Cell discovery algorithms for mm-wave access of highly directional devices

(1)

POLITECNICO DI MILANO

SCHOOL OF INDUSTRIAL AND INFORMATION ENGINEERING Master of Science in Telecommunication Engineering

Cell Discovery Algorithms for mm-wave Access of

Highly Directional Devices

Master Thesis

Candidate:

Johanna Marcela Bolivar Gonzalez, ID 10479825

Supervisor: Ing. Ilario Filippini Assistant supervisor: Ing. Francesco Devoti

(2)

Acknowledgements

I would like to recognize all the effort that my parents, Victor and Hilma, have put in my education both personal and professional, all their moral support and the encourage they give me day by day, to them because I am far from home but never from their guidance and love.

I also want to say thanks to my boyfriend and friends that have become in my family here and have been always glad to give me a hand in the harder moments and feel my happiness, achievements and dreams like their own ones. To my old friends, family and the special people that always believe on me and show me there is always a reason to smile.

I want to express my gratitude with the Politecnico di Milano University, with their professors in the DEIB, their teaching and patience, specially to professor Ilario Filippini who gave me the opportunity to develop this work and to learn not just about thesis but about me during this process. To Francesco who was always available with the best disposition to help me with any doubt.

(3)

(4)

Abstract

Every time, everywhere, the users are continuously demanding for more data traffic, at higher rates and faster response; with the current techno-logy, the internet capacity will reach a saturation point leading to the need of the next generation communication, the 5G, able to support all of their expectations in the near future through the use of higher frequencies than in legacy network. Thus, allowing the use of legacy micrometric cells and micrometrical small cells in parallel, where the former one is used for the Control Plane and the latest one for the User Plane.

5G technology is based on the radio millimetric waves transmission, translated into higher data rate capacity but weaker propagation channel, thus making necessary the exploitation of antennas able to manage beam-forming which concentrates more power in a specific direction and with steerable capacity. As consequence, a procedure called cell discovery pro-cess should be completed on the directional transceivers to scan over all the antennas configuration until having a connection between them. The required time to complete this process contributes over the total latency that user experiments to get a service and consequently over the QoS. That is why it is important to manage it in an optimum way.

Meanwhile in previous works, the directionality and research on its in-telligence was focused on the mmWave base station side. In this work, we propose some algorithms for the user equipment to calculate the beam configuration sequence that has to be followed to get the first access, they are context aware with respect to position and orientation information. We study the case where the small cell base station is considered as omnidirec-tional by mobile station and we make analysis about the consequences of having directional capable and smart user against an omnidirectional and unintelligent one, on both, free and obstructed space. Continuing with the evaluation on free space of different and specific directionality capa-city, supposed by the mobile, on the base station side. Analyzing always in function of the delay due to first connection considering also intelligent search sequences on the small cell base station.

(5)

(6)

List of Figures

2.1 Expectation improvement from 4G to 5G . . . 10

2.2 Internet applications for 5G based on challenges for new technologies . . 11

2.3 Heterogeneus Networks of radio technologies . . . 12

2.4 Discovery process by both Base Station and Mobile Station . . . 14

3.1 Fusion sensing implemented by smartphones . . . 18

3.2 Simplification of antenna beam configuration. . . 21

3.3 Position information for scBS . . . 22

3.4 Context information for M S at nominal position . . . 23

3.5 MS’ appropriate set of beams pointing to scBS . . . 24

3.6 DGS scan process. . . 25

3.7 EDP discovery process . . . 26

3.8 DS-EDP discovery process . . . 26

3.9 Limited range DS-EDP (LZ-PSEDP) discovery process . . . 27

3.10 ZZ-SLS scan process. . . 28

3.11 MPAE-all scan process . . . 29

3.12 MPAE-simple scan process . . . 29

4.1 Cell Scenario with 10 obstacles . . . 32

4.2 UE’s antenna radiation pattern in 2D . . . 32

4.3 Average on number of switches on MS side Vs. Orientation error using conservative algorithms . . . 38

4.4 Average on number of switches on MS side Vs. Orientation error com-parison between conservative and non-conservative MPAE sequences . . 39

4.5 Average number of switches on BS side according to location error with zero orientation error . . . 39

4.6 Average number of switches on MS side according to orientation error for non/conservative sequences . . . 40

4.7 Average number of switches on BS side according to location error with zero orientation error. . . 41

4.8 Average of uncovered UEs using NC MPAE, according with the position and orientation error. . . 41

4.9 Uncovered UE quantity using conservative algorithms in different scen-arios. . . 41

4.10 First beam-width for MPAE-all at (a)360◦, (b)30◦, (c)15◦ considered directional capability on BS side. . . 42

(9)

4.11 Last (unique) beam width for MPAE-all (MPAE-single) for (a)10m, (b)100m, (c)250m location error. . . 43 4.12 Average number of switches on MS side according to orientation error

with 0m position error and MPAE sequences (including conservative and non-conservative) . . . 43 4.13 Average number of switches on MS side according to orientation error

with 10m position error for different sequenes om both BS and MS . . . 44 4.14 Average number of switches on MS side according to orientation error

with 100m position error and MPAE sequences . . . 45 4.15 Average number of switches on MS side according to orientation error

(10)

List of Tables

3.1 Common parameter for 3D channel model . . . 19

(11)

(12)

Chapter 1

Introduction

The operation of 5G will cope with the exponential growth of the internet usages expected in the following years which includes applications that demands stronger requirements from the network like latency and data rate by implementing an archi-tecture that makes use of the legacy one for signaling and management (C-plane), and adds small cells (U-plane) to increase the traffic density offered by user and by area. The former plane transmissions employ microwaves and the latest one will be supported on the millimeter waves able to provide higher data rates.

Systems based on mmWaves use frequencies above 10 GHz and communications at such high frequencies face higher challenges than microwaves, its higher propagation losses cause a great variability in the quality of the transmission channel. For this reason, it is necessary to take advantage of directional antenna design able to concen-trate the power in a desired direction. Hybrid and steerable beam-forming capacity, allowed by the implementation of MAA (Modular Antenna Arrays), gives to the device the capacity of receiving or transmitting power in different directions. Thus, the small cell base station (scBS) and mobile station (MS) need a discovery procedure where both will scan across the available antenna configurations until they find the suitable combination that select the appropriate beams to stablish the communication between them through the LOS path or if is not possible through a reflected one.

If the cell discovery process is not optimized it could increase the latency which causes degradation in the QoS or even produce an unwanted situation where the com-munication is supposed to act with strict latency values such as an autonomous car in a highway which will require fast response. It is important to highlight that the total latency was between 50 and 150 milliseconds on 4G while with 5G is expected to be reduced to a maximum of 10 ms.

