC, the successive data symbols overlap the considered one, from a factor simply called overlapping factor. The use of prototype filters with rectangular impulse responses leads to undesirable amplitude responses, affected by large side lobes in the frequency domain. Furthermore under real multipath channels, a data rate loss is induced by the mandatory use of a cyclic prefix, longer than the impulse response of the channel. With FBMC, the cyclic prefix can be removed and subcarriers can be better localized, thanks to more advanced prototype filter design. The FBMC prototype filter can be designed in many ways, trying to satisfy different constraints. In general, it is chosen to be:
• complex modulated for good spectral efficiency;
• uniform to equally divide the available channel bandwidth;
• with finite impulsive response for simplifying the design and implementation; • orthogonal, in order to have a single prototype filter;
• nearly perfect reconstruction (NPR) : certain amount of filter bank distortions can be tolerated as long as they are negligible compared to those caused by the trans-mission channel.
A digital version of the FBMC waveform, called PPN-FFT, is shown in Fig. 3.5, which FFT is exploited and the Polyphase Network(PPN) substitutes the filter bank [9]. In this way, it is possible to reduce the computational complexity. Moreover, in FBMC
systems, given the overlapping of the data symbols, any kind of modulation can be used whenever the sub-channels are separated. In order to keep adjacent carriers and sym-bols orthogonal, real and pure imaginary values alternate on carriers and on symsym-bols at the transmitter side. This so-called Offset QAM(OQAM) modulation implies a rate loss of a factor of 2. This is efficiency loss of OQAM modulation is compensated by halving the symbol period T .
3.4
Generalized Frequency Division MultiplexingGeneralized Frequency Division Multiplexing (GFDM) is a non-orthogonal, digital multicarrier transmission scheme proposed in [10], where a block of data symbols is transmitted per subcarrier and each subcarrier is circularly convoluted with an ad-justable pulse shaping filter, which limits the OOB radiations. Circular convolution is employed in the process to preserve the block oriented structure (preventing non negligible rate loss that would otherwise occur from filter tails in burst transmission scenarios) and allows introduction of cyclic prefix to provide a simple way of equal-ization when data is transmitted through a multipath channel. It’s possible to better understand the features of GFDM concentrating on its transmitter, shown in Fig. 3.6, assuming to transmit a generic vector S = (ST0, ..., STM−1), where the total number of the symbol is N = KM , it can be decomposed in K subcarriers with M subsymbols, so generic Sm = (ST0,m, ..., STK−1,m). The signal outcomes from GFDM transmitter can
be described as: x[n] = K−1 X k=0 M−1 X m=0 gk,m[n]Sk,m n = 0, ..., N − 1 (3.3) where gk,m[n] = g[(n − mK)modN]e−2π k Kn (3.4)
Focusing on the transmitter scheme, it’s possible to underline each branch of the
(a) (b) (c)
Figure 3.7: frequency-time diagram for OFDM (a), SC-FDMA (b) and GFDM (c)
ulator performs a circular convolution, where the resulting symbols is placed on a spe-cific subcarrier at a certain time. In Fig. 3.7, the time-frequency graph is shown, in which it is possible to appreciate the difference between a frequency allocation, as ex-ploited for OFDM in Fig. 3.7(a), a time allocation, as exex-ploited for SC-FDMA in Fig. 3.7(b), and the flexibility achieved by GFDM modulation scheme in Fig. 3.7(c), ca-pable to spread data across a two-dimensional (time and frequency) block structure, where multiple symbols per subcarrier can be transmitted. Additionally, time window-ing schemes can be applied over the extended GFDM block, allowwindow-ing further control of OOB radiation at a small expense of the CP length. By introducing variable pulse shap-ing filters, the orthogonality between the subcarriers is initially dismissed. As a result, self-induced inter-carrier and inter-symbol interferences need to be accounted for. Nev-ertheless, GFDM can also explore OQAM modulation, similar to the FBMC approach, with additional elimination of filter tails in the signal with its circular pulse shaping approach. So, in favorable applications, GFDM can be shaped to address orthogonal conditions as well. As a generalization of OFDM, GFDM is compliant with it when the number of symbols per subcarrier is chosen to be one. It can reach OFDM BER performance while facilitating pulse shaped subcarriers for suppression of out of band radiation and thus minimizing interference to the legacy system when opportunistically used in white spaces.
