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Bit error rate aware accurate prediction of original signals with low distortion using low complexity detection algorithms
Author(s) -
Kasiselvanathan M.,
Kumar N. Sathish
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5192
Subject(s) - algorithm , computational complexity theory , pruning , computer science , heuristic , reduction (mathematics) , detection theory , signal (programming language) , distortion (music) , detector , mathematics , artificial intelligence , bandwidth (computing) , telecommunications , amplifier , computer network , geometry , agronomy , biology , programming language
Summary In the Massive MIMO system, complexity reduction acts as a significant role. This problem is focused in previous method whose foremost objective is to attain the low complexity. The main goal of this algorithm is to process the signal vector with more maximum likelihood cost instead of handling every signals vector, which might lead to more iteration. This is done by introducing different heuristic algorithms which attempts to find the signal vectors with more maximum likelihood values. The algorithms introduced in the existing work are QP detector and the Branch and Bound algorithm. The existing work still poses more complexity by taking more number of iterations for finding the more relative signal vector. These problems are resolved in the proposed system by introducing the novel framework, namely, pruning‐based maximum likelihood detection using low complexity detection algorithms (PRUN‐MLD‐LCDA). In the proposed system, complexity of the system is further reduced by introducing the cross correlation based pruning technique which attempts to find the similar signal, which is similar based on more signal components. The modified branch and bound algorithm is introduced to perform low complexity detection for the different QAM modulation techniques. The overall evaluation of the proposed research work is done in the Matlab simulation environment from which it is proved that the proposed research method leads to provide better result than the existing research methods.