Effective Channel Blind Estimation in Cell-Free Massive MIMO Networks

Souza, Daynara D.; Freitas, Marx; Borges, Gilvan; Cavalcante, André; Costa, Daniel Benevides da; Costa, João Crisóstomo Weyl Albuquerque

doi:10.1109/lwc.2021.3132418

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…Maximum Likelihood Channel Estimation (MLCE) [28], ML based estimation [29], channel state information (CSI) estimation [30], Finite Alphabet Signal Recovery (FASR) [31], recovering DNN (RC-DNN) [32], and Attention-Aided Deep Learning (AADL) [33], which aims at enhancing channel estimation efficiency under different communication scenarios. These models are highly efficient, and can be extended via the work proposed in studies [34][35][36][37][38], which uses Unified Channel Estimation Frameworks, Orthogonal Chirp Division Multiplexing, Downlink estimations, entanglementbreaking channels, and maximum-ratio (MR) precoding methods, that aim at pre-empting channel changes for efficient estimation of channel parameter sets. Extensions to these models are discussed in studies [39][40][41][42][43] which propose use of Affine-Pre-coded Superimposed Pilots (APCSP), Bayesian Learning, Deep Learning, Machine Learning based pilots, and Log-Sum Sparse Constraints that aims at reducing channel BER via complex iterative operations.…”

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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“…The two-stage approach based on the vector approximate message passing algorithm and linear minimum mean square error method was proposed to detect the random activities of devices and estimate their channel states for the devices of Internet of Thing in UC networks with massive random access in Ref. [20]. Besides, the blind channel estimation method for UC MIMO networks was investigated in Ref.…”

Section: Channel Estimationmentioning

confidence: 99%

Joint association and beamforming optimization in reconfigurable intelligent surface-enhanced user-centric networks

Yao

Tian

Chen

et al. 2022

Intell. and Converged Netw.

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Fully coordinated Cell-Free (CF) networks can alleviate the Inter-Cell Interference (ICI) for the cell-edge users in cellular networks. Due to the complex topology of the association between the Access Points (APs) and the users in CF networks, it is challenging to deploy CF networks in practical scenarios. In order to make CF networks feasible, we introduce User-Centric (UC) networks enabling each user served by a limited number of APs. As a low-cost and energyefficient technology, Reconfigurable Intelligent Surface (RIS) can be embedded in UC networks to further improve the system performance. First, we provide a brief survey on the prior works in UC networks for clear comprehension. Then, we formulate a Spectral Efficiency (SE) maximization problem for RIS-enhanced UC networks. For solving the nonconvex problem, we divide it into three subproblems and propose a joint optimization framework for optimizing APuser association, active beamforming of multiple antennas at the APs, and the passive beamforming of the RIS. Besides, a channel gain based association method coupled with the design of RIS is proposed to construct a dynamic and efficient association. The subproblems about optimizing active and passive beamforming are solved with the fractional programming. Simulation results show that the proposed joint optimization framework for RIS-enhanced UC networks can obtain good SE compared with other benchmark schemes.

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Energy aware resource control mechanism for improved performance in future green 6G networks

Taneja

Rani²,

Garg³

et al. 2022

Computer Networks

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Effective Channel Blind Estimation in Cell-Free Massive MIMO Networks

Cited by 8 publications

References 8 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

Joint association and beamforming optimization in reconfigurable intelligent surface-enhanced user-centric networks

Energy aware resource control mechanism for improved performance in future green 6G networks

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