3.5
Universal Filtered MultiCarrierUniversal Filtered Multicarrier (UFMC) is born from the joining of f-OFDM and FBMC. While the former waveform filters the entire band and the latter performs a per-subcarrier filtering, UFMC applies a kind of tradeoff: subcarriers are split into several subbands, applying filtering on them separately. Subband-wise filtering is motivated by the ob-servation that time-frequency misalignments typically occur between entire blocks of subcarriers (e.g. due to block-wise resource allocation of different uplink users). Fur-thermore, as the filters are broader in frequency, they become shorter in time, providing a good support for communication in short bursts and limiting the OOB emissions as well. The classical architecture of UFMC waveform is depicted in Fig. 3.8. In order to simplify the description of the system, just the signal processing of a single multi-carrier symbol has been illustrated. The data coming from the upper level and stored in
Figure 3.8: UFMC waveform
the generic vector S = (S0, ..., SN −1)T composed by N = BD complex symbols, are
split into B branches, called subbands, forming the vectors Si = (Si,0, ...Si,D−1)T =
(SiD, ..., SiD+D−1)T, which represents a partition of S where i = 0, ..., B − 1.
Ze-ropadding is applied to each subband for performing an IDFT operation with size NFFT,
obtaining xi,u = NFFT−1 X k=0 Si,kzpe j2πku NFFT = = iD+D−1 X k=iD Ske j2πku NFFT (3.5)
The vector xiis then filtered by a Dolph-Chebyshev finite impulse response (FIR) filter
qi , (qi,0, · · · , qi,L−1)T, with 60 dB of side lobe attenuation and length L, tuned to the
i-th subband by a normalized frequency shift ∆i , D−12 + iD, so
qi,l = qle
−j2π∆il
NFFT ∀i ∈ B (3.6)
At the output of each filter, the generic u-th element zi,uis given by
zi,u=
NFFT−1
X
m=0
xi,mqi,u−m. (3.7)
The vectors zi = (zi,0, · · · , zi,NFFT+L−2)
T coming from all subbands are element-wise
summed and finally the resulting multicarrier symbol z , PB−1
frequency-selective fading channel. The channel impulse response (CIR) h is assumed stationary for the whole transmission of a packet. The received signal in the time-domain turns out to be
rl=
NFFT+L−2
X
m=0
zmhl−m+ wl, (3.8)
where l = 0, · · · , NFFT+L+LCH−3, LCHis the length of the channel impulse response
and wl ∈ CN (0, σ2) is a sample of complex Gaussian noise realization. The first
NFFT+ L − 1 samples of the received signal (Eq. 3.8) are zero-padded and processed
by an FFT of size 2NFFT. The resulting samples are down-sampled by factor 2, taking
only the even-indexed subcarriers, as suggested in [11], in order to avoid any kind of interference. A proof of this processing is given in appendix A. The frequency-domain sample corresponding to the subcarrier k is expressed as:
Yk , Hk B−1
X
i=0
Qi,kX˜i,k + Ik+ Wk, (3.9)
where k = 0, 2, · · · , 2NFFT− 2, Hk, Qi,k, ˜Xi,k and Wk represent the generic k-th
el-ement of the 2NFFT-FFT output related to the CIR h, FIR filter qi, symbols xi and
ambient noise respectively, while Ik is the interference contribution analyzed in [12].