Until this moment we can find related works where the delay due to the cell dis-covery process was studied on the BS, developing intelligent algorithms that generate scanning sequences based on context information and where the mobile station was assumed to be omnidirectional or directional with limited intelligence in its search as it is the Sector Level Sweep (SLS) characterized by a casual clockwise sweep. We pro-pose algorithms for the generation of the sequences on the MS side in a smarter way using the context information related to the position and orientation of the devices. As first approach, we will analyze the cell discovery time (equivalent to the number of switches that antennas should do in their sequences) and coverage performance over

(13)

free and non-free space scenarios when directional MS’s and scBS’s discovery processes are context information aware and each one assumes as omnidirectional the opposite part; the BS will use existent proposed sequences developed in previous works. Com-parisons between directional capable MS and omnidirectional MS, with and without smart algorithms will be done. Then, we will set on the MS side the consideration of further and specific directionality capability on BS and compare what is the influence of it in the resulting searching time.

This document us organized as follows: Chapter 2 includes a description of the motivations to develop this new generation, relevant features, and usages of this tech-nology. Also, we describe the Heterogeneous network highlighting its major specific-ations based on 5G context. As last point, we talk about the millimetric waves and a general idea about cell discovery approaches is given. Chapter 3 presents the smart cell discovery process proposals and assumptions for both BS and MS, but not before describing the technology behind the positions’ and orientations’ devices information gathering and the channel model for mmWaves on two sections giving its importance for the proper working of the on the searching process. Chapter 4 includes a descrip-tion of the simuladescrip-tion scenario and its settings, providing on the last part the results and analysis of the considered cell search procedures under the different scenarios and assumptions on the MS side. Finally, chapter 5 contains the conclusions of this work, and some potential future works.

(14)

Chapter 2

5G technology context

In this chapter we take a look on the 5G technology and the content is ordered like follows: 2.1. gives a look on its requirements and expectations, presenting next the technology behind, able to support this new generation, in 2.2. we can find the ar-chitecture over which 5G will work known as Heterogeneous Network and its needed division on control and data plane, detailing on why it should be used and what are the consequences, 2.3. contains the characteristics of millimeter waves required on the small cells, 2.4 explains why it is important to have an intelligent searching procedure for Cell discovery and in 2.5 we give the references and overview of related works.

2.1 5G - Next Generation Mobile Networks

Nowadays, the internet plays a significant role in the society and daily new applications have been released to satisfy different kind of needs, having a strong impact on the number of users, amount of connected devices, and internet services. As referenced, according to We Are Social 2017 [24], 50% of web traffic comes from mobile devices, with a growth of no less than 30% compared to the previous year, the number of social network users also has grown 20% in the last twelve months. Today there are more than 6000 million devices connected to the Internet. In addition, it is considered that Internet of Things (IoT) is expected to increase the number of devices connected to 100 billion by 2025 according to Huawei’s forecast [6]. However, the current architecture and capacity of mobile network is not able to handle this exponential growth and will not be enough in a near future. The fifth generation (IMT-2020) of mobile technology is expected as the solution to the limitations that 4G (IMT-Advanced) is facing.

There are still no official specifications for 5G but an ITU’s research group has been working further to develop the standard for 5G mobile systems. This group is called IMT-2020 and they have published the Draft New Report ITU-R M which includes the proposal for minimum requirements related to technical performance for 5G that represents a high improvement from 4G as shows Figure 2.1. i.e., One of the main items of the new technology is related to increase the data transmission speed, with Peak data rate, defined as the maximum achievable data rate under ideal conditions when all assignable radio resources for the corresponding link direction are utilized, of 20 Gbit/s for downlink and 10 Gbit/s for uplink and Peak spectral efficiency of 30

(15)

Figure 2.1: Expectation improvement from 4G to 5G

bit/s/Hz for downlink and 15 bit/s/Hz for uplink [23].

Another key point is the Latency. The impact of a high Latency level depends on the application, as is shown in Figure 2.2, for example if it is a medical application as a remote surgery, the latency is critical. Considering the IMT-2020 draft report, there are two requirements for this item: i) User plane latency, defined as the contribution of the radio network to the time from when the source sends a packet to when the destination receives it, has the minimum requirements for user plane latency set to 4 ms for Enhanced Mobile Broadband (eMBB)and 1 ms for Ultra-Reliable Low-Latency Communication (URLLC). The eMBB is supporting the evolution of today’s broad-band traffic with an increased spectral efficiency and the URLLC is applied where messages need to be transferred with high reliability and low latency, ii) Control plane latency, which refers to the transition time from a most “battery efficient” state to the starting of continuous data transfer, is required to be 20 ms at least [16].

Some of the significant requirements which also must be fulfilled in 5G are: • Energy efficiency, which is the capability to minimize the radio access network

energy consumption in relation to the traffic capacity provided, will be addressed: efficient data transmission in a loaded case and idle state when there is no data. • Connection density, which should be at least of 1 000 000 devices per km2

main-taining a good QoS.

• Mobility, which has been identified with the following scenarios in order to guar-antee QoS: 0 km/h for stationary, 0 km/h to 10 km/h for pedestrian, 10 km/h to 120 km/h for Vehicular, and High speed vehicular corresponding to 120 km/h to 500 km/h.

(16)

Figure 2.2: Internet applications for 5G based on challenges for new technologies

Among others [23].

2.2 Heterogeneous network

Considering the traffic growth, the spectral efficiency in cellular networks will be reach-ing a saturation point in their capability. It is needed to improve the capacity of the network but it is not enough adding macro cells. It is worst if the place already has a high density because this could be limited by a high interference between cells.

The concept of heterogeneous networks (HetNet) is based on mixing different types of radio technology and making use of nodes or small cells in conjunction with macro cells, as it is illustrated in Figure 2.3, with the aim of improving coverage and capacity of the network. One example of HetNet is present from 3G with the use of comple-mentary network technologies such as WiFi and femtocell where the data traffic from cellular networks is now delivered from those technologies, operating over the same spectral bands, to get better performance on data offloading [3],[13]. The HetNet spectral efficiency enhancement for 5G is defined by ITM-2020 requirements where two items are highlighted: The 5th percentile user spectral efficiency which refers to the 5% point of the Cumulative Distribution Function [23],[10] and the Average spec-tral efficiency (or spectrum efficiency) used for determining how well is employed the frequency band used to transmit data. Handling in an optimal way the spectrum effi-ciency will significantly raise the user rates in the proximity of small cell Base Stations (scBSs) as consequence of decreased distance between the UE and the access base station and distributing the traffic between small and macro cells [7].

The 5G systems and network should support the agile use of different spectrums, from both licensed and unlicensed bands. The 5G HetNet should integrate

(17)

intelli-Figure 2.3: Heterogeneus Networks composed by several kind of radio technologies

gently and seamlessly multiple component networks, including the cellular radio ac-cess network (RAN) and wireless fidelity (Wi-Fi) network using different radio acac-cess technologies (RATs) over different carrier frequencies, to achieve the QoS and quality of experience (QoE)-guaranteed, as well as spectrum-, energy-, and cost-efficient (SE, EE, and CE) Anywhere, Anytime, Any One, Any Device connectivity [22].

