A Low-Complexity Detection Algorithm for Uplink Massive MIMO Systems Based on Alternating Minimization

In this letter, we propose an algorithm based on the alternating minimization technique to solve the uplink massive multiple-input multiple-output (MIMO) detection problem. The proposed algorithm is specifically designed to avoid any matrix inversion and any computations of the Gram matrix at the receiver. The algorithm provides a lower complexity compared to the conventional minimum mean square error detection technique, especially when the total number of user equipment antennas (across all users) is close to the number of base station antennas. The idea is that the algorithm re-formulates the maximum-likelihood detection problem as a sum of convex functions based on decomposing the received vector into multiple vectors. Each vector represents the contribution of one of the transmitted symbols in the received vector. Alternating minimization is used to solve the new formulated problem in an iterative manner with a closed-form solution update in every iteration. Simulation results demonstrate the efficacy of the proposed algorithm in the uplink massive MIMO setting for both coded and uncoded cases.

Elgabli Anis, Elghariani Ali, Aggarwal Vaneet, Bell Mark R.

Publication type:
A1 Journal article – refereed

Place of publication:

alternating minimization, MIMO, non convex optimization, signal detection


Full citation:
A. Elgabli, A. Elghariani, V. Aggarwal and M. R. Bell, “A Low-Complexity Detection Algorithm for Uplink Massive MIMO Systems Based on Alternating Minimization,” in IEEE Wireless Communications Letters, vol. 8, no. 3, pp. 917-920, June 2019, https://doi.org/10.1109/LWC.2019.2899852


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