It occurs when contiguous data packets have been transmitted. As shown in Fig. 3.9,
Figure 3.9: Elongation of multicarrier symbols before filtering, after filtering and after passing through a multipath channel
some elongations occur in the filtering operation and passing through a multipath chan-nel, due to the linear convolution. While the former is a well-known elongation and for this reason contiguous multicarrier symbol are placed to the right distance, the latter is unknown elongation that leads to the overlapping of the symbols. So, Ikterm includes
the ICI due to the FFT processing of only NFFT+ L − 1 samples from (Eq. 3.8), the
ISI as a result of the overlapping of consecutive multicarrier symbols and also an am-plitude reduction factor of the useful signal due to the loss of orthogonality. Given that
a 2NFFT-FFT is used, ˜Xi,k can be written as ˜ Xi,k = Xk 2 = S k 2, for k 2 = iD, · · · , iD + D − 1. (3.10) Therefore, Eq. 3.9 becomes
Yn , HnQi(n),nSn+ In+ Wn, (3.11)
with n ∈ N and i(n) , bn/Dc, i(n) ∈ B. After the UFMC receiver processing, the signal (Eq. 3.11) is equalized by a zero forcing (ZF) equalizer. For better understanding the system, a matrix representation is also given. Transmitted vector z ∈ CNFFT+L−1,
can be obtained as:
z = QVS (3.12)
where the filtering matrix Q ∈ CNFFT+L−1×(N·NFFT) can be see as a composition of
subband filtering matrices Qi ∈ CNFFT+L−1×NFFT as follows:
Q = [Q0, ..., QB−1] (3.13)
while the IDFT matrix V ∈ C(N·NFFT+L−1)×N can be expressed as:
V = diag(V0, ..., VB−1) (3.14)
where the i-th matrix Vi ∈ CNFFT×D performs the NFFT-IDFT on the i-th subband.
The channel matrix H ∈ CNFFT+LCH+L−2×NFFT+L−1 can be created from the matrix
HT ∈ CNFFT+LCH+L−2×NFFT+L−1, which is toeplitz matrix of h, adding a further matrix
HOL for taking in account the overlapping of contiguous symbols. Ambient noise
can be added by the matrix W. Here just a multiplication with 2NFFT-DFT matrix
is needed thanks by matrix U ∈ C2NFFT×NFFT+LCH+L−2, followed by a downsampling
and equalization matrices, D and EZP respectively. So the whole system, except to
equalizer, can be expressed with: b
S = EZPDUHTQVS + EZPDUHOLQVS0 + EZPDUW (3.15)
As already mentioned, interference occurs at UFMC receiver. This is given by the receiving window dimension and by the receiver has been used. In particular, under the hypothesis of perfect timing synchronization, the receiving windows size plays a fundamental role on the receiver performance. In case of sporadic short packet com-munication, such as the transmission of just one multicarrier symbol for carrying a few amount of data, it is possible to achieve the orthogonality at the receiver. The size of the receiving windows can be fixed at 2NFFTso any overlapping and any cutting originates
in interference. However, this case represents just a special case but it could be quite common for IoT communications. For the transmission of more than one multicarrier symbol, this receiving windows size is not much suitable. Changing this dimension from 2NFFTto NFFT+ L − 1, the previous symbol overlaps the considered one causing
ISI while the final part of the signal is cut causing ICI, as shown in Fig. 3.9. Normally, this kind of interference affects system performance but not in this case, thanks by the effect of the per-subband filtering, as proved by calculating the mean square error (MSE) of the received symbols in absence of ambient noise as:
M SE = 1 NT XNT−1 n=0 |ˆ(Sn) − Sn| 2 (3.16)
(a) (b)
Figure 3.10: Filter shape in time(a) and frequency(b) domain
By using a Monte Carlo simulation, MSE achieves very low values, around −59dB for 4-QAM, 16-QAM and 64-QAM mappings, where the QAM has been normalized in amplitude. After this test, the interference can be discarded. So, the equation Eq. 3.11 can be re-written as:
Yn∼= HnQi(n),nSn+ Wn (3.17)
This behaviour enables the usage of UFMC in every kind of communication scenario. The per-subband pulse shaping filtering leads to get very well-located spectrum in fre-quency. Chebyshev filters are adopted in UFMC for simplify the processing, because only two parameters are needed for the creation of the FIR function. These parameters are the attenuation of the side lobe level (SLL) and the filter length. In Fig. 3.10a, the normalized impulsive response is shown while in Fig. 3.10b, it is highlighted the SLL attenuation, set to −60dB. Under this threshold, the frequency response doesn’t continue to decay but it shows a floor to that constant level. Further considerations can be highlighted about the main lobe: the passband bandwidth cannot be narrower due to the filter length constraint and also the in-band filter response is not flat, but this considerations doesn’t degrade our system in any way. By the way, the filtering function can be further optimize based on maximization or minimization of some pa-rameters. In [11], the authors optimized filtering based on maximization of Signal to in-band Distortion plus