Luận văn thạc sĩ performance analysis of network mimo systems

i VIET NAM NATIONAL UNIVERSITY, HA NOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY PERFORMANCE ANALYSIS OF NETWORK MIMO SYSTEMS A THESIS SUBMITTED IN FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MAST[.]

INTRODUCTION

Wireless Communication

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MIMO Techniques

The benefits of exploiting Multiple-Input-Multiple-Output (MIMO) may be categorized by the following [6]:

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

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

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

• Infrared/Non-infrared network optimization

• Cognitive MIMO based on intelligent techniques

Network-MIMO systems

Thesis’s Structure

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.

BASIC MIMO THEORY

Wireless Background

MIMO Communications

𝑆𝑆 = [𝑆𝑆 1 ,𝑆𝑆 2 , … ,𝑆𝑆 𝑀𝑀 ] 𝑇𝑇 Where ( ) T denotes the transpose matrix

The channel matrix can be given by:

The noise at the receiver is another column matrix of size [N, 1], denoted by w:

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

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:

𝐻𝐻𝐻𝐻� Where: C is the Shannon limits on channel capacity

SNR is signal-to-noise ratio

In the practical case of time-varying and randomly fading wireless channel, the capacity can be written as:

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

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

Thus, we can see that the channel capacity for the MIMO systems is higher than that of SIMO and MIMO systems

Different signal transmitted by each antenna

However, we have M of these channels, so the total capacity of the system is:

𝐻𝐻𝐻𝐻� Assume𝑁𝑁 ≥ 𝑀𝑀, 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]:

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 → 𝑆𝑆 𝑁𝑁

𝐻𝐻𝐻𝐻� Therefore, the capacity increases linearly with the number of transmit antennas

Multi-user Communications

2.3.1 Limitations of Single-User view

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

Second, disregarding the other users may limit the performance by keeping a certain single-user connection, even when the channel conditions are unfavorable

2.3.2 Multi-User MIMO (MU-MIMO)

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:

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

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

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

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

Multi-cell Communications

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

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.

Background

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

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

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

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

MSC by other names in literature, such as macro diversity, multi-cell MIMO/processing and base station cooperation/coordination

Figure 13 Example of a small wireless communication with terminals, AP and the Central Network Controller

Figure 15 Conventional vs Network MIMO average SINR and data rate improvements

Theory behind Network MIMO

Figure 16 Wireless network with two transmit and two receive antennas communicating through independent channels

Letting TX antenna 1 transmit a signal 𝑋𝑋1 and TX antenna 2 transmit a signal 𝑋𝑋2 The received signal at the RX antennas 1 and 2 are:

𝑌𝑌2 =𝐻𝐻21 ∗ 𝑋𝑋1 +𝐻𝐻22 ∗ 𝑋𝑋2 +𝑠𝑠2 [3-2] Where 𝐻𝐻 𝑖𝑖𝑖𝑖 is the channel between receiver 𝑖𝑖 and transmitter 𝑖𝑖, 𝑠𝑠1 and 𝑠𝑠2 are system noise respectively

Network-MIMO systems Model

We consider a cellular network with M cells of N T base station antennas each, serving K users with N receive antennas each Assuming a synchronous network

MIMO systems, we consider the received signal in two cases: uplink and downlink transmission

The Network-MIMO uplink channel is shown in Fig 17 Each BS receive signal from k-th cell and collaborate with other BS to decision the transmitted signal

Figure 17 Network-MIMO uplink channel: from m -th cell to all of base station

The received signal vector of the Network-MIMO systems can be written here:

- 𝑌𝑌𝐵𝐵𝑠𝑠𝐵𝐵=𝑣𝑣𝑝𝑝𝑣𝑣([𝑌𝑌 1 ,𝑌𝑌 2 , … ,𝑌𝑌 𝑀𝑀 ]) is the system receive signal vector that 𝑌𝑌 𝑚𝑚 is the receive signal vector at the m-th base station.

- 𝑋𝑋 𝑆𝑆𝑌𝑌𝑆𝑆 =𝑣𝑣𝑝𝑝𝑣𝑣([𝑋𝑋 1 ,𝑋𝑋 2 , … ,𝑋𝑋 𝑀𝑀 ]) is the system transmit signal vector that 𝑋𝑋 𝑘𝑘 is the transmit signal vector of the k-th cell.

