Semi-Blind Channel Estimation and Data Detection for Multi-Cell Massive MIMO Systems on Time-Varying Channels

Naraghi-Pour, Mort; Rashid, Mohammed; Vargas-Rosales, César

doi:10.1109/access.2021.3132263

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…their internal operating characteristics. For instance, researchers [5,6] propose use of Polar Codes, and Semi-Blind Channel Estimation, which assists in improving channel estimation efficiency via code integrations. But these models show reduced scalability when applied to large-scale sets.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a Genetic Algorithm Based Dynamic Learning Method for Improved Channel Modelling in mmWave Radios via Temporal Breakpoint Analysis

Bhoi,

Hendre

2023

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Dynamic channel modelling allows communication interfaces to integrate continuous learning operations for incremental BER reductions. These models scan temporal BER patterns, and then tune internal-channel parameters in order to improving communication efficiency under real-time traffic scenarios. But these models showcase high complexity, thus cannot be scaled to large-scale network deployments. Moreover, these models are not flexible, and do not support denser channel models, which restricts their applicability under real-time scenarios. To overcome these issues, this text proposes design of a novel dynamic learning method for improved channel modelling in Phased array antennas mm Wave radios via temporal breakpoint analysis. The model initially collects information about channel BER and uses a Grey Wolf Optimization (GWO) technique to improve its internal model parameters. These parameters are further tuned via a novel breakpoint model, which enables for continuous and light-weighted tuning of channel modelling parameters. This allows the model to incrementally reduce BER even under denser noise levels. The model is further cascaded with a Q-Learning based optimization process, which assists in improving channel modelling efficiency for large-scale networks. Due to these integrations, the model is capable of reducing Bit Error Rate (BER) by 8.3% when compared with standard channel modelling techniques that use Convolutional Neural Networks (CNNs), Sparse Bayesian Learning, etc. These methods were selected for comparison due to their higher efficiency and scalability when applied to real-time communication scenarios. The model also showcased 6.5% lower computational delay due to linear processing operations. It was able to achieve 10.4% better channel coverage, 8.5% higher throughput, and 4.9% higher channel estimation accuracy, which makes it useful for a wide variety of real-time network deployments.

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Section: Literature Reviewmentioning

confidence: 99%

Design of a Genetic Algorithm Based Dynamic Learning Method for Improved Channel Modelling in mmWave Radios via Temporal Breakpoint Analysis

Bhoi,

Hendre

2023

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“…and 6 To derive (31) we use the fact that for a Bernoulli variable x, we have Bern(x;φ 1 ) Bern(x;φ 2 ) ∝ Bern(x; φ) where φ =…”

Section: Expectation Propagation Algorithmmentioning

confidence: 99%

“…• Expectation propagation (EP) algorithm [26], [27] has been recently applied to SIMO and MIMO channel estimation [28]- [31]. It has also been applied to solve the inference problem in the CS literature [32].…”

Section: Introductionmentioning

confidence: 99%

Clustered Sparse Channel Estimation for Massive MIMO Systems by Expectation Maximization-Propagation (EM-EP)

Rashid¹,

Naraghi-Pour²

2020

Preprint

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We study the problem of downlink channel estimation in multi-user massive multiple input multiple output (MIMO) systems. To this end, we consider a Bayesian compressive sensing approach in which the clustered sparse structure of the channel in the angular domain is employed to reduce the pilot overhead. To capture the clustered structure, we employ a conditionally independent identically distributed Bernoulli-Gaussian prior on the sparse vector representing the channel, and a Markov prior on its support vector. An expectation propagation (EP) algorithm is developed to approximate the intractable joint distribution on the sparse vector and its support with a distribution from an exponential family. The approximated distribution is then used for direct estimation of the channel. The EP algorithm assumes that the model parameters are known a priori. Since these parameters are unknown, we estimate these parameters using the expectation maximization (EM) algorithm. The combination of EM and EP referred to as EM-EP algorithm is reminiscent of the variational EM approach. Simulation results show that the proposed EM-EP algorithm outperforms several recently-proposed algorithms in the literature.

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Performance of MIMO MC-CDMA System with Relay using Adaptive Pilot Channel Estimation

Tamilarasan

Lenin

Nithyanandan

2022

J. Inst. Eng. India Ser. B

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Semi-Blind Channel Estimation and Data Detection for Multi-Cell Massive MIMO Systems on Time-Varying Channels

Cited by 7 publications

References 40 publications

Design of a Genetic Algorithm Based Dynamic Learning Method for Improved Channel Modelling in mmWave Radios via Temporal Breakpoint Analysis

Design of a Genetic Algorithm Based Dynamic Learning Method for Improved Channel Modelling in mmWave Radios via Temporal Breakpoint Analysis

Clustered Sparse Channel Estimation for Massive MIMO Systems by Expectation Maximization-Propagation (EM-EP)

Performance of MIMO MC-CDMA System with Relay using Adaptive Pilot Channel Estimation

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