Massive multiple-input multiple-output (M-MIMO) is one of the cutting edge technologies that provides significant improvement in throughput, coverage and spectral efficiency. The challenge with M-MIMO systems is to ex...
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Massive multiple-input multiple-output (M-MIMO) is one of the cutting edge technologies that provides significant improvement in throughput, coverage and spectral efficiency. The challenge with M-MIMO systems is to extract individual signals from the composite signal, thus making optimal detectors prohibitively complex. Recently, approximate message passing (AMP) and its variant detectors have gained substantial importance due to their decreased complexity and improved performance. However, AMP algorithm does not always converge. Non-linear detectors like vertical Bell-Labs layered space-iime improve the bit error rate (BER) but with high complexity, whereas, linear minimum mean square error (MMSE) detector offers low complexity while compromising on BER performance. Neumann series based MMSE detectors further reduce MMSE computationalcomplexity, however, the BER performance remains the same. In this work, the authors propose a hybrid Neumann series based MMSE detector which decomposes the detected signal into its constituent components and apply a neighbourhood selection algorithm on the obtained components thus improving the overall performance. Another contribution of this work is derivation of an off-set value for optimised neighbourhood set selection that enables more accurate detection while further reducing computational complexity. Simulation results confirm that the proposed scheme outperforms aforementioned algorithms in terms of BER performance and computationalcomplexity in a continuously changing Rayleigh channel.
Monte-Carlo GO is a computer GO program that is sufficiently competent without the knowledge expressions of IGO . Although it is computationally intensive, the computationalcomplexity can be reduced by properly pruni...
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Monte-Carlo GO is a computer GO program that is sufficiently competent without the knowledge expressions of IGO . Although it is computationally intensive, the computationalcomplexity can be reduced by properly pruning the IGO game tree. In this study, we achieved this by using a potential model based on the knowledge expressions of IGO . The potential model treats GO stones as potentials. A specific potential distribution on the GO board results from a unique arrangement of stones on the board. Pruning with the potential model categorizes legal moves into effective and ineffective moves in accordance with the potential threshold. In this study, certain pruning strategies based on potentials and potential gradients were experimentally evaluated. In particular, for different-sized boards, the effects of pruning strategies were evaluated in terms of their robustness. We successfully demonstrated pruning with a potential model to reduce the computationalcomplexity of the game of GO as well as the robustness of this effect across different-sized boards.
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