- 𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 is the circularly symmetric complex additive Gaussian noise vector at the Base station, 𝐸𝐸[𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 ] = 0 and 𝐸𝐸[𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 𝑠𝑠 𝐵𝐵𝑠𝑠𝐵𝐵 𝐻𝐻 ] =𝑁𝑁 0 I

The random channel matrix is given by

Where 𝐻𝐻𝑖𝑖→𝑖𝑖 is the channel matrix between i-th cell and the j-th base station, i=1 M, j=1…M

Therefore, we have the received signal at the m-th base station as below:

𝐻𝐻 𝑚𝑚→𝑠𝑠 = [𝐻𝐻 𝑚𝑚→𝑠𝑠,1 ,𝐻𝐻 𝑚𝑚→𝑠𝑠,2 , … ,𝐻𝐻 𝑚𝑚→𝑠𝑠,𝑘𝑘 ] 𝑇𝑇 , where 𝐻𝐻 𝑚𝑚→𝑠𝑠,𝑘𝑘 is the channel between the k-th user of m-th cell to n-th base station

5, to demodulate received signals and best estimate what was sent

Where 𝜎𝜎 is the standard deviation of system noise

In the uplink channel, assume the transmit power of the k-th user is P k , and the power for all users is limited by the maximum uplink transmit power: 𝑃𝑃 𝑘𝑘 ≤

𝑃𝑃 𝑚𝑚𝑎𝑎𝑚𝑚 ,𝑈𝑈𝑈𝑈 ,𝑘𝑘= 1,2, … ,𝐾𝐾 Users transmit independently so the transmit covariance P can be written as a diagonal matrix

Figure 18 Network-MIMO downlink channel: from all base station to k-th user in the m-th cell

The received signal vector of the system can be written here:

- 𝑌𝑌𝐵𝐵𝑠𝑠𝐵𝐵=𝑣𝑣𝑝𝑝𝑣𝑣([𝑌𝑌 1 ,𝑌𝑌 2 , … ,𝑌𝑌 𝑀𝑀 ]) is the system receive signal vector that 𝑠𝑠 𝑚𝑚 is the receive signal vector at the m-th cell.

- 𝑋𝑋 𝑆𝑆𝑌𝑌𝑆𝑆 =𝑣𝑣𝑝𝑝𝑣𝑣([𝑋𝑋 1 ,𝑋𝑋 2 , … ,𝑋𝑋 𝑀𝑀 ]) is the system transmit signal vector that 𝑚𝑚 𝑚𝑚 is the transmit signal vector at m-th cell.

- 𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 is the circularly symmetric complex additive Gaussian noise vector at the user’s receiver, 𝐸𝐸[𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 ] = 0 and 𝐸𝐸[𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 𝑠𝑠 𝐵𝐵𝑠𝑠𝐵𝐵 𝐻𝐻 ] =𝑁𝑁 0 I

In the downlink case, the random channel matrix 𝐻𝐻 𝑆𝑆𝑌𝑌𝑆𝑆 is given by

By denoting 𝑌𝑌 𝑚𝑚 =𝑣𝑣𝑝𝑝𝑣𝑣𝑡𝑡𝑙𝑙𝑝𝑝(𝑠𝑠 𝑚𝑚 ,1 ,𝑠𝑠 𝑚𝑚 ,2 , … ,𝑠𝑠 𝑚𝑚 ,𝐾𝐾 ) is the receive signal vector at the m-th cell The received signal of k-th user at the m-th cell is:

- ℎ 𝑚𝑚 ,𝑘𝑘 is the channel between the m-th BS and the k-th user, 𝐻𝐻 𝑚𝑚 [𝐻𝐻 𝑚𝑚 𝑇𝑇 ,1 ,𝐻𝐻 𝑚𝑚 𝑇𝑇 ,2 , … ,𝐻𝐻 𝑚𝑚 𝑇𝑇 ,𝑘𝑘 ] 𝑇𝑇

- 𝐻𝐻 𝑠𝑠→𝑚𝑚,𝑘𝑘 is the channel matrix between the n-th BS and the k-th user in the m-th cell, 𝐻𝐻 𝑠𝑠→𝑚𝑚 = [ 𝐻𝐻 𝑠𝑠→𝑚𝑚 𝑇𝑇 ,1 , 𝐻𝐻 𝑠𝑠→𝑚𝑚 𝑇𝑇 ,2 , … ,𝐻𝐻 𝑠𝑠→𝑚𝑚 𝑇𝑇 ,𝐾𝐾 ] 𝑇𝑇

- 𝑠𝑠 𝑚𝑚 ,𝑘𝑘 is the noise vector of the k-th user in the m-th cell

Assume that the transmit power of the k-th antenna is P k , and the power for each antenna is limited by the maximum downlink transmit power: 𝑃𝑃 𝑘𝑘 ≤ 𝑃𝑃 𝑚𝑚𝑎𝑎𝑚𝑚 ,𝐷𝐷𝑈𝑈 , 𝑘𝑘 1,2, … ,𝑀𝑀

Each channel coefficient ℎ 𝑚𝑚 ,𝑘𝑘 captures the effects of fading, shadowing, and path- loss over time-frequency symbol n between the m-th BS and k-th user Specifically,

In other words, if there is no fading 𝑝𝑝 𝑚𝑚,𝑘𝑘 =𝑆𝑆 𝑚𝑚,𝑘𝑘 = 1 and 𝑑𝑑 𝑚𝑚,𝑘𝑘=𝑑𝑑 𝑝𝑝𝑝𝑝𝑟𝑟 , then ℎ 𝑚𝑚,𝑘𝑘 =𝜇𝜇

We refer to the parameter 𝜇𝜇 as the reference signal-to-noise ratio (SNR)