The micrometrical waves propagation allows the use of macro cells to guarantee coverage, because this kind of transmission is more resilient to the propagation pathloss meanwhile with the use of mmWaves on the small cells is not possible to guarantee the continue coverage but the higher data rates. Considering these two conditions, split between the control plane and the user plan enables user equipment to provide and receive signaling to/from the macro cell base and get radio resources from the small cells. Macro cells are standard cells as they support both C-plane and U-plane signaling [7],[25].

5G is able to offer a reliable support and management of the radio resources by con-necting both, legacy BS and scBSs to the central controller C-RAN, through which, is possible to get context information such as UE’s information (i.e., geographical position, orientation angle, uncertainty, etc.), network load, pathloss approximation, antenna configuration among others which are relevant data to coordinate the mobil-ity management, the network discovery, the small cell active state periods, the load balancing and inter-cell interference coordination [26].

2.3 Millimeter waves - Extremely high frequency

The millimeter waves have a wavelength in the radius of 1 to 10mm, which corres-ponds to the radiation within the electromagnetic spectrum between 30 and 300GHz. Comparing with the spectrum of the predecessors of the 5G technology, the range of

(18)

frequencies is very much above, since until now it was only used below 3GHz.

The bandwidth of the global spectrum for all cellular technologies does not exceed 780 MHz. It is important to mention that within this same spectrum it is necessary to support multiple technologies for both older and inefficient cell phones, as well as customers with smartphones and IoT devices.

In today’s architecture, the BS radiated a high amount of energy in order to cover the big regions or Macrocells. The goal with massive MIMO is manage the network resources to improve the radiated energy efficiency. If the signal is shaped in a very small region, massive MIMO can obtain a higher gain with a much lower emitting power per antenna. In consequence the traditional expensive ultra-linear amplifiers (tens of Watt power) could be replaced with the low-cost and low-power amplifiers with milli-Watt emitted power. In addition, the energy efficiency is also improved with the small cells when the average distance decreases between transmitters and users because there are lower propagation losses [15].

The millimeter waves and 5G technology must face with many technical challenges like the propagation and antenna characteristics. Traditional cellular wireless com-munication channel models cannot be applied directly to mmWave comcom-munications. Atmospheric attenuation is between 0.01-40 dB/km at the mmWave frequency, which is much higher than 0.001-40 dB/km range of frequency bands used by traditional cel-lular networks. The attenuation due to rain is between 0.001-40 dB/km and it varies with the frequency and the rainfall rate in mm, the reflection coefficients are up to 0.896 externally and 0.74 internally separated [15]. In addition, the MMWave commu-nications undergo severe interruptions when the distance exceeds 200 m and suffer from a severe path loss for NLOS, rapid channel fluctuations and intermittent connectivity, and is extremely sensitive to shadowing. Considering that the propagation conditions for mmWave has more limitation, it can be used for short-range communications using directional antennas, otherwise, this would lead to a big mismatch between the omni-directional discovery range, the range within a user can discover a mmwave BS, and the highly-directional communication range, the range within a user can be served by the same mm-wave BS.

As it has been described, the implementation of 5G requires exigent system re-quirements. The use of the conventional cellular frequencies will be highly limited on capacity; thus it is needed the use of multibeam antenna systems to work on the millimeter-wave frequency bands. This kind of antenna supports a high data trans-mission rate, increases the spectral and energy efficiency, and has adaptable beam forming. An alternative for handle this are the high gain antennas with a directional beam which greatly enhances the signal-to-interference-plus-noise ratio (SINR), mit-igates the Doppler effect, and improves the data security [11]. More specifically, the named modular antenna array (MAA) which is a set of several small phased antenna sub-array modules, with low cost, built in RF Intermediate Frequency, where each module is able to offer independent beam-steering with the help of phase shifters con-trollers who make a coarse beamforming in RF and is refined in baseband through a central beamforming unit [17], [21].

(19)

2.4 Millimeter-wave Cell Discovery Approaches

Differently from the previous mobile generations, where synchronization signals are typically broadcast into the cell, the directional nature of mm-wave communications forces the transmitter and the receiver to be spatially aligned in order to exchange synchronization signals, Figure 2.4. There must be a phase where the base station (BS) and the user must sweep through the available antenna configurations until they find the one that allows them to communicate. The very same discovery procedure is repeated at each handover, thus, reducing the discovery time is fundamental also to limit service interruptions. Smart directional cell discovery procedures are needed in order to maintain a reasonable discovery delay and for this reason, the knowledge about user context information (position, user mobility, applications’ needs, terminal capabilities, etc.) will play a relevant role to make a discovery process faster.

Figure 2.4: Discovery process by both Base Station and Mobile Station

If the signal is transmitted by broadcast, in case of mmWaves, it would extend only few meters from the BS causing a reduction of the service availability and wast-ing precious wireless resources, while takwast-ing advantage of directional beams, it would extend farther away. The cell discovery process in mm-wave small cells requires user equipment (UE) and scBS to scan over different beam directions until they point each other with the appropriate beamwidths with enough contribution on power to set a connection; as we can see in the Figure 2.4, setting a bigger beamwidth means lower coverable range on radial distance but a lesser number of beam configurations to cover a given angular area and, in the same order of ideas, a thinner beamwidth can cover further radial distance range but instead, will need a higher number of beams to cover the same angular area than before.

The mmWaves technology presents big challenges that must be addressed as Dis-covery Cell process where both the transmitter and the receiver must be in direct line of sight to exchange the synchronization signals. Therefore, different phases must be implemented for both the base station and the mobile terminal in which the differ-ent possible configurations in the antennas would be varied until they can be found between them, as it has been described in [7], [1], [25], and [8]. If this process is not carried out properly, it can increase or generate a significant latency in the initial ac-cess, which affects applications with low tolerance to it. In addition, if the mobility

(20)

of the user is considered, whenever it is necessary to exchange a cell, the latency in the discovery process would generate interruptions in the service causing a negative impact on the offered QoS [2].

Taking as starter point the work developed in [19], the beamforming training de-termines the suitable receiver and transmitter antenna sectors for a pair of stations trough two steps: Sector Level Sweep (SLS) and then an optional Bean Refinement Phase (BRP). During SLS, the antenna sectors for a pair of stations are determined; each of the two stations either trains its transmitter antenna sector or the receiver antenna sector and if it is desirable, BRP makes a fine tune on that sector selec-tion. In [7], new improved cell discovery algorithms (extension of Sector Level Sweep called gSLS, Dynamic Sector-Level Sweep algorithms (D-SLS), Enhanced Discovery Procedure (EDP), and Dynamic-Sector EDP) are proposed for the BS based on the available context information through the C/U plane heterogeneous network architec-ture [9]. Implements the same previous algorithms for BS proposing the iBWS (initial Beam Width Selection) where the best combination of beam widths at both BS and MT side is selected based on the MT’s location, and then informed to both devices through the separated C-plane connection, with this approach user mobile overcomes the fixed-beamwidth constraint of SLS.

Other methods get advantage of the previous successful connections to mitigate the effect of an obstacle obstruction along the exploration path like in [5], where the EDP algorithm is modified in order to include a memory-store feature, here, every time that a user is discovered, the modified algorithm will store the beamforming configuration in a fingerprint map.

