channel estimation and performance … · where c is the channel capacity and b is the channel...

International Journal of Research and Reviews in Applied Sciences ISSN: 2076-734X, EISSN: 2076-7366 Volume 1, Issue 2(November 2009)

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CHANNEL ESTIMATION AND PERFORMANCE ENHANCEMENT OF HYBRID TECHNOLOGY FOR NEXT

GENERATION COMMUNICATION SYSTEM

1NIRMALENDU BIKAS SINHA, 1M. NANDY, 1K.K.KAPRI, 2R.BERA and 3M.MITRA

1College Of Engineering & Management, Kolaghat, K.T.P.P Township,PurbaMedinipur, W. B, India. 2 Sikkim Manipal University, Majitar, Rangpo, East Sikkim, 737132, India.

3 Bengal Engineering and Science University, Shibpur, Howrah, W.B, India.

ABSTRACT

A large demand of frequency allocation has fuelled the Multi-carrier system, resulting in a crowded spectrum as well as simultaneous access being required by a large number of users. Existing wireless systems may utilize single frequency, single antenna and pulse for carrier transmission and reception. Problems of such system is that in case of failure the total system will become non operational. A distributed system in terms of multi-carrier, multi-antenna and coded pulse can provide a more suitable communication this give rise to OFDM-MIMO /hybrid technology as the ultimate solution. This technology is a promising technique for high-data-rate broadband wireless communications and radar because it can reduce interference, multipath effect, jamming and higher target resolution as compared to conventional communication & radar etc. This paper focuses on the MIMO channel estimation under channel known and channel unknown condition along with the performance improvement of hybrid system. The results reveal some interesting effects of spatial correlation, multipath and number of antennas on MIMO channel capacity.The hybrid approach has essentially been developed for 60GHz frequency provided we have the necessary bandwidth. The performance analysis for the MIMO-OFDM system has been carried out based on MATLAB simulation. The experimental results have been verified using the simulation, the results of simulation have been verified with the various works being carried out in this area and the results conferred to be correct. KEYWORDS: MIMO, STBC, CCI, CSI, ZF , MMSE.

1. INTRODUCTION: Communication systems using multiple antennas both at the transmitter and receiver have recently received increased attention due to their ability to provide great capacity in wireless fading environment. MIMO architectures can be used for combined transmit and receive diversity, as well as parallel transmission of data or spatial multiplexing. When used for spatial multiplexing MIMO technology promises high bit rate in a narrow bandwidth and as such it is of high significance for spectrum users. One of the key limitations in MIMO is co-channel interference (CCI) which can also significantly decrease the capacity of wireless and personal communications systems. For communication systems with unknown channel state information (CSI) at both ends, conventional receivers have a two phase structure followed by coherent detection by treating estimated channel as the actual channel coefficients. There is a rising need for high data rate in wireless communication as user’s demands exceed the capacity of wireless networks. A tremendous technological growth towards exploiting the bandwidth of a system is under concern. Theoretical capacity of these multiple-input multiple-output (MIMO) systems was shown linearly with the smaller of number transmit and receive antennas in rich scattering environments, and at sufficiently high signal-to-noise (SNR) ratios [1]. MIMO channel capacity is heavily dependent on statistical properties and antenna element. Thus the potential advantages of the MIMO system can be guaranteed and the MIMO system will work in the best possible way.


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All wireless technologies face the challenges of signal fading, multipath, increasing interference and limited spectrum. MIMO technology has recently been demonstrated to have the potential of achieving extraordinary data rates and simultaneous increase in range and reliability without consuming extra radio frequency. It solves speed and range of the toughest problems faced by any wireless communication today. However the paper “MIMO principles” assumed frequency flat fading MIMO channels. For broadband communications, OFDM turns a frequency selective channel into a set of parallel flat channels, which significantly reduces the receiver complexity and capability to mitigate multipath. In OFDM the high speed data stream is divided in Nc narrow band data streams, Nc corresponding to the subcarriers or sub channels i.e one OFDM symbols consists of N symbols modulated for example by example by QAM or PSK. As a result the symbol duration is N times longer than in a single carrier system with the same symbol rate. The symbol duration is made even longer by adding a cyclic prefix to each symbol. As long as the cyclic prefix is longer than the channel delay spread OFDM offers inter-symbol interference (ISI) free transmission. The combination MIMO-OFDM is very natural and beneficial since OFDM enables support of more antennas and larger bandwidths since it simplifies equalization dramatically in MIMO systems. This report shows that OFDM applied to MIMO systems with V-BLAST signal processing at the receiver is a practical solution for Co -Channel Interference (CCI) between transmitted and received sub streams of single –frame TDMA data. Recent technical literature has been reviewed to present some of the basic characteristics of MIMO-OFDM systems analyzed in [2] that makes them attractive for high data- rate transmission over wireless channels, and the problems with the CCI associated to multi-user operation [3,4] which can reduce their performance. we present how the V- blast algorithm [5,6] implements a nonlinear detection technique based on spatial nulling process (ZF or MMSE approach to separate the transmitted independent sub streams) combined with symbols cancellation to reduce CCI and to improve performance. we then discuss briefly the MIMO-OFDM system examined[2] that implements V-BLAST CCI for multi-user operation to increase system capacity by increasing the sub stream data. The analysis and simulation of the MIMO-OFDM V-Blast system for CCI cancellation compares the performance of the system for different antenna configurations and correlation factors between the MIMO channel components.

