An Improved Expectation Propagation Based Detection Scheme for MIMO Systems

Yao, Guoqiang; Chen, Hang; Hu, Jianhao

doi:10.1109/tcomm.2020.3048942

Cited by 19 publications

(20 citation statements)

References 46 publications

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“…Performance comparison of the proposed LSFSD, IFSD, 40 FSD, 13 EP, 30 GTA, 33 and MMSE for MIMO systems with

{N}_T=32

transmit antennas and

{N}_R=32,40,48,56,64,72

receive antennas…”

Section: Simulation Resultsmentioning

confidence: 99%

“…BER performance comparison of the proposed LSFSD, IFSD, 40 FSD, 13 EP, 30 GTA, 33 and MMSE detectors for

{N}_T=32

and

{N}_R=40,64

antenna configurations using 64‐QAM modulation for the cases of imperfect CSI and different antenna correlations…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The complexity of FSD based detectors comes from SQRD operation and tree searching. From Table 2, we can write that the F I G U R E 12 BER performance comparison of the proposed LSFSD, IFSD, 40 FSD, 13 EP, 30 GTA, 33 and MMSE detectors for N T = 32 and N R = 40, 64 antenna configurations using 64-QAM modulation for the cases of imperfect CSI and different antenna correlations…”

Section: Complexity Analysismentioning

confidence: 99%

“…However, when the

{N}_R/{N}_T

ratio is close to one, the performance of MMSE/ZF is not close to optimal. Hence, in this case, other detection algorithms such as Tabu search, 28 FSD, 13,20 expectation propagation, 29,30 approximate message passing (AMP), 31 channel hardening‐exploiting message passing (CHEMP), 32 Gaussian tree approximation (GTA), 33 etc. can be used to obtain better performance.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic branch pruning aided low switching fixed complexity sphere decoding for small scale and massive MIMO detection

Chakraborty

Sinha

Mitra

2022

Trans Emerging Tel Tech

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In this article, we have proposed a dynamic branch pruning aided low switching fixed complexity sphere decoding (LSFSD) algorithm that can reduce the number of node visits in conventional fixed‐complexity sphere decoder (FSD) or imbalanced fixed‐complexity sphere decoder (IFSD) detector dynamically based on the channel condition. Further, we propose a low complexity QR‐based FSD ordering scheme in the real domain that requires half of the number of real arithmetic operations in the conventional case. As a case study, we have simulated the proposed algorithm for 4prefix×4$$ 4\times 4 $$ and 6prefix×6$$ 6\times 6 $$ MIMO system with 64‐QAM modulation. The simulation results show that the proposed algorithm can reduce approximately 50% of node visits to achieve a BER of 10prefix−3$$ 1{0}^{-3} $$. Additionally, a low complexity solution of the soft bit information generation method is also proposed. The concept is further extended to massive MIMO applications where the number of receiving antenna to transmit antenna ratio is close to one. A smaller node extension parameter is defined for the proposed detector to reduce the complexity in massive MIMO detection. Simulation results demonstrate that the proposed detection algorithm has significantly lower computational complexity than other recent state‐of‐the‐art detection algorithms for massive MIMO and large MIMO systems.

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“…Performance comparison of the proposed LSFSD, IFSD, 40 FSD, 13 EP, 30 GTA, 33 and MMSE for MIMO systems with

{N}_T=32

transmit antennas and

{N}_R=32,40,48,56,64,72

receive antennas…”

Section: Simulation Resultsmentioning

confidence: 99%

“…BER performance comparison of the proposed LSFSD, IFSD, 40 FSD, 13 EP, 30 GTA, 33 and MMSE detectors for

{N}_T=32

and

{N}_R=40,64

antenna configurations using 64‐QAM modulation for the cases of imperfect CSI and different antenna correlations…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Complexity Analysismentioning

confidence: 99%

“…However, when the

{N}_R/{N}_T

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic branch pruning aided low switching fixed complexity sphere decoding for small scale and massive MIMO detection

Chakraborty

Sinha

Mitra

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…The main goal in multi-antenna systems is to increase the channel capacity with M T transmit and M R receive antennas by a factor of min(M T , M R ) without using additional transmit power or spectral bandwidth [43]. Considering the MIMO digital communication system r(k) = H T x(k) + η(k), with transmitted signal x(k) ∈ C M T , received signal r(k) ∈ C M R , and additive white Gaussian noise (AWGN) vector η(k) ∈ C M R , the channel capacity of H ∈ C M T ×M R is expressed as [44]…”

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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