(21)

(22)

Chapter 3

Cell discovery process

In this chapter we will expose some cell discovery processes which include algorithms that have been already studied and some novel algorithms which is the center of this work. For those processes could be necessary some information about the scenario where the connection will be placed. Section 3.1 describes the elements involved in the procurement of some context information like the position and orientation of the scDevices (Sub-section 3.1.1) and the channel model definition (Sub-section 3.1.2). Section 3.2 shows how scBS and scMS see each other based on the context information. Section 3.3 references some algorithms used for the first connection between BS and MS from scBS point of view. Section 3.4 explains new discovery cell algorithms for scMS which are context aware.

3.1 Getting the context information

As explained before, the C-plane can collect the information from the mobile phone or from the small cell base station (scBS) and exchange the suitable data between them, like their location and orientation with their respective accuracy, which are very important for the development of an intelligent discovery process to minimize the latency in the success of the first connection establishment, known as Primary Signal Synchronization (PSS), having also into account the path-loss model and the antenna radiation pattern on both sides.

3.1.1 Positioning and orientation technology

For getting the location information, the network devices can use the Global Position-ing System (GPS), and for the orientation specification they can rely on the sensPosition-ing fusion of accelerometer, gyroscope, and compass. The GPS determines the position of a specific object around the world using at least three satellites (out of 24 avail-able), each one sends a tag to identify itself, its position and a timestamp based on stable atomic clocks; when these transmissions are received by the device, the timing of the different signals is useful to calculate the distance to each satellite and finally, its position. Thus, the GPS’ accuracy depends on several factors as atmospheric con-ditions, signal blockage, signal multipath, number of satellites available, and receiver features, however it can be improved using additional technologies and procedures i.e.,

(23)

Differential GPS, Assisted GPS [18], [27].

Figure 3.1: Fusion sensing implemented by smartphones

As it is shown in Figure 2.1, finding out the equipment orientation, and sub-sequently rotation and linear movement, is mainly based on the following three com-ponents: Accelerometer, Gyroscope, and Magnetic field (also called Compass) sensors. The accelerometer processes gravitational forces information, linear motion changes and rotational forces as centripetal acceleration. The 3-axis accelerometer embedded into smart devices measures the gravity acceleration vector and it is used to determine which angle the device is held in [14]. Gyroscopes do not sense on external reference, it measures their own rotation, calculating the angular velocities amongst X, Y, and Z axis. The device takes the information from the compass and calculates its orientation with respect to magnetic North. The magnetic sensor identifies the three field strength components forming a vector which is tangential to the magnetic field at equipment location and his length represents the scalar field strength., i.e. if it is located in a way that one sensor axis points in the direction of the magnetic field, the sensor read two components in zero and the other one with the magnetic field strength at that point. The accuracy of these sensing is highly dependent of the quality components as the magnetic disturbances present in the environment caused by electrical appliances, metallic objects, buildings, etc [12].

3.1.2 3D Channel Model

The carrier frequency of mmWave faces several challenges in the signal propagation properties as the propagation loss (PL), according to the Friis transmission equation at small wavelengths, and the quasi-optical nature of the beam. To compensate this, both transmit and receive antennas must be highly directional. In addition, the quasi-optical nature makes that majority of the transmissions be propagated through the Line-of-sight (LOS) and low-order reflected paths.

The 3D Channel model, described below, is taken from the Milimetre-Wave Evol-ution project for Backhaul and Access, kwon as MiWEBA [20]. Quasi-Deterministic methodology was used for modeling the channel propagation and power losses affected by the travelled path, considering the influence of obstacles and the radiation

(24)

pat-Parameter Description

Fc Carrier frequency, [Hz]

C Light speed, c = 299792458, [m_s]

AO Oxygen absorption, [_kmdB], 0.015 [dB_m] for 60 [GHz]

Htx Transmitter height above ground, [m]

Hrx Receiver height above ground, [m]

L Horizontal distance between TX and RX,[m]

Atx TX antenna pattern

Arx RX antenna pattern

εr Relative permittivity of the ground surface at Fc

σ Surface roughness

Table 3.1: Common parameter for 3D channel model

terns of the antennas. The received power PR will be determined using the following equation.

PR= PT + GT + GR− P L (3.1)

Where PT is the transmitter power, GT is the transmitter gain, GR is the receiver

gain, and PL is the reduction in power density of an electromagnetic wave or path loss. According to the methodology developed by MiWEBA, the generation of the 3D channel model consists of the following steps:

• Define the scenario and model parameters. In Table 3.1, some common para-meters used for 3d Channel Model are listed.

• Calculate the deterministic components data and random components data in accordance with selected scenario recommendations

• Apply path blockage in accordance with scenario requirements to the selected clusters

• Execute antenna TX and RX antenna patters and beamforming algorithms Experimental measurements in open area environments shows that it is possible represent the channel only with two rays: direct ray which corresponds to the LOS component and first reflected ray which corresponds to the reflection from the ground surface. The other propagation paths were 15-20 dB lower than both direct and re-flected rays.

An additional model, called the street canyon channel model, represents typical urban scenario: city street with pedestrians’ sidewalks along the tall long buildings. The access link between the Access point located on lampposts and the UEs at human hands is modeled in this scenario. It is relevant to define the following power para-meters for some specific Channel model considering that λ is wavelength, d is distance from transmitter to receiver (d0, dg - transmitter-ground-receiver, dw -

(25)

• Direct Ray describes random obstacles of the direct ray with blockage several Fresnel zones. PD0= 20log10( λ 4pid0 ) − A0d0 , in dB (3.2)

• Ground-reflected ray describes signal scattering from the rough surface. PG0 = 20log10( λ 4pid0 ) − A0dG+ R + F , in dB (3.3) R = 20log10 sinφ − √ B sinφ +√B ! (3.4)

B = εr− cos2φ , f or horizontal polarization (3.5)

B =εr− cos 2_φ 2 r , f or vertical polarization (3.6) tan(φ) = (Htx+ Hrx) L (3.7) F = 80 log10 πsinφσG λ 2 , in dB (3.8)

• Wall-reflected ray describes signal scattering from the rough surface. PW 0 = 20log10( λ 4pidW ) − A0dW + R + F , in dB (3.9) R = 20log10 sinφ − √ B sinφ +√B ! (3.10)

B = εr− cos2φ , f or horizontal polarization (3.11)

B =εr− cos 2_φ 2 r , f or vertical polarization (3.12) tan(φ) = (Dtx+ Drx) dD (3.13) F = 80 log10 πsinφσW λ 2 , in dB (3.14)

Finally, the 3D Channel Model includes antenna models because the real world antenna design may have sophisticated radiation pattern which makes harder the sim-ulations of the channel and this is particularly true for the phased antenna arrays with very high number of radiation elements such as Modular Antenna Arrays (MAA).

(26)

For the present work, all the efforts will be focus on Steerable Antenna Model with Gaussian Main Lobe Profile.

