Iterative signal detection for large scale GSM-MIMO systems

Generalized spatial modulations (GSM) represent a novel multiple input multiple output (MIMO) scheme which can be regarded as a compromise between spatial multiplexing MIMO and conventional spatial modulations (SM), achieving both spectral efficiency (SE) and energy efficiency (EE). Due to the high computational complexity of the maximum likelihood detector (MLD) in large antenna settings and symbol constellations, in this paper we propose a lower complexity iterative suboptimal detector. The derived algorithm comprises a sequence of simple processing steps, namely an unconstrained Euclidean distance minimization problem, an element wise projection over the signal constellation and a projection over the set of valid active antenna combinations. To deal with scenarios where the number of possible active antenna combinations is large, an alternative version of the algorithm which adopts a simpler cardinality projection is also presented. Simulation results show that, compared with other existing approaches, both versions of the proposed algorithm are effective in challenging underdetermined scenarios where the number of receiver antennas is lower than the number of transmitter antennas.info:eu-repo/semantics/acceptedVersio

Efficient recovery algorithm for discrete valued sparse signals using an ADMM approach

Motivated by applications in wireless communications, in this paper we propose a reconstruction algorithm for sparse signals whose values are taken from a discrete set, using a limited number of noisy observations. Unlike conventional compressed sensing algorithms, the proposed approach incorporates knowledge of the discrete valued nature of the signal in the detection process. This is accomplished through the alternating direction method of the multipliers which is applied as a heuristic to decompose the associated maximum likelihood detection problem in order to find candidate solutions with a low computational complexity order. Numerical results in different scenarios show that the proposed algorithm is capable of achieving very competitive recovery error rates when compared with other existing suboptimal approaches.info:eu-repo/semantics/publishedVersio

Recovery under Side Constraints

This paper addresses sparse signal reconstruction under various types of structural side constraints with applications in multi-antenna systems. Side constraints may result from prior information on the measurement system and the sparse signal structure. They may involve the structure of the sensing matrix, the structure of the non-zero support values, the temporal structure of the sparse representationvector, and the nonlinear measurement structure. First, we demonstrate how a priori information in form of structural side constraints influence recovery guarantees (null space properties) using L1-minimization. Furthermore, for constant modulus signals, signals with row-, block- and rank-sparsity, as well as non-circular signals, we illustrate how structural prior information can be used to devise efficient algorithms with improved recovery performance and reduced computational complexity. Finally, we address the measurement system design for linear and nonlinear measurements of sparse signals. Moreover, we discuss the linear mixing matrix design based on coherence minimization. Then we extend our focus to nonlinear measurement systems where we design parallel optimization algorithms to efficiently compute stationary points in the sparse phase retrieval problem with and without dictionary learning