Abstract
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, achieving both spectral efficiency and energy efficiency. Due to the high computational complexity of the maximum likelihood detector in large antenna settings and symbol constellations, in this correspondence 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.
Highlights
Large-scale multiple input multiple output (LS-MIMO) schemes, where a large number of antenna elements (AEs) are employed at the base station (BS), are considered one of strongest candidates for enabling the intended capacity and reliability improvements in future 5G systems [1]
While spatial multiplexing MIMO is aimed at spectral efficiency (SE) and spatial modulations (SM) are targeted at EE [6][7], generalized spatial modulations (GSM) can be regarded as a compromise between both, as only a subset of the available transmitting antennas is active at any given moment
The values applied for the penalty parameters were ρx=ρz=2.5 as these were numerically found to result in good recovery performances
Summary
Large-scale multiple input multiple output (LS-MIMO) schemes, where a large number of antenna elements (AEs) are employed at the base station (BS), are considered one of strongest candidates for enabling the intended capacity and reliability improvements in future 5G systems [1]. In [16], a greedy algorithm named multipath matching pursuit with slicing (sMMP) was presented which combines the use of an inner integer slicing step with the adoption of multiple promising candidates for minimizing the residual This approach was adapted and evaluated for GSM-MIMO transmissions in [17], with good performance-complexity tradeoffs. What makes ADMM appealing for the GSM-MIMO detection context is its ability to split the MLD problem into a sequence of simpler steps comprising 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 As this last subproblem can incur an excessive complexity cost when the valid antenna combination set is large we propose a simpler algorithm version where this projection is relaxed into a simpler cardinality one. Symbols taken k at a time and In is the n×n identity matrix
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