Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

Haidine, Abdelfatteh; Salmam, Fatima Zahra; Aqqal, Abdelhak; Dahbi, Aziz

doi:10.5772/intechopen.98517

Cited by 28 publications

(12 citation statements)

References 14 publications

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“…The authors in [11] tried to answer the fundamental questions; what the optimal number of antennas are, active users and transmit power, for a multiuser MIMO system to be designed from scratch to uniformly cover a given area with maximal energy efficiency. Haidine et al [7] states that AI and ML will unlock the power of software and algorithms that will allow for efficient deployment of assets and resources. In this work, ML approach was used in determining the key features of a 5G production dataset for improving the energy efficiency of a 5G network.…”

Section: Reviewed Literaturementioning

confidence: 99%

Comparative Analysis of the Features of a 5G Network Production Dataset: The Machine Learning Approach

Okpara

Idigo

Okafor

2023

EJENG

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5G networks deployment is much data driven, leading to more energy consumption. The need to efficiently manage this energy consumption is a major drive in the comparative analysis of the features of a 5G production dataset. The features of the 5G production dataset generated with G-Net track pro were analyzed using Python programming language. From the correlation coefficient results obtained, the highest correlation value of 0.78 exists between the reference signal power and the received signal reference power of the neighbouring cells. Using the significant indicator, we observed that the signal to noise ratio is the most important of all the features. Using heat map and scatter plots, we further observed that there were good relationships between the key features selected from the significant indicator. These features will play a big role in improving the energy efficiency of a 5G network.

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Section: Reviewed Literaturementioning

confidence: 99%

Comparative Analysis of the Features of a 5G Network Production Dataset: The Machine Learning Approach

Okpara

Idigo

Okafor

2023

EJENG

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“…Machine learning (ML) is a subset of artificial intelligence (AI) that concerns the development of algorithms, which allows the machine to learn via inductive inference based on observation data that represent incomplete information about statistical phenomena [9]. To carry out the learning process an algorithm is used based on examples of the task we want to solve (data) and letting the computer find patterns and make inferences that optimize the decision-making according to a user-defined objective [10]. Based on the training strategy, ML can be divided into three classical categories with different learning approaches: supervised learning, unsupervised learning, and reinforcement learning [10].…”

Section: Machine Learningmentioning

confidence: 99%

“…To carry out the learning process an algorithm is used based on examples of the task we want to solve (data) and letting the computer find patterns and make inferences that optimize the decision-making according to a user-defined objective [10]. Based on the training strategy, ML can be divided into three classical categories with different learning approaches: supervised learning, unsupervised learning, and reinforcement learning [10]. The first one includes classification and regression tasks, in the second one the widely used task is clustering, and the third one consists of the process of training a model on a series of actions that lead to a particular outcome, where the system receives rewards for performing well and punishment for performing poorly directly from its environment [10].…”

Section: Machine Learningmentioning

confidence: 99%

A State-of-the-Art Survey on Various Domains of Multi-Agent Systems and Machine Learning

Barrientos¹,

Luévano²

2023

Multi-Agent Technologies and Machine Learning

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Multi-agent systems (MASs) are defined as a group of interacting entities or agents sharing a common environment that changes over time, with capabilities of perception and action, and the mechanisms for their coordination provide a modern perspective on systems that traditionally were regarded as centralized. The main characteristics of agents are learning and adaptation. In the last few years, MASs have received tremendous attention from scholars in different fields. However, there are still challenges faced by MASs and their integration with machine learning (ML) methods. The primary goal of the study is to provide a broad review of the current developments in the field of MASs combined with ML methods. First, we present features of MASs considering the ML perspective. Second, we provide a classification of applications of MASs combined with ML methods. Third, we present a density map of applications in E-learning, manufacturing, and commerce. We expect this study to serve as a comprehensive resource for researchers and practitioners in the area.

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“…This integration of ML and AI will lead to improved performance of network applications in terms of latency and reliability 26 . The relationship between AI, ML, and DL is illustrated schematically in Figure 3 27 . An excessive amount of overhead is required to keep up with the expanding number of 5G‐IoT wireless devices and the vast variety of new applications.…”

Section: Introductionmentioning

confidence: 99%

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

Shankar

2023

Int J Communication

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SummaryIn this work, a deep learning (DL)‐based massive multiple‐input multiple‐output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL‐C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi‐directional long short‐term memory (Bi‐LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi‐LSTM‐based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function‐trained Bi‐LSTM‐based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi‐LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short‐term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.

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Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

Cited by 28 publications

References 14 publications

Comparative Analysis of the Features of a 5G Network Production Dataset: The Machine Learning Approach

Comparative Analysis of the Features of a 5G Network Production Dataset: The Machine Learning Approach

A State-of-the-Art Survey on Various Domains of Multi-Agent Systems and Machine Learning

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

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