out-of-band Leakage Ratio (SDLR) and Signal to out-of-band Leakage Ratio (SLR), in order to get improvements in case of not perfect synchro-nization but achieving more or less the same results of Chebyshev filter when strictly synchronization is achieved. Talking about SLL, in figure Fig. 3.11 the difference between classical OFDM spectrum and UFMC is shown. This achievement is very important in a typical multiuser scenario, where it will be possible to avoid the inser-tion of guard intervals, exploiting the spectrum resources in the best way and allowing the spectrum sharing as proposed in case of cognitive radio or in such scenario with primary and secondary users. SLL can be further improved simply using a correct re-source allocation on the subcarriers at the subband border, renamed active interference cancellation (AIC) in [13], or a small guard intervals, useful for contrast this effect but loosing spectral efficiency. Taking a look to the Fig. 3.12, the UFMC subband wave-form could be seen as multiservices per terminal/user equipment, taking into account
Figure 3.11: Comparison between OFDM and UFMC spectra
different requirements individually. So it could be possible to change the filter length or the number of subcarrier or the FFT size without loss of generality but simply using some precautions as smoothing windowing, such as a raised-cosine shaped edges, as shown in [14]. Another observation is related to the behaviour in case of frequency
Figure 3.12: Subband UFMC spectra
misalignments. At the receiver side, when a CFO occurs, subband filtering protects the subcarriers from the effect of the other subbands, improving the performance of UFMC in comparison with OFDM. However, ICI occurs and its effect cannot be contrasted in-side the subbands, where a sync shape models each symbol.
3.6
Time-Frequency efficiencyUFMC improves the waveform efficiency, as said in [15]. Let us define time-frequency efficiency rTF as: rTF , rT· rF = LD LD+ LT · NU N0 (3.18)
where rT is the efficiency in time direction relating the information carrying body (LD)
of the burst to its overall length including the tails (LT). Here, the use of a cyclic prefix
and the lengths of the filters are of relevance. rFis the efficiency in frequency direction
relating the number of usable subcarriers NU (i.e. excluding guards) to the overall
number of subcarriers N0 within the usable band. Just to have a fair comparison, it is possible to define a couple of parameters. Let’s assume:
L is the filter length K is the overlapping factor
LCP represents the length of the cyclic prefix
Ng is the number of users sharing the band
So, we start to calculate the time efficiency. The time efficiency is characterized by the ratio of the length of the information carrying body of the transmitted burst to its overall length. If we assume the burst to contain M multicarrier symbols (each comprising N samples), we get rT = M N . Focusing on the tails of the considered waveform, they
can be calculated as:
LT,OFDM , MLCP LT,UFMC , M (L − 1) LT,FBMC, N (K − 1 2) (3.19)
As described in the equation Eq. 3.19, LT depends on the number of the symbols the
burst is constitute of, and the number of symbols, except to FBMC, where the over-lapping factor K and total number of subcarriers N plays a crucial role. While being advantageous for long bursts, this feature is unfavorable for very short burst transmis-sions. About frequency efficiency, we can assume to use the parameters of 10 MHz channelization of LTE, with subcarrier spacing around 15 kHz, so we can evaluate:
N0 = f loor(10M Hz
15kHz ) = 666 (3.20)
According to the standard, the number of subcarriers actually carrying data for OFDM is NU,OFDM = 600. For FBMC, assuming just one subcarrier guard to each side of
the band, it can achieve NU,FBMC = 664 − (Ng − 1). Ng reflects the number of users
sharing the band: as outlined earlier, FBMC (SMT) is not orthogonal with respect to the complex plane. Therefore, we need an additional guard subcarrier to separate UL transmissions (if complex precoding is applied the same holds for DL transmissions) of users being allocated adjacent in frequency. This is necessary as the transmissions of different users are experiencing different channel gains introducing multi-user inter-ference at the allocation edges. So, Ng equals the number of users sharing the
trans-mission time interval (assuming continuous user allocations). In case of UFMC, the frequency efficiency depends on the choices of the filter length L and the attenuation of SLL. About filter length, we know it is possible to get better results just increasing filter length and decreasing the SLL attenuation, in a sort of trade-off between better spectral shape and better spectral efficiency, as proved in the Tab. 3.1 [15]. In order to achieve the coexistence with OFDM, typically L is set to LCP− 1. So, setting L = 73
(LCP = 72 in LTE with 10 Mhz channelization) and SLL=-60, NU,UFMC = 652.
