Improved sparse error recovery approach for detecting QAM signals in overloaded massive MIMO systems

Yacine Meslem (1) , Abdeldjalil Aissa El Bey (2, 3) , Mustapha Djeddou (1)

1 EMP - École Militaire Polytechnique [Alger]
2 Lab-STICC_IMTA_CACS_COM
3 IMT Atlantique - SC - Département Signal et Communications

With a convenient concatenation of a convex relaxation-based detector and a simple greedy algorithm, we propose an improved Post Detection Sparse error Recovery (PDSR) approach for massive Multiple Input Multiple Output (m-MIMO) systems that, in particular, transmit QAM signals. The proposed PDSR approach can perform well in situations, where the classical one, either acts poorly or completely fails. We further propose an Alternating Direction Method of Multipliers (ADMM)-based solver for the convex detector, which is advantageous in maintaining an affordable complexity to the overall proposed detection scheme. Numerical experiments show the efficiency of our approach, especially when applied to overloaded m-MIMO systems.

Mots clés: Massive MIMO (m-MIMO); Signal detection; Convex optimization; ADMM; Greedy algorithms; Compressive sensing

Domaines: Traitement du signal et de l'image [eess.SP]

Yacine Meslem, Abdeldjalil Aissa El Bey, Mustapha Djeddou. Improved sparse error recovery approach for detecting QAM signals in overloaded massive MIMO systems. IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Jun 2020, Hangzhou, China. ⟨10.1109/SAM48682.2020.9104392⟩. ⟨hal-02861810⟩