Low Complexity, Pairwise Layered Tabu Search for Large Scale MIMO Detection

This paper presents a low complexity pairwise layered tabu search based detection algorithm for large-scale multiple-input multiple-output system. The proposed can compute two layers simultaneously and reduce the effective number of searches. An efficient Gram matrix matched filtered output update strategy is developed to reuse computations from past visited layers. Also, precomputation technique adapted redundancy in computation within iterations. Complexity analysis shows that upper bound initialization reduces $$O(N_t^4)$$ $$O(N_t^3)$$ . performance detector almost same as conventional complex version LTS 64QAM 16QAM modulations. However, outperforms system 4QAM modulation, especially $$16 \times 16$$ $$8 8$$ MIMO. Simulation results show percent reduction method approximately 75% $$64 64$$ , 85% systems achieve BER $$10^{-3}$$ Moreover, we have three layer-wise iteration allocation strategies further with minor degradation performance.