Low-complexity detector for very large and massive MIMO transmission

Yasser Fadlallah, Abdeldjalil Aissa-El-Bey, Karine Amis, Dominique Pastor
2015 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)  
Maximum-Likelihood (ML) joint detection has been proposed as an optimal strategy that detects simultaneously the transmitted signals. In very large multiple-input-multiple output (MIMO) systems, the ML detector becomes intractable due the computational cost that increases exponentially with the antenna dimensions. In this paper, we propose a relaxed ML detector based on an iterative decoding strategy that reduces the computational cost. We exploit the fact that the transmit constellation is
more » ... rete, and remodel the channel as a MIMO channel with sparse input belonging to the binary set {0, 1}. The sparsity property allows us to relax the ML problem as a quadratic minimization under linear and ℓ1-norm constraint. We then prove the equivalence of the relaxed problem to a convex optimization problem solvable in polynomial time. Simulation results illustrate the efficiency of the low-complexity proposed detector compared to other existing ones in very large and massive MIMO context.
doi:10.1109/spawc.2015.7227038 dblp:conf/spawc/FadlallahAAP15 fatcat:xvklckl775bdpmp4f2erqal3me