Performance Analysis of Network-MIMO Systems

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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 v 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 vi LIST OF TABLES Page Table 1 Power Delay Profile .................................................................................. 35 Table 2 Simulation parameters ............................................................................... 39 vii LIST OF FIGURES Page Figure 1 MIMO communication from SISO to IA-MIMO (Source: www.wikipedia.org) ................................................................................................. 4 Figure 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” ....................................................................... 9 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. .......... 14 Figure 4 Illustration of MU-MIMO: Downlink and Uplink .................................. 15 Figure 5 MU-MIMO systems: MIMO Broadcast (Source: www.wikipedia.org) . 16 Figure 6 MU-MIMO systems: MIMO MAC (Source: www.wikipedia.org) ....... 17 Figure 7 Frequency reused in cellular network with the reuse factor is 3 and 7. Cells of same color are used with same frequency. ............................................... 18 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. .............................. 20 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. .. 20 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. .............................................................. 22 viii 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........................................................................................ 23 Figure 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........................................................................................ 24 Figure 13 Example of a small wireless communication with terminals, AP and the Central Network Controller. ................................................................................... 25 Figure 14 Network MIMO solution where all the signals are useful, i.e., interference is removed .......................................................................................... 25 Figure 15 Conventional vs. Network MIMO average SINR and data rate improvements. ........................................................................................................ 26 Figure 16 Wireless network with two transmit and two receive antennas communicating through independent channels ...................................................... 27 Figure 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 ............................................................................................................ 31 Figure 19 Block Diagram showing key functions that are to be implemented in MATLAB simulation ............................................................................................. 37 Figure 20 Simulation environment with 9 cell, each cell include 1 access point and 1 end-user with randomly place. ............................................................................ 40 Figure 21 OFDM Pilot symbol to estimate the channel state information at both transmitter (AP/user) and receiver (user/AP) side with 3 users. ............................ 41 Figure 22 Compare between real channel and the estimated channel by using pilot symbol. ................................................................................................................... 42 ix 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). ......................... 43 Figure 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 x ABBREVIATIONS 1G, 2G, 3G, 4G 1st to 4th generations of wireless (phone) networks BER Bit Error Rate CSCG Circularly Symmetric Complex Gaussian CSI Channel State Information CSIR Channel State Information at the Receiver CSIT Channel State Information at the Transmitter DPC Dirty Paper Coding GSM Global System for Mobile(originally: Groupe Spéciale Mobile) IEEE Institute of Electrical and Electronics Engineers LOS Line of Sight MIMO Multiple-Input Multiple-Output MISO Multiple-Input Single-Output MMSE Minimum Mean Square Error MU-MIMO Multiuser MIMO NLOS Non Line of Sight OFDM Orthogonal Frequency Division Multiplexing OSTBC Orthogonal Space Time Block Code PEP Pairwise Error Probability RF Radio Frequency SDMA Space Division Multiple Access SER Symbol Error Rate SIMO Single-Input Multiple-Output SINR Signal to Interference and Noise Ratio SNR Signal to Noise Ratio STBC Space Time Block Code xi STC Space Time Code SU-MIMO Single-User MIMO WiMAX Worldwide Interoperability for Microwave Access WLAN Wireless Local Area Network ZF Zero-Forcing MSE Mean Square Error xii 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 1 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 2 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. 