Performance Analysis of Network-MIMO Systems

ABBREVIATIONS 1G, 2G, 3G, 4G 1st to 4th generations of wireless phone networks CSCG Circularly Symmetric Complex Gaussian CSI Channel State Information CSIR Channel State Information

TABLE OF CONTENTS

Page

LIST OF TABLES vii

LIST OF FIGURES viii

ABBREVIATIONS xi

CHAPTER 1: INTRODUCTION 1

1.1 Wireless Communication 1

1.2 MIMO Techniques 2

1.3 Network-MIMO systems 5

1.4 Thesis’s Structure 5

CHAPTER 2: BASIC MIMO THEORY 7

2.1 Wireless Background 7

2.2 MIMO Communications 8

2.2.1 MIMO systems Model 9

2.2.2 Theoretical MIMO Capacity Gains 10

2.2.3 Types of MIMO 12

2.3 Multi-user Communications 12

2.3.1 Limitations of Single-User view 13

2.3.2 Multi-User MIMO (MU-MIMO) 14

2.4 Multi-cell Communications 18

2.4.1 Limitations of Single-Cell View 19

2.4.2 Multi-Cell MIMO 19

3.1 Background 21

3.1.1 Inter-cell Interference 21

3.2 Theory behind Network MIMO 27

3.3 Network-MIMO systems Model 28

3.3.1 Uplink 29

3.3.2 Downlink 30

CHAPTER 4: SIMULATION AND RESULTS 34

4.1 Simulation Model 34

4.2 Simulation Diagram 36

4.3 Simulation Results 39

CHAPTER 5: CONCLUSION 45

REFERENCES 46

LIST OF TABLES

Page Table 1 Power Delay Profile 35Table 2 Simulation parameters 39

LIST OF FIGURES

Page Figure 1 MIMO communication from SISO to IA-MIMO (Source:

www.wikipedia.org) 4Figure 2 MIMO channel with M transmit and N receive antennas The sketched path, from transmitter and receiver, represent the channel which h11 is the channel between transmit antenna 1 and receive antenna 1 The transmit and receive signal are often presented by “black boxes” 9Figure 3 From single- to multiuser communications, where all the users in the coverage area are simultaneously considered in the optimization The base station may choose to transmit data to a single or multiple user terminals at once 14Figure 4 Illustration of MU-MIMO: Downlink and Uplink 15Figure 5 MU-MIMO systems: MIMO Broadcast (Source: www.wikipedia.org) 16Figure 6 MU-MIMO systems: MIMO MAC (Source: www.wikipedia.org) 17Figure 7 Frequency reused in cellular network with the reuse factor is 3 and 7 Cells of same color are used with same frequency 18Figure 8 From multi-user to multi cell communication, where all the cells and all the users in the network are simultaneously considered in optimization The solid line marks the useful signals, where the interfering is dashed 20Figure 9 Coordination or Cooperation between all base stations in the wireless communication network under fast backhaul The central unit played an central network controller for control the coodination/cooperation between all the BS 20Figure 10 Illustration of typical interference between users and access points in a cell-based wireless system The left image shows interference in down link and the right image shows interference in uplink 22

Figure 11 Illustration of traditional interference control between users and access points in a cell-based wireless system The left image shows down link and the right image shows uplink 23Figure 12 Illustration of MIMO interference control between users and access points in a cell-based wireless system The left image shows down link and the right image shows uplink 24Figure 13 Example of a small wireless communication with terminals, AP and the Central Network Controller 25Figure 14 Network MIMO solution where all the signals are useful, i.e.,

interference is removed 25Figure 15 Conventional vs Network MIMO average SINR and data rate

improvements 26Figure 16 Wireless network with two transmit and two receive antennas

communicating through independent channels 27Figure 17 Network-MIMO uplink channel: from m-th cell to all of base station.

