High-Efficiency Expectation Propagation Detector for High-Order Massive MIMO Systems

Yao, Guoqiang; Yang, Guiwu; Hu, Jianhao; Chao, Fei

doi:10.1109/access.2019.2932900

Cited by 11 publications

(6 citation statements)

References 37 publications

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“…Low-complexity versions of the EP detector have been proposed in [22]- [27]. The matrix inversion operations of the original EP detector have been approximated by the CG, Sherman-Morrison formula, and Neumann series approximation (NSA) in EP-CG [22], EP-SU [23], and EP-NSA [24], [25] detectors, respectively. Nevertheless, the EP-CG and EP-SU have a high processing delay due to requiring a large number of iterations [13] and the nature of the sequential updating, respectively.…”

Section: A Related Workmentioning

confidence: 99%

“…The variance of the BSE module in the 𝑡-th iteration is expressed as Fig. 3: The number of multiplications needed by the HI [15], HF [16], AMI [13], PIC-DSC [19], MMSE [9], MMSE-SIC [12], AMP [28], OAMP [30], D-EP [27], EP-NSA [24], EPA [26], EP [21], B-PIC-DSC, and IB-PIC-DSC detectors Although (23) is just a restatement of (11), we write it to emphasize that 𝑥 (𝑡) PIC,𝑘 in (23) must follow Assumption 1, expressed in (19). We can then iterate the computation of (𝑣 (𝑡) , 𝑉 (𝑡) 𝑘 ) in ( 20) or ( 21) and (23) until 𝑣 (𝑡) converges to a certain value, 𝑣 (𝑡) − 𝑣 (𝑡−1) ≤ 𝜁 .…”

Section: B the Variance Of The Output Of The Bse Modulementioning

confidence: 99%

“…Existing Bayesian detectors obtain the weighting coefficients for ( 12) and ( 13), from trial and error processes [21], [23], [27]- [29]. The weighting coefficients vary over the system configurations.…”

mentioning

confidence: 99%

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A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems

Kosasih,

Miloslavskaya,

Hardjawana

et al. 2021

Preprint

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The stringent requirements on reliability and processing delay in the fifth-generation (5G) cellular networks introduce considerable challenges in the design of massive multiple-input-multiple-output (M-MIMO) receivers. The two main components of an M-MIMO receiver are a detector and a decoder. To improve the trade-off between reliability and complexity, a Bayesian concept has been considered as a promising approach that enhances classical detectors, e.g. minimum-meansquare-error detector. This work proposes an iterative M-MIMO detector based on a Bayesian framework, a parallel interference cancellation scheme, and a decision statistics combining concept. We then develop a high performance M-MIMO receiver, integrating the proposed detector with a low complexity sequential decoding for polar codes. Simulation results of the proposed detector show a significant performance gain compared to other low complexity detectors. Furthermore, the proposed M-MIMO receiver with sequential decoding ensures one order magnitude lower complexity compared to a receiver with stack successive cancellation decoding for polar codes from the 5G New Radio standard.

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Section: A Related Workmentioning

confidence: 99%

Section: B the Variance Of The Output Of The Bse Modulementioning

confidence: 99%

See 1 more Smart Citation

A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems

Kosasih,

Miloslavskaya,

Hardjawana

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Up to our knowledge, GS has not been applied to EP MIMO detection, since it does not provide the estimation of the needed covariance matrix. Other approaches to approximate the inverse using EP are proposed in [25], [26], however, reported complexities scale as OpN t 3 q. All these low-complexity approaches focus on the approximate computation of the inverse, but furthermore they share the need for the Gram matrix to be computed in advance, involving in most of the cases a complexity that scales with N t 2 N r .…”

Section: Introductionmentioning

confidence: 99%

“…The EP-based techniques in [12]- [14], [23], [25], [26] describe solutions for standalone detection where EP is exclusively applied within the detector. A first version of EPbased IDD for MIMO is proposed in [11].…”

Section: Introductionmentioning

confidence: 99%

A Low-Complexity Double EP-based Detector for Iterative Detection and Decoding in MIMO

Fuentes¹,

santos²,

Aradillas³

et al. 2020

Preprint

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<div> <div> <div> <p>We propose a new iterative detection and decoding algorithm for multiple-input multiple-output (MIMO) based on expectation propagation (EP) with application to massive MIMO scenarios. Two main results are presented. We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO detector and the soft output of the channel decoder. With this novel approach, denoted by double-EP (DEP), the convergence is very much improved with a computational complexity just two times the one of the linear minimum mean square error (LMMSE), as illustrated by the included experiments. Besides, as in the LMMSE MIMO detector, when the number of antennas increases, the computational cost of the matrix inversion operation required by the DEP becomes unaffordable. In this work we also develop approaches of DEP where the mean and the covariance matrix of the posterior are approximated by using the Gauss-Seidel and Neumann series methods, respectively. This low-complexity DEP detector has quadratic complexity in the number of antennas, i.e., the same as the low-complexity LMMSE techniques. Experimental results show that the new low-complexity DEP achieves the performance of the DEP as the ratio between the number of transmitting and receiving antennas decreases </p> </div> </div> </div>

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Iterative Receiver Design for Probabilistic Constellation Shaping in ISI Channel

Wei

et al. 2020

IEEE Access

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This paper investigates the receiver design for probabilistic constellation shaping signaling over inter-symbol interference channel. The key component performing the constellation shaping is an adjustable distribution matcher, and the probabilistic shaping system is capable to adapt to variable data rates by adjusting the distribution match rate rather than the modulation or coding mode. In this paper, we resort to distinct techniques to derive two iterative receivers operating in time domain. Shaped-BCJR is a trellis-based solution where forward/backward algorithm together with turbo iteration is used to compute the posteriori probability in which the nonuniform a priori symbol probability initializes the calculation. Another receiver is based on linear filtering where expectation propagation provides a Gaussian approximation of the posteriori probability of each symbol. This receiver structure is represented by a factor graph and we detail the derivation of messages exchanging between adjacent nodes. The Bayesian equalization in each EP iteration brings unacceptable computational burden. An efficient block-wise matrix inversion strategy is proposed to tackle this problem, significantly reducing the computational complexity with little performance loss. Simulation results show that the proposed algorithms remarkably outperform LMMSE solutions and traditional EP based algorithms. Proposed matrix inversion strategy can also be used to improve the performance of other filter-type solutions.

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High-Efficiency Expectation Propagation Detector for High-Order Massive MIMO Systems

Cited by 11 publications

References 37 publications

A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems

A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems

A Low-Complexity Double EP-based Detector for Iterative Detection and Decoding in MIMO

Iterative Receiver Design for Probabilistic Constellation Shaping in ISI Channel

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