mimo

The MLD (Maximum-likelihood decoding) of a particular random code over an AWGN (additive white Gaussian noise) channel requires an exhaustive search over all the possible code-word, and so the computational complexity of the optimal decoding scheme is exponential in the length of the code-word. A new type of the detection technique called the sphere decoding algorithm is proposed to lower the computational complexity. The principle of the sphere decoding algorithm is to search the closest lattice point to the received signal within a sphere radius, where every codeword is represented by a lattice point in a lattice field.
Index Terms - Fincke - Pohst (FP),Maximum Likelihood Decoder (MLD), Multiple Input Multiple Output (MIMO), Partial Euclidean Distance (PED), Sphere Decoder (SD), Schnorr-Euchner (SE).
NOMENCLATURE
In the paper, vectors are regarded as column vectors. These are written in lowercase bold characters. For matrices, uppercase bold characters are used. ‖.‖,.^T,.^HandE {.}denote the Euclidean norm, transpose, complex conjugate transpose and expected value, respectively.

I. INTRODUCTION
MIMO is the promising technology for next generation wireless communication system, as it obtains high throughput and diversity gain on rich scattering environment. As ML detection is optimal bit error rate detector for MIMO system which is infeasible due to the complexity of ML when a large number of antennas used together with high order modulation scheme. Therefore, SD is the lattice decoding algorithm, which is introduced for MIMO system to reduce the complexity of ML detection. It is a very tempting approach to reduce the implementation complexity of ML detector, significantly. This paper proposes Sphere Decoder that efficiently re...

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... result of the proposed schemes in terms of the bit-error rate by using MAT-Lab Programmed. Here, we have compared the result of different decoders for 4 X 4 MIMO systems and denote them by different colors. We find that the BER performance of our proposed Sphere Decoder is better than that of the conventional

Fig. 5: Comparison betweenDifferent Decoders
VII. CONCLUSION
The generalized sphere decoder was modified to overcome an impairment normally overlooked in the general equation for calculating the radius of the search sphere. In our proposed sphere decoder, larger number of overall iterations causes the decoder to be nearer to the optimal ML solution. It has been viewed here that proposed sphere decoder has performed better in terms of complexity and the performance as compared to the generalized sphere decoder and performs near optimal to the ML decoder.