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This letter presents a novel detection strategy for Spatially-Multiplexed Generalized Spatial Modulation systems. It is a multi-stage detection that produces a list of candidates of the transmitted signal vector, sorted according to the proximity of the data vector to one of the possible vector subspaces. The quality metric and list-length metric selects the best candidate and manages the list length, respectively. Performance results show that it significantly reduces the performance gap to the optimal maximum likelihood detector, while maintaining significant computational cost reduction.
This paper introduces a novel approach of utilizing the reconfigurable intelligent surface (RIS) for joint data modulation and signal beamforming in a multi-user downlink cellular network by leveraging the idea of backscatter communication. We presen
In this paper, we consider the downlink of a massive multiple-input-multiple-output (MIMO) single user transmission system operating in the millimeter wave outdoor narrowband channel environment. We propose a novel receive spatial modulation architec
Massive MIMO, a candidate for 5G technology, promises significant gains in wireless data rates and link reliability by using large numbers of antennas (more than 64) at the base transceiver station (BTS). Extra antennas help by focusing the transmiss
In this paper, an analytical framework for evaluating the performance of scalable cell-free massive MIMO (SCF-mMIMO) systems in which all user equipments (UEs) and access points (APs) employ finite resolution digital-to-analog converters (DACs) and a
Multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) cellular network is promising for supporting massive connectivity. This paper exploits low-latency machine learning in the MIMO-NOMA uplink transmission environment, where a su