29

Figure 18 Network-MIMO downlink channel: from all base station to k-th user in the m-th cell 31Figure 19 Block Diagram showing key functions that are to be implemented in MATLAB simulation 37Figure 20 Simulation environment with 9 cell, each cell include 1 access point and

1 end-user with randomly place 40Figure 21 OFDM Pilot symbol to estimate the channel state information at both transmitter (AP/user) and receiver (user/AP) side with 3 users 41Figure 22 Compare between real channel and the estimated channel by using pilot

Figure 23 Channel estimation between 4-th AP and 1-st User (in the different cell) and the channel between 1-st AP and 1-st cell (in the same cell) 43Figure 24 Comparison between performance of Network-MIMO and non

Network-MIMO communication system with the ranger of Signal-to-Noise Ratio (SNR) is 10 to 20 dB 43

ABBREVIATIONS

1G, 2G, 3G, 4G 1st to 4th generations of wireless (phone) networks

CSCG Circularly Symmetric Complex Gaussian

CSI Channel State Information

CSIR Channel State Information at the Receiver

CSIT Channel State Information at the Transmitter

GSM Global System for Mobile(originally: Groupe Spéciale Mobile) IEEE Institute of Electrical and Electronics Engineers

MIMO Multiple-Input Multiple-Output

MISO Multiple-Input Single-Output

MMSE Minimum Mean Square Error

MU-MIMO Multiuser MIMO

OFDM Orthogonal Frequency Division Multiplexing

OSTBC Orthogonal Space Time Block Code

PEP Pairwise Error Probability

SDMA Space Division Multiple Access

SIMO Single-Input Multiple-Output

SINR Signal to Interference and Noise Ratio

STC Space Time Code

SU-MIMO Single-User MIMO

WiMAX Worldwide Interoperability for Microwave Access

WLAN Wireless Local Area Network

CHAPTER 1: INTRODUCTION

Modern wireless networks tend to be interference limited, mainly caused by their own base stations and mobile terminals Suppressing interference would thus result in significant improvements in data rates, capacity, and coverage Our studies determined the feasibility of achieving significant performance Network MIMO (Multiple-Input/Multiple-Output) gains This led to a proposed solution to suppress inter-cell interference via phase- coherent coordination and joint spatial filtering between the base stations

1.1 Wireless Communication

Wireless communication services are basic features of global civilization, soon available everywhere and adopted by everyone The development has been especially rapid in the last few decades, in which time wireless communications has taken a leap from being a niche technology towards achieving a status as an independent growth industry and diverse research area [1]

The history of wireless communication technologies can be traced back over 140 years, to Maxwell’s theories on electromagnetic waves and Hertz’ later demonstration of their existence [2] Marconi’s 1896 invention of wireless telegraphy supplied the first useful application, enabling transatlantic communication services Then followed radiotelephony, and commercial car phone services were spreading slowly from the late 1920s [3]

First generation (1G) personal mobile phone systems came in the early 1980s, with user terminals that were expensive and of questionable portability However, the introduction of a cellular structure, for base station location and

frequency reuse, helped control the interference and made the networks more easily scalable, and the wireless revolution was ignited The analog 1G networks were followed by the digital second generation (2G) systems, among which the GSM, first introduced for regular service in Finland in 1991, is one successful example

Third generation (3G) standards were released from 2000, aiming for unified global roaming, more users and higher data rates However, the actual deployment of networks was long delayed by enormous spectrum licensing fees and a lack of industry incentive The fourth generation (4G) of wireless networks,

also known as Beyond 3G, notably include implementations of the WiMAX and

the Long-Term Evolution (LTE) standards [4]

For years, there is an on-going shift in end-user mobile communications service The future of wireless communication is multimedia, which includes image, video, and local area network applications; with the data transmission rate more than 1000 times faster than that of the present systems However, the physical limits imposed by the mobile radio channel cause performance degradation and make it very difficult to achieve high bit rate at low error rate over the time dispersive wireless channels Another key limitation is co-channel interference (CCI) which can also significantly decrease the capacity of wireless and personal communications systems

1.2 MIMO Techniques

As presented in Section 1, future wireless communication networks will need to support extremely high data rates in order to meet the rapidly growing demand for broadband applications Existing wireless communication technologies cannot

efficiently support broadband data rates, due to their sensitivity to fading Multiple antennas have recently emerged as a key technology in wireless communication systems for increasing both data rates and system performance

The benefits of exploiting Multiple-Input-Multiple-Output (MIMO) may be

categorized by the following [6]:

Array gain

Array gain refers to the average increase in the SNR at the receiver that arises from the coherent combining effect of multiple antennas at the receiver or transmitter or both The average increase in signal power at the receiver is proportional to the number of receive antennas

Diversity gain

Signal power in a wireless channel fluctuates When the signal power drops significantly, the channel is said to be in a fade Diversity is used in wireless channels to combat fading Utilization of diversity in MIMO channels requires antenna diversity at both receive and transmit side The diversity order is equal to the product of the number of transmit and receive antennas, if the channel between each transmit-receive antenna pair fades independently

