Performance Analysis of Network-MIMO Systems

Performance Analysis of Network-MIMO Systems Tạ Đức Tuyển Trường Đại học Công nghệ Luận văn ThS ngành: Kỹ thuật Điện tử - Viễn thông; Mã số: 60 52 70 Người hướng dẫn: TS. Trịnh Anh Vũ Năm bảo vệ: 2010

Performance Analysis of Network-MIMO Systems

Abstract: Network MIMO is a means of coordinating and processing the

information gathered from multiple- input multiple- output (MIMO) communication systems to increase spectral efficiency, robustness, and data rates These properties make it a topic of great interest in the near future as the number of wireless users continues to grow and their individual demands on bandwidth climb Systems employing network MIMO capitalize on the fact that inter-cell interference, a major problem for dense wireless systems, is a superposition of signals With careful coordination between receivers (and transmitters), these super-positions can be decoupled and the information they contain can be utilized The goal of this thesis is to investigate the ability of network MIMO techniques to increase data rates in multi-user indoor wireless networks of various sizes with various channel schemes The simulation results also show that Network MIMO systems can be increase data rates and good through put than non- networked MIMO systems

Keywords: Kỹ thuật điện tử; Mạng Mimo; Xử lý thông tin; Mạng truyền

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 𝑖𝑡𝑕

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

𝑆 = 𝑆1, 𝑆2, … , 𝑆𝑀 𝑇Where ( ) 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:

𝐶 = 𝐵 𝑙𝑜𝑔2 1 + 𝑆𝑁𝑅 𝐵𝑝𝑠

𝐻𝑧 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:

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:

𝐶 = 𝑀 𝐵 𝑙𝑜𝑔2 1 + 𝑁

𝑀 𝑆𝑁𝑅𝑆𝐼𝑆𝑂

𝐵𝑝𝑠

𝐻𝑧 Assume𝑁 ≥ 𝑀, the capacity of MIMO channels is roughly equal to:

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]:

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:

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

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

Figure 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

The main idea of multi-cell MIMO, which also called multi-cell optimization, is the coordinating/cooperative between the base station/access point under the controlled of central unit, which called multi-cell coordination and cooperation The wired or wireless backhaul, to which all base stations are attached, is exploited for inter-cell information exchange Fig 9 illustrates a multi-cell network with

overlapping coverage areas

Figure 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

Multi-cell optimization offers great flexibility, in particular, for the idealized setting of perfect BS cooperation The price to pay is the substantial and sometimes prohibitive, complexity from information exchange and large-scale processing In addition, while the optimization is often geared towards maximizing the sum network capacity, a trade-off between capacity- and fairness-orientation can be imagined for practical settings, where serving multiple users may be more important than achieving the best sum rate

CHAPTER 3: Network-MIMO SYSTEMS

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],[9] 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

In this chapter, we present the Network-MIMO systems model as an enhanced model of MIMO systems to achieve high system performance and reduce inter-cell interference

3.1 Background

Multiple-input multiple-output (MIMO) techniques can be applied to enhance the performance of wireless systems It so that the new system enables frequency reuse within each cell and still subject to the high levels of interference from other cells It is becoming increasingly clear that, MIMO schemes notwithstanding, major improvements in spectral efficiency will have to entail addressing such inter-cell interference

3.1.1 Inter-cell Interference

Traditionally, in cellular systems each user is assigned to an access point (AP) based on criteria like signal strength The user then communicates with that serving AP while causing interference to all other AP’s This point is illustrated in Fig 10, where two nearby users and access points interfere with each other during both uplink and downlink

Typically, problems with interference are avoided by differentiating between users in frequency, time, or code Such techniques are called frequency (FDMA), time (TDMA), and code (CDMA) division multiple access schemes, respectively By requiring nearby users to communicate over separate channels in one of these three domains, existing multi access schemes sacrifice spectral efficiency, data rates, and or capacity to provide users with more reliable communication This point is illustrated in Fig 11, where two nearby users and access points do not interfere with each other during both uplink and downlink thanks to F-, T-, or C-DMA

Useful transmission over all frequencies Transmissions over all frequencies causing interference Mobile Switching Center

MSC

Figure 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

Useful transmission over a set of frequencies (set 1) Useful transmission over a set of frequencies (set 2) Mobile Switching Center

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

3.1.2 Network MIMO

When apply MIMO techniques to traditional wireless communication, one

advantage of the new system is frequency reuse within each cell However, it also

is still subject to the high levels of interference from other cells It is becoming

MSC