2. THE MIMO CHANNEL

MIMO systems are an extension of smart antennas systems. Traditional smart antenna systems employ multiple antennas at the receiver, whereas in a general MIMO system multiple antennas are employed both at the transmitter and the receiver. The addition of multiple antennas at the transmitter combined with advanced signal processing algorithms at the transmitter and the receiver yields significant advantage over traditional smart antenna systems - both in terms of capacity and diversity advantage. It can be represented by the Shannon’s equation

………(i) Where C is the channel capacity and B is the channel Bandwidth. From the expression it is clear that theoretically capacity increase as the bandwidth is increased. The graph in Figure1. indicates the increase in capacity as the bandwidth increases.

( )2log 1= +C B SNR


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0 10 20 30 40 50 600

50

100

150

200

250

300

350

400

SNR(dB)

CA

PA

CIT

Y (M

bps)

(SHANONS CAPACITY)

B=20MHzB=40MHz

Fig 1: Capacity with respect to Bandwidth

We consider a MIMO system with a transmit array of M antennas and a receive array of N antennas. The block diagram of such a system is shown in figure 1.The transmitted matrix is an M * 1 column matrix S where S i is the ith component, transmitted from antenna i, and of the form: S =[S1,S2,.....SM]T, 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 be independent identically distributed (i.i.d) variables. If the channel is unknown at the transmitter, we assume that the signals transmitted from each antenna have equal powers of Es/Mt. We assume that he channel matrix is known at the receiver but unknown at the transmitter. The channel matrix can be given by:

h11 h1Nt H = h11 h2Nt hR1 hNrNt

The noise at the receiver is another column matrix of size N * 1, denoted by w: W = [W1, W2... .......WN]T The receiver vector is N * 1 vector which satisfied: R [m] = H.S[m] + W [m], where m is a real number from 1 to N.

3. MIMO SYSTEM CHANNEL CAPACITY

3.1. Capacity of Single-Input-Single-Output (SISO) System 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:

. 1 ⁄ ……..(ii) Where: C is the Shannon limits on channel capacity SNR is signal-to-noise ratio, B is bandwidth of channel.


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In the practical case of time-varying and randomly fading wireless channel, the capacity can be written as: . 1 . | | ⁄ ……..(iii)

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 [1]. 3.2. Capacity of Single-Input-Multiple-Output (SIMO) System For the SIMO system, we have M antennas at receiver and only one at transmitter. If the signals received on these antennas have on average the same amplitude, then they can be added coherently to produce an M2 increase in the signal power. On the other hand, there are N sets of noise that are added incoherently and result in an N-fold increase in the noise power. Hence, there is an overall increase in the SNR:

.

..

So the capacity of SIMO channel is: . 1 . ⁄ ………(iv)

The capacity of SIMO system in the practical case of time-varying and randomly fading wireless channel

[4] is: . 1 . ⁄ ……..(v) Where H is 1xM channel vector and ( )* is the transpose conjugate. 3.3. Capacity of Multiple-Input-Single-Output (MISO) System For the SIMO system, we have N antennas at transmitter and only one at receiver. As same as the case of the SIMO system, we have capacity of MISO system

. 1 . ⁄ ………..(vi) In the practical case of time-varying and randomly fading wireless channel, it shown that the capacity of N x 1 MIMO system is [3, 11]:

. 1 . ⁄ ……(vii) Compared with SISO system, the capacity of SIMO and MISO system shows improvement. The increase in capacity is due to the spatial diversity which reduces fading and SNR improvement. However, the SNR improvement is limited, since the SNR is increasing inside the log function [1] 3.4. Capacity of Multiple-Input-Multiple-Output (MIMO) System For the SIMO system, we have M antennas at transmitter and N antennas at receiver. We analyze the capacity of MIMO channel in two cases: 3.4.1 Same signal transmitted by each antenna In this case, the MIMO system can be view in effect as a combination of the SIMO and MISO channels. As same as the case in 3.2 and 3.3, we have:

. . .

. .

So the capacity of MIMO channels in this case is:

. 1 . . ⁄ ………..(viii) Thus, we can see that the channel capacity for the MIMO systems is higher than that of SIMO and MIMO system. But in this case, 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. 3.4.2 Different signal transmitted by each antenna


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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 is like we are creating a channel for each one of the transmitters.