Spatial multiplexing (SM)

SM offers a linear (in the number of transmit-receive antenna pairs or min (Mt, Mr) increase in the transmission rate for the same bandwidth and with no additional power consumption

Interference reduction

Co-channel interference arises due to frequency reuse in wireless channels When multiple antennas are used, the difference between the spatial signatures of the desired signal and co-channel signals can be exploited to reduce the interference This operation is done at the receiver side

Figure 1 MIMO communication from SISO to IA-MIMO (Source: www.wikipedia.org)

In addition, we will increase system performance or reduce cost by apply some enhancement techniques to MIMO communication systems These can be categorized into two groups: evolutionary and revolutionary approaches

• Cross-layer MIMO: Scheduling, etc

• Advanced decoding MIMO: Multi-user detection such as MLD

• Beamforming and SDMA: widely known multi-user MIMO (MU-MIMO) scheme

• Infrared/Non-infrared network optimization

• Network MIMO (Net-MIMO)

• Cognitive MIMO based on intelligent techniques

• Cooperative/competitive MIMO

• Cooperation: DPC, Wyner-Ziv, etc

• Competitive: Game theory, autonomous packets, implicit MAC fairness

• etc

1.3 Network-MIMO systems

Network MIMO is a MIMO communication scheme, which falls within the family

of techniques that use cooperation in a MIMO systems to increase system performance More specifically, network MIMO is a family of techniques whereby each end user in a wireless access network is served not just by multiple antennas but also by multiple access points [8] This allows users similar performance increases to those seen in other MIMO processing methods but achieves it by taking advantage of the already existing infrastructure in any multi-point access network

For example, an indoor wireless system for a small business would have several access points (AP) These access points would all be connected through a wired grid to a central router and then to the internet via an ISP Taking advantage

of the fact, these access points are all connected, network MIMO could be used to coordinate the transmission and reception of data without needing to add additional antennas to local access points

1.4 Thesis’s Structure

In general terms, this thesis focuses on performance analysis of network MIMO systems Because Network-MIMO is an enhancement model of the original MIMO systems, we first analysis the theoretical of MIMO techniques in Chapter

2 That is the basic knowledge to work with Network-MIMO in the next chapters

In Chapter 3, we consider a Network-MIMO systems where two or more

AP served each end-user to achieve high system performance while also reduces the system interference

Chapter 4 presented the simulation model and simulation results of a Network MIMO systems using Matlab The model simulates an indoor wireless access system with multiple Access Point (AP) and multiple End-User For simplicity, we assumed that the MIMO link is created only by the way of multiple wireless access The simulation results show that Network MIMO systems can be archive high system performance than the non Network-MIMO systems

Finally, we have some conclusion and discussion about Network-MIMO systems in Chapter 5

CHAPTER 2: BASIC MIMO THEORY

Future wireless communication networks will need to support extremely high data rates in order to meet the rapidly growing demand for broadband applications such as high quality audio and video Existing wireless communication technologies cannot efficiently support broadband data rates, due

to their sensitivity to fading Multiple-input multiple-output (MIMO) is a key technique for increasing both data rates and system performance It can increase data throughput and link range without bandwidth or transmit power expansion

a Single-Input Single-Output (SISO) system

In this thesis, communications is assumed to take place between a stationary access point (AP) or base station (BS) and a mobile user terminal (MS)

The BS transmits data to the user terminal on the downlink, while the reverse direction is the uplink With a multiple base stations network, these are often assumed to be connected by a wired or wireless backbone network, offering high-

rate inter-base communications

The wireless communications medium is space, and so a system’s characteristics are highly dependent on the local propagation environments formed by natural and manmade structures, such as mountains, foliage, buildings,

and large vehicles Flat and rural areas offer free space conditions, under which a

transmitted signal will reach the destination only via the direct Line-Of-Sight (LOS) path Non Line-Of-Sight (NLOS) conditions occur when the direct path is blocked, which is common in cities and suburban areas, but which may also be caused by a countryside hill

Propagation over space is additive in nature, which makes wireless communications susceptible to crosstalk between same-frequency signals, so

called co-channel interference (CCI) If the desired and the interfering signal are

received with comparable powers, the desired signal may well be impossible to retrieve from the new, sum signal