3 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. • Evolutionary approaches: 1. Use an existing techniques with enhanced PHY capabilities, perhaps a 16×16 array configuration. 2. Use new MIMO algorithms such as pre-coding or multi-user scheduling at the transmitter. • Revolutionary approaches: developing the fundamentally of new MIMO concepts. Based on the literature, we summarize a number of advanced MIMO techniques that leverage multiple users as seen in Fig 1: • 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. 4 • 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 5 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. 6 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. 2.1 Wireless Background A simple wireless communication system consists of a transmitter and a receiver, both equipped with a single antenna, transmitting information-carrying electromagnetic waves over space. The transmit antenna provides the input to the wireless channel, and the output is picked up by the receive antenna, thus, forming 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 highrate 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, 7 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. 8 Figure 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” 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: 9 ℎ11 ℎ12 ℎ ℎ 𝐻𝐻 = � 21 22 ⋮ ℎ1𝑀𝑀 ℎ𝑀𝑀2 … ⋯ ⋱ ⋯ ℎ1𝑁𝑁 ℎ2𝑁𝑁 � ⋮ ℎ𝑀𝑀𝑀𝑀 The noise at the receiver is another column matrix of size [N, 1], denoted by w: 𝑤𝑤 = [𝑤𝑤1 , 𝑤𝑤2 , … , 𝑤𝑤𝑁𝑁 ]𝑇𝑇 So the receiver vector is [N, 1] vector that satisfied: 𝑅𝑅 [𝑚𝑚] = 𝐻𝐻. 𝑆𝑆[𝑚𝑚] + 𝑤𝑤[𝑚𝑚] [2-1] Where m is a real number from 1 to N. 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: 𝐵𝐵𝐵𝐵𝐵𝐵 � 𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑆𝑆𝑆𝑆𝑆𝑆] � 𝐻𝐻𝐻𝐻 Where: C is the Shannon limits on channel capacity SNR is signal-to-noise ratio B is bandwidth of channel. In the practical case of time-varying and randomly fading wireless channel, the capacity can be written as: 𝐵𝐵𝐵𝐵𝐵𝐵 � 𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑆𝑆𝑆𝑆𝑆𝑆. |𝐻𝐻 |2 ] � 𝐻𝐻𝐻𝐻 [2-2] Where H is the 1x1 unit-power complex matrix Gaussian amplitude of the channel. Moreover, it has been noticed that the capacity is very small due to fading events [6]. 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 10 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: 𝑁𝑁 2 . 𝑀𝑀2 . 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑆𝑆𝑆𝑆𝑆𝑆 ≈ = 𝑀𝑀. 𝑁𝑁. 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑁𝑁. 𝑀𝑀. (𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛) Therefore, the capacity of MIMO channels in this case is: 𝐵𝐵𝐵𝐵𝐵𝐵 � 𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑀𝑀. 𝑁𝑁. 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 ] � 𝐻𝐻𝐻𝐻 [2-3] [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 + 𝑁𝑁 𝐵𝐵𝐵𝐵𝐵𝐵 � . 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 � � 𝑀𝑀 𝐻𝐻𝐻𝐻 [2-5] However, we have M of these channels, so the total capacity of the system is: 𝐵𝐵𝐵𝐵𝐵𝐵 𝑁𝑁 � 𝐶𝐶 = 𝑀𝑀. 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 �1 + . 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 � � 𝐻𝐻𝐻𝐻 𝑀𝑀 Assume𝑁𝑁 ≥ 𝑀𝑀, the capacity of MIMO channels is roughly equal to: 𝑁𝑁 𝐵𝐵𝐵𝐵𝐵𝐵 � 𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 �1 + . 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 � � 𝑀𝑀 𝐻𝐻𝐻𝐻 [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 11 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]: 𝑆𝑆𝑆𝑆𝑆𝑆 𝐵𝐵𝐵𝐵𝐵𝐵 � 𝐶𝐶 = 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 �𝑑𝑑𝑑𝑑𝑑𝑑 �𝐼𝐼𝑁𝑁 + . 𝐻𝐻𝐻𝐻 ∗ �� � 𝑀𝑀 𝐻𝐻𝐻𝐻 [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 → 𝐼𝐼𝑁𝑁 𝐵𝐵𝐵𝐵𝐵𝐵 � 𝐶𝐶 = 𝑀𝑀. 𝐵𝐵. 𝑙𝑙𝑙𝑙𝑙𝑙2 [1 + 𝑆𝑆𝑆𝑆𝑆𝑆] � 𝐻𝐻𝐻𝐻 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 (singlelayer) 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 12
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