The capacity of each one of these channels is roughly equal to: . 1 . ⁄ .................(ix)

But we have M of these channels, so the total capacity of the system is: . . 1 . ⁄ ....................(x)

The plot for total capacity for different MIMO channel unknown as shown in Fig.2

Fig.2: MIMO capacity for unknown channel.

Roughly ,With N ≥ M, the capacity of MIMO channels is equal to:

. . 1 ⁄ …………..(xi)

Thus, we can get linear increase in capacity of the MIMO channels with respect to the number of transmitting antennas. So, 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 system is [9,10]: Channel known

. . ⁄ ………(xii)

The plot for MIMO capacity under known channel is as shown in figure3.

0 10 20 30 40 50 600

20

40

60

80

100

120

SNR(dB)

capa

city

(bps

/Hz)

MIMO Capacity

MIMO, NT=2 NR=2MIMO, NT=3 NR=4MIMO, NT=6 NR=8MIMO,NT=10 NR=12


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Fig.3: MIMO capacity for known channel

We can see that the advantage of MIMO systems is significant in capacity. As an example, for a system which Therefore, the capacity increases linearly with the number of transmit antennas .

. . 1 ⁄ ……………(xiii) MIMO is best when SNR and angular spread are large but for Small angular spread or presence of a dominant path (e.g. LOS) reduce MIMO performance.

4. EXPERIMENTAL SETUP FOR OFDM-MIMO SYSTEMS: The experiment is simulated at the laboratory as shown in Figure 4. The data stream b[n, k] is first encoded by a space-time or space-frequency encoder. Then, the coded data is divided into MT sub streams with each sub stream forming an OFDM block transmitted through one transmit antenna. At the receiver, the received signals at multiple receive antennas are decoded using channel state information obtained through a training-based symbol (resulting in coherent detection of the transmitted symbol). Therefore, channel state information in terms of channel impulse response (CIR) or channel frequency response (CFR) is critical to achieve the advantages (diversity gains and the expected increase in data rate) of a MIMO-OFDM system.

Fig.4: A broadband MIMO-OFDM/HYBRID System

0 10 20 30 40 50 600

50

100

150

200

250

300

SNR(dB)

CA

PA

CIT

Y(b

ps/H

z)

MIMO Capacity(channel known)

MIMO, NT=2 NR=2MIMO, NT=3 NR=4MIMO, NT=6 NR=8MIMO,NT=10 NR=12

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Fig. 10. Comparison between ZF V-BLAST (OSIC) and QR algorithm

6. V-BLAST AND STBC COMBINING SYSTEM'S PERFORMANCE ANALYSIS The model of V-BLAST has high multiplexing gain and larger channel capacity; On the contrary, the model of STBC (space time block code) has high diversity gain and BER (better bit-error rate) performance. It is obviously that the BER will be smaller while the SNR (signal-noise ratio) is larger. The specific chart of SNR and BER of different models is shown in Fig. 11. As can be seen from the Fig., STBC model has the best bit error rate performance because of obtaining the full diversity gain, and the performance of composite model proposed in this paper is greatly improved than that of the V-BLAST model, and the BER has been reduced to below than 10- 4 when the SNR is 10 dB.


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Fig. 11, The BER Comparison of three model

7. Discussion The performance of the proposed OFDM-MIMO system for different antenna configurations and propagation conditions was analyzed based on our Lab model. It has found that the V-BLAST can get potentially higher spectral efficiency because no orthogonal transmitted signals and received co-channel signals are separated by decorrelation (processing algorithm) due to multipath and capable of improving bit rate without increasing total transmit power or required bandwidth with V-BLAST processing at the receiver as an efficient CCI cancellation technique. Acknowledgement Authors would like to thank to the contribution of Manish Sonal and Sudipta Ghosh, of Electronics and Communication Engineering Department, C.E.M.K for their continuous hard work and sincere support in completing this project.


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8. REFRENCES:

[1] G. J. Foschini and M. J. Gans, “On limits of wireless communications in a fading environment when using multiple antennas”, Wireless Personal Communications, Vol. 6, No. 3, March 1998, pp 311-335. [2] Heiskala, J. and J. Terry, OFDM Wireless LANs : A Theoretical and Practical Guide, Indianapolis, IN : Sams Publishing, 2002. [3] Mody, A.N. and G.L. Stuber, ‘Synchronization for MIMO-OFDM,’ IEEE Global Communication Confarence, San Antonio, Texas, November 2001. [4] OFDM and MC-CDMA by L. Hanzo et.al, IEEE Press, 2003. [5] Jeffrey H .Reed , Software Radio- A modern approach to radio engineering , Chapter 6 on smart antenna , published by Pearson Education, 2006. [6] M. Jankiraman, Space –time Codes and MIMO systems, published by Artech House, 2004. [7] B. Vucetic & J. Yuan, ‘Space Time Coding’, published by John Wiley &Sons Inc., 2003. [8] Mody, A.N. and G.L. Stuber, ‘Synchronization for MIMO-OFDM,’ IEEE Vehicular Technology Conference, Taiwan, 2002. [9] W. Stallings, “Wireless communication and networks”, Pearson Education Asia publication, 2002. [10] P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela, “V-BLAST: An architecture for realizing very high date rates over the rich-scattering wireless channel,” Proc. URSI ISSSE, pp. 295–300, 1998. [11] M.O. Damen, K. Abed-Meraim, and M. S. Lemdani, “Iterative QR detection for BLAST”, in Wireless Personal Communications, Massachusetts, Kluwer Academic Publishers, 2001. [12] B. Hassibi, “An efficient Square-Root Algorithm for BLAST”, in http://mars.bell-labs.com, Bell Labs, January 2000. [13] L. Giangaspero et al., “Co-Channel Interference cancellation based on MIMO OFDM systems”, IEEE Wireless Communications, vol. 9, no. 6, pp. 8-17, December 2002. [14] J. Li, K. B. Letaief, and Z. Cao, “Co -Channel Interference Cancellation for Space-Time coded OFDM systems”, IEEE Trans. Wireless Communications, vol. 2, no. 1, pp. 41-49, January 2003. [15] Y. Li, J. H. Winter, and N. R. Sollenberger, “MIMO-OFDM for Wireless Communications: Signal detection with enhanced channel estimation”, IEEE Trans. Communications, vol. 50, no. 9, pp. 1471-1477, September 2002. [16] K. Ng, R. Cheng, and R. Murch, “A simplified bit allocation for VBLAST based OFDM MIMO systems in frequency selective fading channels,” Proc. International Conference on Communications 2002, vol. 1, pp. 411-415, May 2002. [17] HAN Shuangfeng, ZHOU Shidong, WANG Jing, PARK Kyung, “Combined STBC and V-BLAST schemes in distributed wireless communication systems”, Journal of Tsinghua University (Science and Technology), 2007, Vol47(1), pp: 36-39.


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BIOGRAPHY: Prof. Nirmalendu Bikas Sinha received the B.Sc (Honours in Physics), B. Tech, M. Tech degrees in Radio-Physics and Electronics from Calcutta University, Calcutta,India,in1996,1999 and 2001, respectively. He is currently working towards the Ph.D degree in Electronics and Telecommunication Engineering at BESU. Since 2003, he has been associated with the College of Engineering and Management, Kolaghat. West Bengal, India where he is currently an Asst.Professor is with the department of Electronics

& Communication Engineering & Electronics & Instrumentation Engineering. His current research Interests are in the area of signal processing for high-speed digital communications, signal detection, MIMO, multiuser communications,Microwave /Millimeter wave based Broadband Wireless Mobile Communication ,semiconductor Devices, Remote Sensing, Digital Radar, RCS Imaging, and Wireless 4G communication. He has published large number of papers in different national and international Conference and journals. He is currently serving as a reviewer for Wireless communication and RADAR system in different international journals.

Maitree Nandy is pursuing B.Tech in the Department of Electronics & Communication Engineering at College of Engineering and Management, Kolaghat, under WBUT in 2010, West Bengal, India. Her areas of interest are in Microwave /Millimeter wave based Broadband Wireless Mobile Communication and digital electronics. Kundan Kumar Kapri is pursuing B.Tech in the Department of Electronics & Communication Engineering at College of Engineering and Management, Kolaghat, under WBUT in 2010, West Bengal, India. His areas of interest are in Microwave /Millimeter wave based Broadband Wireless Mobile Communication, digital electronics and Digital signal processing..

Dr. Rabindranath Bera is a professor and Dean (R&D), HOD in Sikkim Manipal University and Ex-reader of Calcutta University, India. B.Tech, M.Tech and Ph.D.degrees from Institute of Radio-Physics and Elecitroncs, Calcutta University. Field of Interests are in the area of Digital Radar, RCS Imaging, Wireless 4G Communication, Radiometric remote sensing. He has published large number of papers in different national and international Conference and journals.

Dr. Monojit Mitra is an Assistant Professor in the Department of Electronics & Telecommunication Engineering of Bengal Engineering & Science University, Shibpur. He obtained his B.Tech, M.Tech & Ph. D .degrees from Calcutta University. His research areas are in the field of Microwave & Microelectronics, especially in the fabrication of high frequency solid state devices like IMPATT. He has published large number of papers in different national and international journals. He has handled sponsored research projects of DOE and DRDO. He is a member of IETE (I) and Institution of Engineers (I).

channel estimation and performance … · where c is the channel capacity and b is the channel...

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