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Conference Papers Year : 2015

Low-complexity detector for very large and massive MIMO transmission


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 discrete, 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.
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hal-01203097 , version 1 (22-09-2015)



Yasser Fadlallah, Abdeldjalil Aissa El Bey, Karine Amis Cavalec, Dominique Pastor. Low-complexity detector for very large and massive MIMO transmission. SPAWC 2015 : 16th IEEE International Workshop on Signal Processing Advances in Wireless Communications, Jun 2015, Stockholm, Sweden. pp.251 - 255, ⟨10.1109/SPAWC.2015.7227038⟩. ⟨hal-01203097⟩
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