International Journal of Scientific Engineering and Technology (ISSN : 2277-1581) Volume No.2, Issue No.9, pp : 830-834 1 Sept. 2013 IJSET@2013 Page 830 Optimized Behaviour of MIMO System under Different Equalization Techniques and Modulation Schemes over Rayleigh and Rician Fading Channels P Suresh Kumar , P Ratna Bhaskar Dept. of ECE, SRK Institute of Technology, Vijayawada, Andhra Pradesh., India. sureshpachhala@gmail.com, ratnabhaskar.prb@gmail.com Abstract-Wireless communications using Multiple-Input Multiple-Output (MIMO) links has emerged as one of the most significant breakthroughs in modern communications because of the huge capacity and reliability gains promised even in worst fading environment. This paper presents the optimized behaviour of MIMO systems over Rayleigh and Rician Fading channel environments. MIMO transmission systems are investigated in terms of Bit Error Rate (BER) performance. BER performance of MIMO systems is simulated for different transmit-receiver combinations such as 2×2, 2×3 and 2×4 using BPSK and QPSK modulation schemes and various equalization techniques such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE) and Maximum Likelihood (ML). Results show that the BER performance of a MIMO system using BPSK modulation and ML equalizer over Rician fading channel is optimum compared to the choice of other modulation schemes, equalizers and fading channels. Keywords— MIMO, ZF, MMSE and ML equalizers, Multiple antennas, fading channels, BPSK, QPSK, BER. I. INTRODUCTION A mobile radio channel is characterized by a multipath fading environment. The signal is offered to the receiver contains not only Line Of Sight of radio wave, but also a large number of reflected waves that arrive at different times. Delayed signals are the result of reflections from terrain features such as trees, hills, mountains, vehicles or buildings. These reflected delayed waves interfere with direct waves and cause Inter Symbol Interference (ISI) which causes significant degradation of network performance. Multiple-Input Multiple- Output (MIMO) wireless antenna systems have been recognized as a key technology for future wireless communications because it offers significant increases in data throughput and link range without additional bandwidth or transmit power. One common approach to exploit the capacity of MIMO system is to employ spatial multiplexing where independent information streams are then separated at the receiver by means of appropriate signal processing techniques such as Maximum Likelihood (ML), Minimum-Mean-Square-Error (MMSE) and Zero-Forcing (ZF) detectors. The better detector that minimizes the bit error probability is the ML detector. But, the ML detector is practically difficult as it has computational complexity is exponential. The ZF detector and MMSE detector have lesser computational calculations as they require only a matrix operation to be carried out, e.g. pseudo-inverse. However error performance in case of both ZF and MMSE detector are greatly lower than the optimal ML detector. In previous work [4], [5], for a 2×2 and 4x4 MIMO system for different equalization techniques such as ZF, MMSE and ML has been analyzed and it showed that the MIMO receiver with ML detection has the least BER for a given SNR for 4x4 MIMO. In [6], the performance of MIMO system with ZF detectors over Rayleigh & Rice fading channels are studied and the degradation in the performance of MIMO systems under exponential correlation matrix is investigated and analyzed and it is found that the SNR degradation is related to the Rician factor K. In this paper we have simulated BER performance for different SNR by different equalizers like ZF, MMSE and ML. And also for 2×2, 2×3, 2×4 MIMO system and comparison of BER by different modulation techniques over Rayleigh and Rician fading channels. The aim of the study is to identify the MIMO technology that gives best bit error rate (BER) performance of different equalizers and transmit-receiver combinations of MIMO by BPSK and QPSK modulation techniques over Rayleigh and Rician fading channels using MAT LAB simulation. II. MIMO SYSTEM MODEL A. MIMO system model We consider a MIMO system as shown in Fig.1 with array of Nt transmit antennas and Nr receiving antennas. Figure 1. MIMO System The received signal denoted by y are represented by Nrx1 column matrix (2). The received signal yj in the jth antenna is