The real antenna is simplified by modelling one with a main lobe with Gaussian profile in linear scale and constant level of side lobes. The gain function for the main lobe is defined by the formula 3.15 where ϕ is the azimuth angle, θ is the elevation angle and G0 is the maximum gain corresponding to direction ϕ=0 and θ=0 where

ϕ−3dB and θ−3dB are the half power beam widths.

GdB(ϕ, θ) = G0,dB− 12 ϕ ϕ−3dB 2 − 12 θ θ−3dB 2 (3.15) Considering that the maximum gain may be represented as a function of ϕ−3dB

and θ−3dB, the final formula for the main lobe gain is written as follows:

GdB(ϕ, θ) = 10log 16π 6.76ϕ−3dBθ−3dB − 12 ϕ ϕ−3dB 2 − 12 θ θ−3dB 2 , in dB (3.16) The gain for the side lobes is defined in the way that integration over total solid angle 4π is equal to the unity. For this scenario, the total radiated power of directive antenna pattern is equal to the total radiated power in the isotropic case.

3.2 Assumptions

The way to handle the searching process varies from the scBS’s and UE’s perspectives but before entering in details, lets state that the former one waits on each of its configurations while the latter one completes its discovery sequence, and then the scBS’s continues with its next configuration, iterating on both sides until a connection can be stablished between them. Also, is worth to mention that BS has directionality capacity and considers MS as omnidirectional and vice versa, but additionally MS can consider the former one as directional in some cases. Also, the beamforming and processing capability on scBS is greater than in the UE’s case.

(27)

In the next part of the text, we assume the beam antenna configurations represented in a simplified way with non-overlapping sectors on the same Beam-width ring, which refers to all the beams with the same width, as it is exemplified in the Figure 3.2. Let’s call to the combination of beam width and direction the “beam configuration”.

With that in mind let’s see how the small cell equipments evaluate the context information, which includes their positions obtained through GPS (nominal positions), their orientation acquired from the proper sensors, the pathloss model and the radiation pattern of the antennas, and how it is used for the development of the discovery process. The millimeter-wave BS location and orientation uncertainty together with the possible mismatch between the propagation model and the real propagation are merged on the UE’s errors thus scBS is supposed to have perfect position information and alignment with the north pole orientation which is the reference.

3.2.0.1 Small Cell Base Station

Figure 3.3: Position information for scBS. M SN P and M SRP are the nominal and real position

information of the Mobile Station, N Pais the location accuracy, d is the Euclidean distance between

scBS and MS. Orientation assumed to be pointing at north pole.

The scBS calculates the beamwidth that should be used and the direction in which the beam must point to reach the user at nominal position M SN P, configuration

(iBWN P, beam with the closest pointing direction to ϕ) assuming perfect location

information to itself. When the accuracy information N Pa is provided, there is a

defined area around the nominal position where the user can be really located M SRP,

Figure 3.3. Then scBS interpretes this situation as a successful connection with the power enough to surpass the stablished, threshold if selected beam belongs to the group of beamwidths able to cover from dmin= d − N Pa to dmax= d + N Paand with

pointing directions within Ψ angular range, where d is the Euclidean distance between the scBS and the M SN P.

(28)

Figure 3.4: Context information for M S at nominal position with Refdir as the reference direction

on the system (pointing to magnetic north) (a) for position: scBSN P and scBSRP are the nominal

and real position information seen by the Mobile Station, N Pa is the location accuracy, d is the

Euclidian distance between scBSN P and M S. (b) For orientation: P ntN dir, δRpntare the pointing

direction, angle of pointing direction towards nominal scBSN P and real scBSRppersepective from

M S; M SN O and M SRO is the nominal and real orientation of the mobile, ψOa

3.2.0.2 Small Cell User

Even though it is assumed that BS has perfect position information, the mobile takes its nominal position as the real one and reflects its own location error on the base station side, therefore, from the Figure 3.4a; scBSN P is the perceived position by the

user at M SN P, at which the base station can be found, forming the circular area with

radius equal to the accuracy N Pa and centered on it. In this area, the scBSRP which

refers to the perceived position of BS by M SRP, can be found. The UE translates this

situation as the selection of a group of beamwidths able to cover from dmin= d − N Pa

to dmax= d + N Pa with pointing directions within ψP aangular range.

(29)

that this is not fixed as in the BS case). When there is an error on this information, the user can be pointing at the wrong direction P ntN dir (pointing nominal direction)

with angle δN pntto the Base Station. The correct pointing direction P ntRdor (pointing

real direction) is between ψOamin = δN pnt− ψOa and ψOamax = δN pnt− ψOa, Figure

3.4b.

Figure 3.5: MS’ appropriate set of beams pointing to scBS summing the accuracies on orientation and position.

From these two kinds of errors the mobile will find the appropriate set of beams to point to the base station and get connected with the minimum power requirement. Figure 3.5 shows this set of beams, which are shadowed by the blue area.

3.3 Former Discovery Proposes for scBS

The searching sequences on this side of the small cell network can be generated tak-ing the desirable data from the accessible context information, i.e. in some cases it takes the position information (DGS) or in others (EDP, LZ-PSEDP) a more detailed information of position which includes its accuracy.

3.3.1 Discovery Greedy Search (DGS)

The base station targets with the configuration of the beam needed to achieve the distance d (iBWN P) on the nearest direction to that one pointing where MS is supposed

to be found (ϕ). If the user is not reached, it continues with a clock-wise sweep across all the beams of the current beamwidth ring, as it is shown in Figure 3.6. Then, if the connection has not been still stablished, it repeats the same procedure but in the next beamwidth ring (with thinner beams’ aperture) thus warranting a higher coverage (by larger distances). The process is iterated as many times is needed until BS finds the

(30)

user.

Figure 3.6: DGS scan process.

3.3.2 EDP, DS-EDP & LZ-PSEDP

The search process is divided in N number of circular sectors (the minimum N is 1 and the maximum depends on the beamforming capacity of the antenna that is assumed to be 360), each one follows a sequence over an angle of 2π_N and once the exploration inside the sectors it finished, it continues with another one swinging between them on a clockwise and counter-clockwise alternated way, every sector n is centered on βn= ϕn+2π_N(n − 1), where ϕ is the pointing direction towards M SN P. In this work,

we managed 4 sectors, as it is observable in the Figure 3.7, every sector S1,S2,S3 and

S4 has an angular wide of π₂ and is centered on ϕ, ϕ +π₂, ϕ + π, ϕ +3π₂ and are visited

sequentially in S1,S4,S2 and S3 order.

In each sector, the sequence starts from the center of it and from the suitable iBWN P, visiting afterguard the neighbor beams with the same width, switching toward

right-handed and left-handed direction until visiting all the beams of the sector in the actual beamwidth ring and letting for latter the same procedure for the thinner beamwidth rings. Again, it will be stopped when the PSS is successfully stablished.

Dynamic sector EDP or DS-EDP is a modified version of EDP where the angular width of the sectors is proportional to the accuracy N Pa, which delimits the angle ψ,

and to the distance d as it is pointed out in the Figure 3.8. The number of sector is given by N = 2π_ψ , so that the nearer is the M SN P or the bigger is the uncertainty on

its position, the smaller is the number of sectors.

