Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems

Bobrov, Evgeny; Troshin, S.; Chirkova, Nadezhda; Lobacheva, Ekaterina; Panchenko, Sviatoslav; Vetrov, Dmitry; Kropotov, Dmitry

doi:10.48550/arxiv.2112.14423

Cited by 2 publications

(4 citation statements)

References 28 publications

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“…To achieve this, several approaches have been considered in the literature. These approaches include AS, power control to minimize interference, low complexity algorithms, optimal resource allocations [141] and spectral efficiency prediction [142]. The ML that has been exploited in overcoming some of the challenges in the CM-MIMO systems and promoting I-mMIMO systems are discussed under the following: power control, resource allocation, and AS.…”

Section: Spectral and Energy Efficiencymentioning

confidence: 99%

“…While the application of ML in AS, power control, and resource control helps to improve and maximize spectral efficiency in the mMIMO systems, the application of ML to predict spectral efficiency using ML techniques is another approach. In [142], different ML techniques were explored in the prediction of average spectral efficiency and user spectral efficiency achievable with precoding methods for the mMIMO systems. The challenge with this approach is that it requires feature extraction based on the existing mMIMO systems configuration and systems performance metrics like the SINR.…”

Section: ) Summarymentioning

confidence: 99%

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Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

et al. 2022

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Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the potential of challenging large-scale problems in conventional massive multiple-input-multiple-output MIMO (CM-MIMO) systems. This introduces the concept of intelligent mMIMO (I-mMIMO) systems. Due to the surge of application of different ML techniques in the enhancement of mMIMO systems for existing and emerging use cases beyond fifth-generation (B5G) networks, this article aims to provide an overview of the different aspects of the I-mMIMO systems. First, the characteristics and challenges of the CM-MIMO have been identified. Secondly, the most recent efforts aimed at applying ML to a different aspect of CM-MIMO systems are presented. Thirdly, the deployment of I-mMIMO and efforts towards standardization are discussed. Lastly, the future trends of I-mMIMO enabled application systems are presented. The aim of this paper is to assist the readers to understand different ML approaches in CM-MIMO systems, explore some of the advantages and disadvantages, identify some of the open issues, and motivate the readers toward future trends.

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Section: Spectral and Energy Efficiencymentioning

confidence: 99%

Section: ) Summarymentioning

confidence: 99%

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

et al. 2022

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“…A deep neural network (DNN) based IRS-aided PB was proposed in [ 24 ] to improve SE. In [ 25 ], the authors proposed a SE maximization method using ML in a massive multiple input–multiple output (MIMO) system. The authors of [ 25 ] used gradient boosting and NN models to measure SE with different mean average percentage error (MAPE) calculations.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 25 ], the authors proposed a SE maximization method using ML in a massive multiple input–multiple output (MIMO) system. The authors of [ 25 ] used gradient boosting and NN models to measure SE with different mean average percentage error (MAPE) calculations. A multi-IRS-aided massive MIMO non-orthogonal multiple access (NOMA) network was designed in [ 26 ] to measure the SE.…”

Section: Introductionmentioning

confidence: 99%

Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System

Aziz,

Rahman,

Sejan

et al. 2023

Sensors

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The intelligent reflecting surface (IRS) is a two-dimensional (2D) surface with a programmable structure and is composed of many arrays. The arrays are used to supervise electromagnetic wave propagation by altering the electric and magnetic properties of the 2D surface. IRS can influentially convert wireless channels to very effectively enhance spectral efficiency (SE) and communication performance in wireless systems. However, proper channel information is necessary to realize the IRS anticipated gains. The conventional technique has been taken into consideration in recent attempts to fix this issue, which is straightforward but not ideal. A deep learning model which is called the long short-term memory (Bi-LSTM) model can tackle this issue due to its good learning capability and it plays a vital role in enhancing SE. Bi-LSTM can collect data from both forward and backward directions simultaneously to provide improved prediction accuracy. Because of the tremendous benefits of the Bi-LSTM model, in this paper, an IRS-assisted Bi-LSTM model-based multi-user multiple input single output downlink system is proposed for SE improvement. A Wiener filter is used to determine the optimal phase of each IRS element. In the simulation results, the proposed system is compared with other DL models and methods for the SE performance evaluation. The model exhibits satisfactory SE performance with a different signal-to-noise ratio compared to other schemes in the online phase.

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Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems

Cited by 2 publications

References 28 publications

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System

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