Analysis of a distributed MIMO channel capacity under a special scenario

Zhao, Xiaonan; Jia, Pengzhen; Zhang, Qianwen; Li, Yupeng; Xing, Ruiqing; Liu, Yuan

doi:10.1186/s13638-019-1515-0

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…In this case, we consider that power is equally divided among the transmitting antennas, which implies R xx = I M T . The capacity in such a case is then given by [11,45]…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

Soares

Mayer

Castro

et al. 2021

Sensors

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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.

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“…In this case, we consider that power is equally divided among the transmitting antennas, which implies R xx = I M T . The capacity in such a case is then given by [11,45]…”

Section: Introductionmentioning

confidence: 99%