Limited range EDP is an extension method which can be applied to EDP, DS-EDP (called LZ-PSDS-EDP in the Chapter 4) or any further proposal that gives priority to the range of beams where it is more probable to find the M SN P postponing the use

of thinner beams that go beyond that area. It divides the sectors on two groups of beamwidth: i) those ones able to cover from the nominal position to the dmax and ii)

(31)

Figure 3.7: EDP discovery process

Figure 3.8: DS-EDP discovery process

those able to cover distances longer than dmax. In the Figure 3.9 this division can be

seen. Sweeping always with the same pattern of EDP in Zone 1 and then in Zone 2.

3.4 Novel Discovery Proposes for scMS

Assuming the beamforming capacity on BS and MS, we propose discovery algorithms for both sides having in mind the previous considerations. Let’s start with some of the proposals for the former, which were developed by the predecessor of this work and, then, we will describe the original contributions for the latter one.

3.4.1 Mobile station discovery process

The MS can also make use of the available data from the context information like the position of the network devices (ZZ-SLS) and additional detailed information which

(32)

Figure 3.9: Limited range DS-EDP (LZ-PSEDP) discovery process

includes the orientation information and the accuracy data, for both orientation and location (MPAE).

3.4.1.1 Zig-Zag ZZ-SLS

This algorithm, will take as the starting point the beam with the thinnest width and closest direction to the P ntN dir (with angle δN pnt) based on the location information

of scBS and M SN P obtained through the GPS. Then, it will do a sweep, like in the

Figure 3.10, alternating clockwise and counter-clockwise directions named as Zig-Zag sequence until finding the proper one to complete the first connection process.

The sequentially ordered use of the last beam width ring starting from a fixed direction is known as SLS, in this case we are using the SLS idea but in a more sophisticated way because it is context information aware.

3.4.1.2 Most Probable Area Effort (MPAE)

The error on the location and orientation information can highly affect the selection of the bean configuration assuming one that can be very far from the needed require-ments to set a connection, nevertheless, the knowledge of its accuracy can lead to a more suitable selection of the beams where it is more probable to succeed. As we men-tioned before, based on LOS, the mobile can get connected with the minimum power requirement if the beam configuration falls inside the area proportional to the error, the blue zone in Figure 3.5, defined by the total angle ψT OT a that is the summation of

location and orientation uncertainties (ψP a+ 2ψOa) and radial width of 2N Pa (from

dmin to dmax).

MPAE is based in this area, depending on the case, it will do a longer or shorter exploration over it. This sequence can be complemented by a conservative sequence making it more resilient to uncovering situation. The next figures shows MPAE ver-sions (of MPAE-simple and MPAE-all) which will be explained bellow. The considered area goes from the beam width able to reach d to dmax within the direction range

(33)

Figure 3.10: ZZ-SLS scan process.

ψT OT a, as it is delimited by the green area; every beam who is touched by this area

will be included into the sequence. 3.4.1.3 MPAE-all

The idea behind this sequence is to do the sweep with a zigzagged pattern starting from the beam configuration which is supposed to be able to cover the threshold for the success of connection with scBS. First in the smallest beamwidth of the area and then repeating the process in the larger ones, going from thicker to thinner beams, until the last width belonging to the MPAE zone.

We will call to the previous sequence, the non-conservative MPAE-all or NC-MPAE-all, whose name emphasizes that we are passing through all the beamwidth rings of the MPAE zone, and MS is not searching in all the directions. The conser-vative extension, where MS explores in all of its directions, can be done by adding the ZZ-SLS sequence at the end of the NC-MPAE, avoiding the duplication of beams (in the case the both sequences overlap). This sequence is illustrated on the Figure 3.11. 3.4.1.4 MPAE-simple

It will be defined based on the MPAE zone, but this time, we will just consider its last beamwidth ring, which covers dmax, to do the sweep with a clockwise and

coun-terclockwise alternated pattern and starting from direction which is supposed to point to scBS. This is the non-conservative MPAE-simple.

To consider the rest of the directions that MS could explore, we propose to add, after the MPAE-simple sequence the ZZ-SLS eliminating the beam repetition (if there is some), forming together the NC-MPAE-simple shown on the Figure 3.12.

Just to clarify, we will manage the notation for the conservative cases as MPAE-all or MPAE-simple and for the non-conservative sequences as (NC)MPAE-MPAE-all or

(34)

Figure 3.11: MPAE-all scan process

(NC)MPAE-simple.

(35)

(36)

Chapter 4

Numerical results and analysis

After presenting the search algorithms already existent for BS and the proposed ones for MS we will explain the results obtained from an ad-hoc simulation on MATLAB for a 2D squared space and taking the propagation model and antenna pattern from MiWeba. The simulator was run 100 times and the results to be analyzed are taking from the average computation.

The structure of this chapter goes like described next: In 4.1 we describe the scenario characterization specifying the error distribution on the UE’s location and orientation, the beamforming capacity, the assumed values for the pathloss model on free space and under reflections besides the antenna gain pattern parameters. In 4.2 the performance of the algorithms is evaluated, as first step in 4.2.1.1. the MS assumes the BS as omnidirectional, from this point we will understand the importance of applying directionality and intelligence at the user side studying also the case where obstacles are present in 4.2.1.2, letting for the last part in 4.2.2. the examination on the significances when MS considers higher directionality on BS side.

4.1 Simulation scenario and settings

The proposed scenario is a squared space with dimensions of 400m by each edge and surrounded by reflective walls. Within this area one BS, whose orientation is aligned with the reference system, is allocated in the central point and 1000 users are randomly dropped with uniform distribution on position and orientation in the obstacle-free space; the study is mainly focused on free space but we also consider a case where 10 obstacles of 20m x 20m are also randomly set to make a general analysis on non-free space, making that obstacles do not overlaid over each other nor over the sideward walls, Figure 4.1 shows an example of a single cell scenario with 10 obstacles.

The uncertainty on the geographical position of each user is modelled as a sym-metric and independent bivariate normal distribution centered in the nominal position U EN P with identical standard deviation in both x and y axes such as σx = σy = σ,

where the relation with the location error is given by N Pa = 3σ. Regarding to the

orientation information, the uncertainty is modeled as symmetric uniform distribution around the nominal orientation angle δN O. In the other hand, the BS is supposed to

(37)

Figure 4.1: Cell Scenario with 10 obstacles where the users are represented by a line ended by a dot, the latest representing the direction of the user meanwhile the scBS is represented by a green triangle located in (200,200) and with 0◦ _{orientation angle w.r.t. y axis.}

The beamforming capacity of the BS antenna has a total 240 configurations start-ing with the minimum directional capability which is omnidirectional and considerstart-ing consecutively 3, 4, 8, 12, 24, 36, 72 and 120 directions corresponding to an elevation aperture θ−3dB of 360◦, 120◦, 90◦, 45◦, 30◦, 15◦, 10◦, 5◦ and 3◦ respectively, meanwhile

MS, illustrated in the Figure 4.2, just manage 1, 3, 4, 8, 12 directions for a total of 24 configurations.