2.2 MIMO Communications

In wireless communication, multiple input multiple output (MIMO) technology is the use of multiple antennas in both transmitter and receiver It has attracted attention in modern wireless communications, because it offers significant increases in data throughput and link range without additional bandwidth or transmit power by higher spectral efficiency (more bits per second per hertz of bandwidth) and link reliability or diversity (reduced fading) Because of these properties, MIMO is an important part of modern wireless communication standards such as IEEE 802.11n, 3GPP Long Term Evolution (LTE), 4G, and WiMax

Figure 2 MIMO channel with M transmit and N receive antennas The sketched path, from transmitter and receiver, represent the channel which h 11 is the channel between transmit antenna 1 and receive antenna 1 The transmit and receive signal are often presented by “black boxes”

2.2.1 MIMO systems Model

We consider a MIMO systems with a transmit array of MT antennas and a receive array of MR antennas The block diagram of such a system is shown in the Fig 2

The transmitted matrix is an [M, 1] column matrix S where Si is the 𝑖𝑖𝑡𝑡ℎ

component, transmitted from antenna i, and of the form:

𝑆𝑆 = [𝑆𝑆1, 𝑆𝑆2, … , 𝑆𝑆𝑀𝑀]𝑇𝑇

Where ( ) T denotes the transpose matrix

For simplicity, we consider the channel is a Gaussian channel such that the elements of S are considered to independent identically distributed (i.i.d) variables Assume that the channel state information (CSI) is known at receiver but unknown at the transmitter side and the signals transmitted from each antenna have equal powers of Es/M with Es is the power of transmitted signal

The channel matrix can be given by:

2.2 2 Theoretical MIMO Capacity Gains

According to Shannon capacity of wireless channels, given a single channel corrupted by an additive white Gaussian noise at a level of SNR, the capacity is:

𝐶𝐶 = 𝐵𝐵 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑆𝑆𝑁𝑁𝑅𝑅] �𝐵𝐵𝐵𝐵𝐵𝐵𝐻𝐻𝐻𝐻� Where: C is the Shannon limits on channel capacity

Form the capacity of SISO system; we can calculate the theoretical capacity gain

of MIMO communication system in two cases:

Same signal transmitted by each antenna

In this case, the MIMO systems can be view in effect as a combination of the Single Input Multiple Output (SIMO) and Multiple Input Single Output (MISO) channels The corresponding SNR of MIMO systems is:

𝑆𝑆𝑁𝑁𝑅𝑅 ≈ 𝑁𝑁2 𝑀𝑀𝑁𝑁 𝑀𝑀 (𝑠𝑠𝑙𝑙𝑖𝑖𝐵𝐵𝑝𝑝)2 𝐵𝐵𝑖𝑖𝑙𝑙𝑠𝑠𝑎𝑎𝑙𝑙 𝐵𝐵𝑙𝑙𝑤𝑤𝑝𝑝𝑝𝑝 = 𝑀𝑀 𝑁𝑁 𝑆𝑆𝑁𝑁𝑅𝑅𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 [2-3]

Therefore, the capacity of MIMO channels in this case is:

𝐶𝐶 = 𝐵𝐵 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑀𝑀 𝑁𝑁 𝑆𝑆𝑁𝑁𝑅𝑅𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆] �𝐵𝐵𝐵𝐵𝐵𝐵𝐻𝐻𝐻𝐻� [2-4] Thus, we can see that the channel capacity for the MIMO systems is higher than that of SIMO and MIMO systems

From Equation [2-4], we can see that the capacity is increasing inside the log function This means that trying to increase the data rate by simply transmitting more power is extremely costly

Different signal transmitted by each antenna

The big idea in MIMO is that we can send different signals using the same bandwidth and still be able to decode correctly at the receiver Thus, it like that we are creating a channel for each one of the transmitters The capacity of each one of these channels is roughly equal to:

𝐶𝐶 = 𝐵𝐵 𝑙𝑙𝑙𝑙𝑙𝑙2 �1 +𝑀𝑀𝑁𝑁 𝑆𝑆𝑁𝑁𝑅𝑅𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆� �𝐵𝐵𝐵𝐵𝐵𝐵𝐻𝐻𝐻𝐻� [2-6]

Thus, we can get linear increase in capacity of the MIMO channels with respect to the number of transmitting antennas Therefore, the key principle at work here is

that it is more beneficial to transmit data using many different low-powered channels than using one single, high-powered channel

In the practical case of time varying and randomly fading wireless channel, it shown that the capacity of M x N MIMO systems is [6]:

𝐶𝐶 = 𝐵𝐵 𝑙𝑙𝑙𝑙𝑙𝑙2 �𝑑𝑑𝑝𝑝𝑡𝑡 �𝑆𝑆𝑁𝑁 + 𝑆𝑆𝑁𝑁𝑅𝑅𝑀𝑀 𝐻𝐻𝐻𝐻∗�� �𝐵𝐵𝐵𝐵𝐵𝐵𝐻𝐻𝐻𝐻� [2-7]

We can see that the advantage of MIMO systems is significant in capacity As an example, for a system which 𝑀𝑀 = 𝑁𝑁 and 𝐻𝐻𝐻𝐻∗/𝑀𝑀 → 𝑆𝑆𝑁𝑁

𝐶𝐶 = 𝑀𝑀 𝐵𝐵 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑆𝑆𝑁𝑁𝑅𝑅] �𝐵𝐵𝐵𝐵𝐵𝐵𝐻𝐻𝐻𝐻� Therefore, the capacity increases linearly with the number of transmit antennas

2.2.3 Types of MIMO

MIMO can be categorized into three main categories: pre-coding, spatial multiplexing, and diversity coding Pre-coding is multi-layer beamforming in a narrow sense or all spatial processing at the transmitter in a wide-sense In (single-layer) beamforming, the same signal is emitted from each of the transmit antennas with appropriate phase (and sometimes gain) weighting such that the signal power

is maximized at the receiver input Spatial multiplexing requires MIMO antenna configuration Diversity Coding techniques are used when there is no channel knowledge (channel state information) at the transmitter

2.3 Multi-user Communications

There is a shifting trend in research and industry in wireless communication from single-user (SU) to multiuser (MU), which, in the prevalent cellular network structure, expands the optimization domain to the entire cell The multiple antenna base station and the single or multiple-antenna user terminals form a generalized

MIMO systems, and approaches for this scenario are referred to as MU-MIMO communications Gesbert et al [7] give a recent overview of the MU-MIMO

paradigm shift, so named because the single- and the multiuser views are essentially different

2.3.1 Limitations of Single-User view

The above MIMO schemes and analysis consider a single link between a

transmitter and a receiver, often referring to the single-user scenario when this

link is between a base station and a user terminal The single MIMO can be seen

as point-to-point MIMO communication This has some limitations: this focus neglects lessons learned from information theory, the demands and conditions of other users, and the presence of co-channel interference (CCI)

First, existing information theoretic results advocate the use of orthogonal multiple-access schemes, where multiple, simultaneous users share a common spectral resource, but are separated in the spatial domain

non-Second, disregarding the other users may limit the performance by keeping

a certain single-user connection, even when the channel conditions are unfavorable

Third, neglecting the interference makes us overly optimistic on behalf of the MIMO performance, as the above capacity results are only achievable for idealized, interference-free transmissions With no knowledge about the channel, the transmitter and receiver are unable to mitigate it, and will simply treat is as noise Increasing degrees of CSI at the receiver enables more techniques that are sophisticated

Nowadays, we can see the shifting trend from single-user (SU) MIMO to multi-user (MU) MIMO communications, which, in the prevalent cellular network

structure, expands the optimization domain to the entire cell This allows for efficient intra-cell interference cancellation, and represents a natural step towards

the ultimate multi-cell scenario

Figure 3 From single- to multiuser communications, where all the users in the coverage area are simultaneously considered in the optimization The base station may choose to transmit data to a single or multiple user terminals at once

2.3.2 Multi-User MIMO (MU-MIMO)

Multi-user MIMO can leverage multiple users as spatially distributed transmission resources, at the cost of somewhat more expensive signal processing In comparison, conventional, or single-user MIMO considers only local device multiple antenna dimensions Multi-user MIMO algorithms are developed to enhance MIMO systems when the number of users, or connections, numbers greater than one Multi-user MIMO can be generalized into two categories: MIMO broadcast channels (MIMO BC) and MIMO multiple access channels (MIMO MAC) for downlink and uplink situations, respectively

Among the major benefits of MU-MIMO are [7]:

• The increased immunity against antenna correlation and channel matrix rank-deficiency, secured by the spatial distribution of the user terminals

• The MIMO multiplexing gain from scheduling multiple users, achievable even when these have simple, single-antenna terminals

• The multiuser gain, reaped from scheduling the best selection of users

Figure 4 Illustration of MU-MIMO: Downlink and Uplink

However, the unavoidable tradeoff between performance and complexity applies and MU-MIMO comes with some costly and computationally intense requirements, in particular for downlink point-to-multipoint communications:

First, the access to CSI at the base station is critical in order to form beams towards the user terminals, which have little or no interference-canceling capability Without this CSIT, the multiuser view holds no additional gains over single-user schemes [7] For the base station to procure channel knowledge is particularly demanding, inducing the extra complexity and delay associated with feedback

A second challenge lies in the extra complexity brought by the layered nature of MU-MIMO optimization, which necessarily involves both the physical and medium access (MAC) layers [4]

cross-To remove ambiguity of the words receiver and transmitter, we can adopt the terms access point (AP), and end-user An AP is the transmitter and an end-user is

the receiver for downlink environments, whereas an AP is the receiver and a user

is the transmitter for uplink environments Homogeneous networks are somewhat freed from this distinction

MU-MIMO can be generalized into two categories: MIMO broadcast channels (MIMO BC) and MIMO multiple access channels (MIMO MAC) for downlink and uplink situations, respectively

MIMO broadcast represents a MIMO downlink case in a single sender to

multiple receiver wireless networks Examples of advanced transmit processing for MIMO BC is interference aware pre-coding and SDMA-based downlink user scheduling For advanced transmit processing, the transmitter has to know the channel state information at the transmitter (CSIT) That is, knowledge of CSIT allows throughput improvement, and methods to obtain CSIT become of significant importance

Figure 5 MU-MIMO systems: MIMO Broadcast (Source: www.wikipedia.org)

MIMO BC systems have an outstanding advantage over point-to-point MIMO systems, especially when the number of transmit antennas at the transmitter, or

AP, is larger than the number of receiver antennas at each receiver (user) In the capacity approaching schemes, DPC pre-coding was using as pre-coding method and zero-forcing beamforming in near capacity scheme

MIMO MAC represents a MIMO uplink case in the multiple transmitters to

single receiver wireless network Examples of advanced receive processing for MIMO MAC is joint interference cancellation and SDMA-based uplink user scheduling For advanced receive processing, the receiver has to know the channel state information at the receiver (CSIR) Knowing CSIR is generally easier than knowing CSIT because to know CSIT costs many uplink resources to transmit dedicated pilots from each user to the AP MIMO MAC systems outperforms point-to-point MIMO systems especially when the number of receiver antennas at

an AP is larger than the number of transmit antennas at each user

Figure 6 MU-MIMO systems: MIMO MAC (Source: www.wikipedia.org)

2.4 Multi-cell Communications

Although the bright feature of the capacity improvement with addition gains for available for end-user which MIMO systems offer, only a fraction of the potential gains have been realized in practical systems Key reasons for this performance gap include the presence of co-channel interference (CCI), diminishing the effect

of MIMO communications, and the limited number of degrees of freedom offered for inter-cell interference mitigation, being confined to the single-cell domain

In both of single-user and multi-user wireless communications, the most challenges are solving the network-wide optimization and resource allocation

Conventional methods are based on divide-and-conquer strategy, which split the

global problem into many smaller, local problems to solve A successful implementation of this strategy is the mobile phone network, which also call cellular network

In cellular networks, each cell contains a single base station and multiple user terminals, and frequency pre-planning assigns carrier frequencies to cells in a

spatial reuse pattern Each such pattern is identified by a reuse factor, illustrated

for factors 3 and 7 in Fig 7, using idealized hexagonal cell shapes

Figure 7 Frequency reused in cellular network with the reuse factor is 3 and 7 Cells of same color are used with same frequency

2.4.1 Limitations of Single-Cell View

The divide-and-conquer strategy is conceptually simple, it allows for frequency

pre-planning and per-cell MU-MIMO optimization The CCI may be of intra-cell

or inter-cell origin, and while the former may be avoided by pre-cancellation in MU-MIMO, the latter may be reduced to negligible levels for sufficient reuse distance A worst-case interference analysis, assuming transmit power constraints, guarantees an upper limit on the inter-cell CCI

However, the performance suffers from the limited degrees of freedom available, and the general lack of inter-cell CSI, leading to the latter being considered as noise In addition, the scarcity of the spectral resources advocates aggressive frequency reuse for increased spectral efficiency, thereby increasing the

inter-cell CCI, so that the sum capacity becomes interference-limited

2.4.2 Multi-Cell MIMO

The way to solve the inter-cell interference and resource allocation problem, specific in spectrum efficiency, is change from single-cell to multi-cell communication That process forming the multi-cell MIMO systems as illustrated

in Fig 8