Let 𝐻𝐻� 𝑚𝑚 denote the estimation MIMO channel in the m-th cell Under the zero-forcing criterion, the transmitted signal vector in Eqt 3-9 is given by

𝑋𝑋 𝑚𝑚 =𝐻𝐻� 𝑚𝑚 ��𝐻𝐻� 𝑚𝑚 � 𝐻𝐻 𝐻𝐻� 𝑚𝑚 � −1 ) 𝑈𝑈 𝑚𝑚 [3-11] Where 𝑈𝑈 𝑚𝑚 is the data symbol vector for the K users in the m-th cell

Therefore, if the channel estimation is ideal (𝐻𝐻� 𝑚𝑚 =𝐻𝐻 𝑚𝑚 ), the received signal by each user is simply its desired data symbol plus additive noise:

SIMULATION AND RESULTS

Simulation Model

To compare the performance of conventional model and network-MIMO, we consider only two extreme cases:

In both cases, the ZF beamforming weights are computed as if the channel estimates were perfect at both end-user and access point’s side

Then the values of intermediate channel coefficients were estimated using a simple zero-order-hold approximation

The algorithm below presented the method to control transmit power of each user/AP

Step 1 Initialize all transmit powers to their maximum values

Step 2 Given the current power levels, compute the estimated SINR for each user This involves finding the SINR in each channel estimation tile resulting from a linear MMSE receiver, and averaging these SINR values over all tiles using an equivalent mutual information approach

Step 3 For each user, find the highest data rate that can be supported at the computed SINR while assuring the targeted packet error rate of 10%

Step 4 Lower each user’s power to just meet the SINR requirement for the selected data rate at the targeted packet error rate (computed as if all other users are maintaining their current power levels)

Step 5 Iterate until no user’s data rate changes between successive iterations.

Simulation Diagram

The simulation diagram is shown in Figure 19 There are some key functions of simulation:

Calculate Necessary User Transmit Power – This function will calculate the user transmit power based on the channel matrix apply the algorithm that is presented in section 4.1

Modulate message and Transmit – modulate message based on OFDM transmission method and transmit them after that

Channel Estimation- Estimate the channel state information at receiver (CSIR) by using sounding pilot method The channel information is then using for decode the received message

Decode received message and calculate BER – received message is decoded

The result is used to calculate the Bit Error Rates (BER) of the simulation system.

Simulation Results

Using Monte-Carlo method to simulated the Network-MIMO and nonNetwork- MIMO wireless access system The simulation scenario is detailed in Table 2 below

Subcarrier spacing in frequency 10kHz

Amplitude gain on pilot symbols relative to other information carrying symbols

Variance of lognormal shadow fading constant 3 dB

Configure of antenna system Tx=1; Rx=1

Length of random message from each user Ld*2*20 Square area in meters of a cell 36 m 2

The simulation environment is shown in Fig 20 with 9 access point and 9 End-user

Figure 20 Simulation environment with 9 cell, each cell include 1 access point and 1 end-user with randomly place

Figure 21 OFDM Pilot symbol to estimate the channel state information at both transmitter (AP/user) and receiver (user/AP) side with 3 users

By using sounding pilot symbol, we can estimate the channel between APs and End-user Fig 22 presented an example of channel estimation by using pilot

Figure 22 Compare between real channel and the estimated channel by using pilot symbol

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)

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

CONCLUSION

[1] S Dekleva, J Shim, U Varshney, and G Knoerzer, “Evolution and emerging issues in mobile wireless networks,” Communications of the ACM, vol

[2] H R Anderson, Fixed Broadband Wireless System Design Chichester, West Sussex, England: John Wiley & Sons Ltd, 2003

[3] T K Sarkar, R J Mailloux, A A Oliner, M Salazar-Palma, and D L.Sengupta, History of Wireless Wiley Interscience, John Wiley & Sons, 2006

[4] K Kyung-Ho, “Key technologies for the next generation wireless communications,” Proc of the 4th Intern Conf Hardware/software codesign and system synthesis, 2006 CODES+ISSS ’06., pp 266–269, Oct 2006

[5] G J Foschini “Layered space-time architecture for wireless communication in a fading environment when using multiple antennas” Bell Labs

[6] D Tse and P Viswanath, Fundamentals of Wireless Communication Cambridge University Press, 2005

[7] D Gesbert, M Kountouris, R W Heath, Jr., C.-B Chae, and T Salzer,

”Shifting the MIMO Paradigm: From Single User to Multiuser Communications”,

IEEE Signal Processing Magazine, vol 24, no 5, pp 36-46, Oct., 2007

[8] S Venkatesan, A Lozano, R A Valenzuela, "Network MIMO: Overcoming Inter-cell Interference in Indoor Wireless Systems", Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, November 2007

Tiêu đề	Performance Analysis of Network MIMO Systems
Tác giả	Duc-Tuyen Ta
Người hướng dẫn	Dr. Trinh Anh Vu
Trường học	Viet Nam National University, Hanoi University of Engineering and Technology
Chuyên ngành	Electrical Engineering
Thể loại	Thesis
Năm xuất bản	2010
Thành phố	Hanoi

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