Figure 4.2: UE’s antenna radiation pattern in 2D

Since we are on 2D, the antenna model is simplified with the azimuth aperture angle fixed to π₃ getting:

GdB(θ, ϕ) = 10log10(G0) − 12 θ θ−3dB 2 (4.1) where

(38)

G0 =

7.1006

θ −3dB (4.2)

According to [20], the α, k and d0 parameters are taken from the street canyon

access path loss propagation model and have values of 82.02dB, 2.36 in case the path distance surpasses the reference distance or 2 otherwise and 5m respectively. Obtain-ing: P L = 82.02 + 20log10 d 5 , dB; d < d0 (4.3) P L = 82.02 + 23.6log10 d 5 , dB; d > d0 (4.4)

The reflections constants belong to the concrete material characterization being the standard deviation σw= 0.2mm and dielectric constant εr = 4 + 0.2i.

According to [4] the minimum SNR for getting success on the PSS is 10dB which is equivalent to a sensitivity of 73dBm, such as the user can get connected to the BS if the received power is greater than the mentioned threshold given the gains of the antennas (both transmitter and receiver), the pathloss model with the influence of obstacles in it (if there are present), and the transmitted power which is set to 30dBm.

4.2 Numerical analysis

4.2.1 Omnidirectional BS

Looking at the beam that users at U EN P with δN O are supposed to point with, most

of them (more than 80%) belong to the thinnest beam width for all the considered location error values causing MPAE-simple and ZZ-SLS sequences to become identical most of the times. MPAE-all will experiment the same situation but for the percentage of the users that do not start the sequence in the thinnest beam width ring can really enlarge more and more the sequence each time the error gets higher.

4.2.1.1 Free space

The previous statement let us to realize that the differences in the resulting average number of switches between ZZ-SLS and MPAE-simple will be almost negligible and, as the location error increases, it will be smaller; MPAE-all presents a slight better performance than MPAE-simple at low location error values but it becomes worst as the location error rises, this gap is more pronounced as the orientation and location error increases. As seen in the Figure 4.3, the MPAE algorithms do not contribute positively in a remarkable way in comparison with the ZZ-SLS one.

Anyhow it is possible to observe that having intelligence at the MS side is better than using a non-smart algorithm like SLS, of course its benefit is greater at lower location error and lower orientation error, otherwise it starts to be like a search by chance just like SLS.

Since applying the non-conservative algorithms means a shorter sequence length, the behavior is different than before. NC versions have a much better performance

(39)

when the orientation error is smaller and from some value starts to be almost like the conservative outcome, this value is lower as long as the location error increases as shown in Figure 4.4. As mentioned in the Chapter 2 one possible drawback of NC is that not all the mobile stations can be covered but in the case of free space besides the considered scenario conditions this situation is not reached, all the users can complete the PSS process.

In the other hand, we should also look the performance on BS, Figure 4.5 shows the length on the searching process according to the location error, for each sequence implemented on BS side two lines are drawn, one in case of any conservative UE sequence and another for NC one, since with the second one the UE is reducing the effort in finding connection now BS has to compensate it doing more searches especially when it uses DGS and the orientation error is within central values because conservative sequence is more similar to NC when the position error increases. When we consider the NC sequence having an orientation error different from zero, the curve will be between the conservative and non-conservative lines of the same BS algorithm as exposed in the figure.

4.2.1.2 Scenario with obstacles

Now we consider the case where 10 obstacles have been placed in the area, some of the results are displayed in the Figure 4.6 and Figure 4.7 to get the main idea of what is happening; as expected the number of switches in both sides, BS and MS, is greater than in free space even when there is no error on the position nor orientation. There is not any positive contribution by the conservative MPAE sequences for any error values, the ZZ-SLS present always the best performance. The 11.8% of users cannot be covered on the managed propagation model because of the presence of obstacles even using conservative sequences at both sides which means to put the highest possible effort. The NC sequences keeps like the better option as orientation error decreases, getting a very important improvement at 0◦ orientation error, but there is a drawback in terms of coverage: given the possible obstructions on the area the opportunity that users are not able to cover the threshold is bigger, the highest the error, the longest is the search sequence therefore the worst case (on terms of coverage) is at the lowest error value; getting that around the 21% of the users cannot complete the PSS process in the worst case as shown in Figure 4.8.

To conclude the Subsection 2.1 is worth to highlight the advantage with respect to coverage, reached when directivity is available at UE, i.e. observing the Figure 4.9 we realize that in a scenario with obstacles, the quantity of UE unable to get connection is reduced to the half against when the user is omnidirectional. Moreover, if we compare the results on [7] where the UE does not have directionality capacity, there is a possible advantage when the MS is able to manage beamforming with respect to the total search duration depending on the orientation and location error and on the algorithms managed at both sides.

(40)

4.2.2 Directional BS

As mentioned in 2.1. when the BS is assumed as omnidirectional, from the UE point of view it is not possible to see a significative difference between the proposed algorithms ZZ-SLS and conservative MPAE given that they are forming the same sequences most of the times. In this study, we also consider two different cases where user thinks that base station offers directivity capability of 30◦ and 15◦ causing a change on the perception of received power, more directionality capability considered in the BS side means that sequences at mobile side begin and finalize from a smaller beam width making more remarkable the differences between the use of different UEs’ sequences depending also on the location error values as we can observe in Figure 4.10 and Figure 4.11 respectively.

Remembering that the lowest directivity capacity is when the aperture of beams corresponds to 360◦and consequently smaller aperture means more directivity capacity, in the case of no position error, as long as the orientation error increases, the MPAE sequence will be enlarged (using always the same beam width) leading to a number of searches inversely proportional to the considered BS directivity capacity as it can be deduced from the Figure 4.12.

From the Figure 4.13, we can notice that when the location error is small and MS is following a conservative algorithm, there is not more than one switch at the BS side because we obtain the same results for every BS sequence, so basically the resulting number of switches just depend on the MS’s sequence length. Since more considered BS directivity capacity implies a selection of lower populated beam-width ring on the UE, we obtain a better result for simple and even an improved one for MPAE-all every time the BS directivity rises; but this behavior is not met at short orientation error values, in fact is totally the opposite (slightly differentiable) due to some users point with beams which are not ideal for surpassing the threshold, so they need to continue on the searching and to use the ZZ-SLS part. In the non-conservative case, the behavior depends also on the BS sequence, mostly at lower values of orientation error: for directional capabilities, different from 360◦ some MSs can get uncovered easier than in conservative case using the same BS beam, then this time MS should wait according to the sequence of BS to get out from that situation. EDP and LZ-PSEDP get almost the same curves which are always better than DGS.

