Abstract
Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.
Highlights
In recent years, with the increasing demand for the real-time processing of big data, the Internet of Things (IoT), and 4K video streaming, technologies to increase area throughput [1] in base station (BS) coverage and hotspot tiers [2] have become increasingly important
Using the formerly mentioned orthogonal STBC (OSTBC) [54] and quasi-orthogonal space–time block coding (QOSTBC) [56] coding schemes, several setups are compared with the proposed approach to validate and assess their performance in massive Multi-input multi-output (MIMO)-orthogonal frequency-division multiplexing (OFDM)
OSTBC and QOSTBC are simulated with the maximum likelihood (ML) decoding with perfect channel knowledge
Summary
With the increasing demand for the real-time processing of big data, the Internet of Things (IoT), and 4K video streaming, technologies to increase area throughput [1] in base station (BS) coverage and hotspot tiers [2] have become increasingly important. There is a linear free parameter vector of weights, which linearly weighs the neuron outputs to yield the network output [35,41] With these three independent sets of parameters, RBFNNs are able to represent high-order nonlinear surfaces without increasing the number of layers, thereby reducing complexity compared with deep neural networks [11,35]. In this context, this work proposes a novel complex-valued RBF neural network architecture: a multiple-input multiple-output phase transmittance RBF (MIMO-PTRBF).
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