New Iterative Detector of MIMO Transmission Using Sparse Decomposition

Abstract

International audienceThis paper addresses the problem of decoding in large scale MIMO systems. In this case, the optimal maximum likelihood detector becomes impractical due to an exponential increase of the complexity with the signal and the constellation dimensions. Our work introduces an iterative decoding strategy with a tolerable complexity order. We consider a MIMO system with finite constellation and model it as a system with sparse signal sources. We propose an ML relaxed detector that minimizes the euclidean distance with the received signal while preserving a constant l1-norm of the decoded signal. We also show that the detection problem is equivalent to a convex optimization problem which is solvable in polynomial time. Two applications are proposed, and simulation results illustrate the efficiency of the proposed detector

Low-complexity half-sparse decomposition-based detection for massive MIMO transmission

“…Following this approach, a sparse representation was proposed in [7] to define a successful separation method for a conditioned dimension system. Underdetermined noisy MIMO system with finite alphabet was dealt with as an application case of [7] in [1] and [8], where the problem was formulated as a basis pursuit denoising (BPDN) problem with relaxed constraints. In [8], the 0 -norm was relaxed into the 1 -norm to obtain a minimization problem which is solved thanks to an iterative algorithm subject to a spherical search space.…”

“…The problem dependency on the sphere radius makes it harder to solve. The problem formulation proposed in [1] outperforms the previous one and its success detection probability relies only on the equivalence between the 0 -norm and 1 -norm. This formulation is based on a ML criterion applied with relaxed constraints.…”

“…This formulation is based on a ML criterion applied with relaxed constraints. In this paper, we consider the method developed in [1] and we look for a new basis with limited size in order to develop a more reduced complexity algorithm and maintain a low computational cost even with the increase of the system and/or constellation size. This proposed algorithm relies on a half-sparse decomposition of the data signal vector and a new problem formulation.…”

“…In Section IV, we apply the proposed scheme to the massive MIMO noisy channel. In Section V, we compare the proposed half-sparse decomposition-based detection method to the one investigated in [1] as well as to the MMSE linear equalizer. Finally, Section VI concludes the paper.…”

“…In the following, we propose to recover x from sparse detection techniques as described in [1] but with a new decomposition that enables a computation cost reduction while keeping the same error rate performance. To this end, in Section III we study the detection scheme with the new decomposition assuming a noise-free transmission.…”