For a higher location error, the MPAE region is bigger every time the supposed BS directivity grows, unless there is some exception, because it will cover more beam width rings starting from the minimum one which is supposed to reach (from the UE’s nominal position) the enough power to complete the PSS process and finishing on that one that covers the maximum distance dmax , reaching thinner beam widths than in

small error values, which causes a smaller probability of falling in uncovered situation when using just the NC part of the sequence using the same BS beam in both error cases, i.e., when the location error is 100m, Figure 4.14:

• 360◦ _{directional capacity BS : here we have the so-called exception, most of users}

have its first and last beam width of the sequence on last beam-width ring, so most of MPAE-simple sequences will be allocated in the last rings covering also the ZZ-SLS, consequently MPAE-all performance will be almost the same

(41)

than MPAE-simple being in both cases the best options regarding to further directionality. Talking about the non-conservative versions, in MPAE-simple, it presents the worst behavior since we can see reflected the fact that more directivity is better, instead MPAE-all will just take the thinnest beam widths in most of times being the best option.

• 30◦ directional capacity BS : here the users manage generally more beams (and not just the last one like on omnidirectional BS case) , most of them belong-ing to the thinnest beam-width rbelong-ings, resultbelong-ing like the possible worst case for NC-MPAE-all depending on the BS sequence, again for NC-MPAE-simple more directivity means a better performance so 30◦ BS directionality is in between om-nidirectional and 15◦ BS performance (even closer to omnidirectional), similar from omnidirectional BS the MPAE sequences are covering also the ZZ-SLS part and apart from that makes the search on more (the preceding) rings resulting on the worst case.

• 15◦ directional capacity BS : the majority of the beams which are inside the MPAE section fit in middle to high beam-width rings so in this case MPAE is taking less populated rings plus the ZZ-SLS becoming in the worst case for con-servative MPAE, in opposite to NC-MPAE-single which has the best behavior over all the considered directional capacities on BS and MS algorithms. Regard-ing to NC-MPAE-all, it is the possible worst result dependRegard-ing on the BS sequence due to the possibility that users experiment insufficient received total power. When very high position error is considered i.e. 250m the best behavior for all the MS and BS algorithms corresponds to omnidirectional BS as shown in the Figure 4.15, this is because the lower is the BS directionality the higher percentage of users ending its MPAE-simple sequences at the last beam, in this specific situation all the users end at the thinnest beam width for 360◦ and 30◦ of direction in BS and just a small percentage in 15◦ BS has to add the ZZ-SLS; using its non-conservative version PSS can delay more, given the huge location error, BS can easily point with a set of not suitable beams making easier to get connection if MS use the thinnest beam width which offers more focused power. Concerning to MPAE-all, it has notoriously the worst performance in both versions, conservative and non-conservative, with pronounced differences between each BS directionality considered, the higher the BS directionality, the lower the beam width where the sequence starts and then the longer the sequence and the worse the result as shown in the image.

To close this chapter, we should mention that is less relevant to put effort on the algorithms on the MS side to complete the PSS process every time the errors are bigger, i.e. if the error in angle is big, is better to use a not complicated algorithm as SLS because it can get the same or little better outcome than an smarter searching process or in case of very high location error we can stay on the easiest assumption which is supposing an omnidirectional BS because it will have better results than assuming a BS with higher directional capacity. Also, we saw that when location error gets greater, MPAE-all and its NC version, which is more robust algorithm and is the best option at low location errors, is getting even worse than MPAE-simple and (NC)MPAE-simple

(42)

(43)

Figure 4.3: Average on number of switches on MS side Vs. Orientation error with a)0m, b)10m, c)100m and d)250m of location error with different BS sequences using conservative algorithms at MS side.

(44)

Figure 4.4: Average on number of switches on MS side Vs. Orientation error comparison between conservative and non-conservative MPAE sequences with a)100 m and b)250 m of location error using LZ-PSEDP sequence on BS side.

Figure 4.5: Average number of switches on BS side according to location error with zero orientation error

(45)

Figure 4.6: Average number of switches on MS side according to orientation error with position error and conservative sequences for (a)0m, and position error and NC sequences for (b)0m, (c)10m, (d)100m.

(46)

Figure 4.7: Average number of switches on BS side according to location error with zero orientation error.

Figure 4.8: Average of uncovered UEs using NC MPAE, according with the position and orientation error.

(47)

Figure 4.10: First beam-width for MPAE-all at (a)360◦_{, (b)30}◦_{, (c)15}◦ _{considered directional}

(48)

Figure 4.11: Last (unique) beam width for MPAE-all (MPAE-single) for (a)10m, (b)100m, (c)250m location error.

Figure 4.12: Average number of switches on MS side according to orientation error with 0m position error and MPAE sequences (including conservative and non-conservative)

(49)

Figure 4.13: Average number of switches on MS side according to orientation error with 10m position error for (a) conservative MPAE on BS, (b) non-conservative MPAE on MS and DGS on BS, (c) non-conservative MPAE on MS and LZ-PSEDP on BS.

(50)

Figure 4.14: Average number of switches on MS side according to orientation error with 100m position error and MPAE sequences (including conservative and non-conservative) at MS side and LZ-PSEDP at BS side.

(51)

Figure 4.15: Average number of switches on MS side according to orientation error with 250m position error and MPAE sequences (including conservative and non-conservative) at MS side and LZ-PSEDP at BS side.

(52)

Chapter 5

Conclusions and further

investigation

It is important to go deeper in the investigation of the technologies supported by mm-Wave because is an essential part on the 5G communications needed in the near future for copping the users’ needs. As the consequence of the weakness on the propagation loss, one of these technologies is the implementation of highly directional devices in the small cell base and mobile station where both have to pass through a cell discovery process to set the first access to the network. The required time for the first connec-tion contributes in the total latency that the user experiments to get a service thus impacting over the offered QoS and acquiring a valuable attraction for its research.

In this work, we have proposed some smart algorithms that define the sequences of beam configurations that MS should explore in the cell discovery process defined by using context information like the position, orientation, the respective accuracy and propagation model: ZZ-SLS who takes just the former information and uses just the thinnest available beamwidth, meanwhile non-conservative MPAE family uses all of the previous data to define the set of beams which have more probability to succeed in the connection, using all of them (NC-MPAE-all) or just those one whit the thin-nest beamwidth belonging within that group (NC-MPAE-simple) and its conservative version is characterized by the inclusion of ZZ-SLS sequence after the NC-MPAE one guarantying the exploration over each one of the available directions on the Mobile side.

In all the considered scenarios, we analyzed the average of latency results for every combination of sequences between Mobile and Base stations, where MS used SLS, ZZ-SLS and MPAE family (conservative and non-conservative), the BS managed DGS, EDP and LZ-PSEDP, and there was not any obstacle unless it is specified. Where the most robust sequence at the user side, that one which applies effort in the searching process, is the MPAE-all followed by NC- MPAE-all, while ZZ-SLS is the less complex one.

As first step, we have assumed that MS considers to BS as omnidirectional and under this circumstance we concluded that the conservative versions of MPAE do not contribute positively in a remarkable way farther than ZZ-SLS does it. There is not a strong reason to apply this kind of complexity if we obtained almost the same, or