Fix-ing the remainFix-ing parameters system parameters, such as N equals 1024, Ng = 15 and
K = 4, we can represents the time-frequency efficiency when M varies from 2 to 20, as depicted in the Fig. 3.13. The black horizontal curve corresponds to a system based on CP-OFDM applying LTE settings. The blue curves depict the performance of a sys-tem applying UFMC while red curve and green curves represent classical FBMC and improved version using burst truncation.
SLL=-30dB SLL=-40dB SLL=-60dB SLL=-90dB L=40 650 648 646 642 L=60 654 652 650 648 L=80 658 656 654 652 L=100 660 660 658 654
Table 3.1: Spectral efficiency of UFMC with different SLL attenuation and filter length
CHAPTER
4
Resource Allocation on UFMC
4.1
Introduction to Resource AllocationIn the chapter 3, we introduce UFMC and other new waveforms proposed as new can-didate for physical layer of the next wireless mobile network since they are able to satisfy several 5G requirements. Experiments in common and typical scenarios have been already performed for proving their characteristics but they need of further opti-mizations for boosting their performance, especially in case of application of adaptive modulation and coding(AMC) schemes. A lot of papers indeed show an improvement of the performance but frequently in uncoded case where the waveforms appear more flexible than coded one. Starting from a brief description of the classical approach of the resource allocation on a generic multicarrier system, such as OFDM, in section 4.2, an explanation of fundamentals principles exploited on the resource allocation are given, followed by the application on UFMC waveform in Sec. 4.3. Finally, in the last sections, we present different strategies of resource allocation, both using perfect and imperfect synchronization.
4.2
Resource Allocation on multicarrier systemThe resource allocation(RA) is a process which involves the selection of different pa-rameters for obtaining the maximum in terms of some metric from a complex system. Power, modulation order and code rate are currently the classical parameters which must be optimized. Obviously, the selection and the optimization of these parameters depends on the system features. For this reason, it is necessary to well define a sys-tem and its AMC scheme. In our case, we propose a Bit Interleaved Coded(BIC) [16] as AMC scheme. BIC modulation is a pragmatic approach combining the best out of signal design and detection of modulation and error correcting codes, which deal with
errors introduced at the demodulator of the underlying waveform channel. So, it takes advantage of the signal-space coding perspective, whilst allowing for the use of pow-erful families of binary codes with virtually any modulation format. Furthermore, BIC avoids the need for the complicated and somewhat less flexible design typical of coded modulation. For better understanding the property of the system, it will be analysed in the next section. In order to provide a general mathematical description, the BIC mod-ulation is applied on packet-oriented OFDM system based, as depicted in Fig. 4.1. The
Figure 4.1: BIC-OFDM scheme
input of the entire scheme coming from the upper layer, namely the MAC layer, by way of packets, each one identified by radio link control (RLC) protocol data units (PDU), and composed by Nu = Np+ NCRC bits, where Np bits of payload and NCRC bits of
parity, coming for the cyclic redundancy check (CRC)encoding. The first block of BIC scheme is a forward error correction (FEC) encoder with variable rate, achievable by using puncturing. So, the rate of the encoding blocks is r ∈ Dr, where Dr is the set of
possible code rate and the number of coded bits at the output is given by Nc = Nu/r.
In order to remove the correlation between contiguous coded bits introduced by the en-coding blocks, a bit-level interleaver is exploited. Here, an OFDM modulator based on N subcarriers of the overall band B is exploited, where bit allocation(BA) and power allocation(PA) are performed. Then, Nc coded bits are Gray-mapped into Ns complex
symbols, which are transmitted by using NOFDM = dNs/N e consecutive multicarrier
symbols. The complex symbols belong to the unit-energy constellation χ, 2mn-QAM,
being mn∈ Dm , 2, · · · , mmaxthe number of bits allocated on the n-th subcarrier and
