International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
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A Sphere Decoding Algorithm for MIMO Jay D Thakar Electronics and Communication Dr. S & S.S Gandhy Government Engg College Surat, INDIA ---------------------------------------------------------------------***-------------------------------------------------------------------
Abstract - Modern wireless communication system
like Zero-Forcing (ZF). There are various decoding techniques used in a MIMO system and these are discussed here. In linear detectors, a linear transform is applied to the outputs of conventional matched filters to produce a new set of outputs, which may generate better results[5]. These include the decorrelator and the minimum meansquare error (MMSE) detector. Maximum-likelihood sequence detection (MLSD) is known to have perfect performance on an additive white Gaussian noise (AWGN) channel.
demanding high data rate operating in bandwidth deficient world is using Multiple Input Multiple Output (MIMO) antenna arrangement. MIMO transmission has become a popular technique to increase spectral efficiency. MIMO supports greater data rate and higher reliability in wireless communication. The receivers employing linear receivers or decision feedback detectors in detection use less hardware are of suboptimal. Optimal detectors are realized by Maximum Likelihood detector can achieve superb performance, yet the computational complexity is enormously high. Therefore suboptimal detectors such as Viterbi Decoding, Sphere Decoding, Genetic Algorithm based Decoder are reach the performance of ML detectors, and potentially a great deal of computational cost can be saved. In this paper, a practical sphere-Decoding algorithm is proposed. It utilizes a simple and effective way to set the initial radius which plays a decisive role in determining the computational complexity. The complexity and SER rate of the sphere decoder is good when compare with other decoders used in MIMO receiver design. The performance of sphere decoder and maximum likelihood decoder is same but the complexity is reduced in sphere decoder.
However, as the length of a channel increases, the number of states grows exponentially in Viterbi detector as, where L is the number of input level and v is the channel memory. A Viterbi decoder uses the Viterbi algorithm for decoding a bit stream that has been encoded using forward error correction based on a convolutional code. The Hamming distance is used as a metric for hard decision Viterbi decoders. The squared Euclidean distance is used as a metric for soft decision decoders. Many signal detectors are used in MIMO system. In that Maximum likelihood detection is an optimal solution. The ML decoding is robust to channel estimation errors and is near optimal with respect to SER. The solution involves an exhaustive search through all possible transmitted signal vectors; this search has exponential complexity, which is undesirable in practical systems[4]. Other sub-optimal algorithms such as MF, ZF and MMSE are practically considered with some disadvantage. Hence, we go to sphere decoder to implement the decoding. Sphere decoder gives near ML detection performance and lowers the computational complexity by limiting the search to the closest lattice point to the received signal within a sphere radius. Sphere decoder can restrict the search by drawing a circle around the received signal just small enough to enclose one signal point and eliminate the search of all points outside the circle[8].
Key Words: Muliple-Input-Muliple-Output (MIMO) system, decoders, sphere decoder, maximum likelihood, complexity.
1.INTRODUCTION Wireless system engineers are encountering a number of challenges. These include the limited availability of radio frequency spectrum, Power, and a time varying wireless environment. In addition, there is an increasing demand for higher data rates, better quality of service, and higher network capacity. Over the past decade, Multiple-Input Multiple-Output (MIMO) systems have evolved as a most promising technology in these measures.
In this paper we discuss about detector which are used in MIMO receiver system and compared the performance of detector’s BER with SNR. Our simulation result show sphere decoder reduce complexity and gives good bit error rate. Section 2 describe the background information about detectors of MIMO system. Section 3 discuss about the new proposed method and finally simulation and results are discussed in Section 4.
Multiple-Input Multiple-Output (MIMO) wireless antenna systems have been recognized as a key technology for future wireless communications. To achieve the capacity of MIMO systems is to use spatial multiplexing where streams of independent data are transmitted from the transmitting antennas. These information streams are then separated at the receiver by means of appropriate processing techniques such as maximum likelihood (ML) which achieves optimal performance[7] or linear receivers
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