Joint Semi-Blind Channel Estimation and Finite Alphabet Signal Recovery Detection for Large-Scale MIMO Systems

Hajji, Zahran; Bey, Abdeldjalil Aïssa El; Amis, Karine

doi:10.1109/ojsp.2021.3097968

Cited by 4 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Blind channel estimation techniques have also been studied in literature over the last years since they do not require the transmission of pilot signals [56], [57]. Hence, this method is a good candidate for mitigating the pilot contamination effect.…”

Section: Channel Estimationmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

Gkonis

2023

IEEE Access

View full text Add to dashboard Cite

The deployment of fifth-generation (5G) broadband wireless cellular networks has enabled the support of highly demanding applications, paving the way towards global broadband connectivity. In the same context, related advances in network infrastructure, such as network function virtualization via the decoupling of related functionalities from hardware equipment has leveraged end-to-end service support in highly heterogeneous environments. To this end, uninterrupted provision of service necessitates a holistic network redesign where access points (APs) should be placed and configured dynamically. One of the key supporting technologies of 5G communications are massive multiple input multiple output (m-MIMO) configurations, deployed over various APs, either in a centralized or in a distributed manner. However, such a configuration would pose an additional degree of complexity during network planning, since on one hand accurate channel estimation for all potential links is limited due to the pilot contamination effect, and on the other hand the number of actually deployed radio frequency chains is again limited when compared to the fully digital approach, in an effort to relax hardware and computational burden. Moreover, additional issues should be taken into consideration as well, such as the coexistence of m-MIMO configurations with other novel technologies in the 5G air interface as well as the need for provision of acceptable quality of service to a large number of mobile users. In an effort to deal with the aforementioned issues, machine learning (ML) algorithms have emerged over the last decade as a promising solution that can deal with complex optimization problems. In this context, the main goal of this survey paper is to present the state-of-the-art approaches in the deployment of ML methods in m-MIMO configurations. Unlike other related survey papers, this is the first scientific attempt to cover all aspects of m-MIMO practical implementations (i.e., channel estimation, MIMO precoding and signal detection, hybrid beamforming, distributed configurations, user pairing and power allocation in non-orthogonal multiple access), in the context of ML-aided calculations. To this end, all presented works are categorized according to the studied aspect, while in the same context potential limitations and future challenges are highlighted as well.INDEX TERMS Massive MIMO, machine learning, channel estimation, hybrid beamforming, cell-free orientations, NOMA.

show abstract

Section: Channel Estimationmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

Gkonis

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…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.…”

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

View full text Add to dashboard Cite

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.

show abstract

Blind and Semi-blind Channel Estimation and Collision Resolution for the Uplink of MU-MIMO and Massive MIMO Systems for B5G Networks

Missaoui,

Kammoun,

Siala

2024

Wireless Pers Commun

View full text Add to dashboard Cite

Joint Semi-Blind Channel Estimation and Finite Alphabet Signal Recovery Detection for Large-Scale MIMO Systems

Cited by 4 publications

References 31 publications

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

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

Blind and Semi-blind Channel Estimation and Collision Resolution for the Uplink of MU-MIMO and Massive MIMO Systems for B5G Networks

Contact Info

Product

Resources

About