Machine Learning Based Link Adaptation Method for MIMO System

Hassan

IEEE Commun. Surv. Tutorials

et al. 2020

282

159

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML-and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML-and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

“…In [164], the authors presented ML algorithms for link adaptation in massive MIMO. For the selection of transmission parameters, autoencoder-SVM and autoencoder-softmax were proposed.…”

Section: ML In Mimo and Massive Mimo Communicationsmentioning

confidence: 99%

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Hassan

IEEE Commun. Surv. Tutorials

et al. 2020

282

159

“…In terms of VANETs LA strategies using Machine Learning (ML) techniques, very few works have been published in the literature. The authors in [23] investigated a machine learning method of link adaptation which analytically characterizes the adaptation problem to maximize the system throughput while maintaining transmission reliability. In order to maximize the whole system throughput, the setup uses an Nt x Nr MIMO system and employs an autoencoder model and a multi-class SVM to select MCS through SNRs for MIMO-OFDM systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Neural Network and Levenberg–Marquardt Algorithm for Link Adaptation Strategy in Vehicular Ad Hoc Network

Feukeu,

Sumbwanyambe

2023

IEEE Access

Vehicular Ad Hoc Network (VANET) was initiated about two decades ago in view of saving lives by mitigating and reducing the number of accidents and incidents on public roads. Moreover, this objective can only be achieved if VANETs mobiles regularly exchange Road State Information (RSI) with their neighborhood and take decisive actions based on the RSI received. Therefore, it becomes paramount to ensure that the transmitted message is well received. And this is only possible if the quality of the sharing medium or link is controlled, and transmission performed while taking into consideration the Channel State Information (CSI). The CSI provides information related to channel quality, Signal-to-Noise Ratio (SNR), and so forth. The process of adapting the payload as a function of the CSI is called Link Adaptation (LA). Several LA works have already been published in VANETs, but almost without serious consideration of the effect of the relative mobility amongst the nodes. Hence, while taking into consideration the Doppler Shift induced by the relative velocity, the current work presents a link adaptation strategy using a Neural Network (NN) and the Levenberg-Marquardt algorithm in VANETs. The simulation results definitively demonstrate that the NN approach outperforms its counterparts by a significant margin. It achieves a performance of 1075% in transmission duration, 180% in transmitted bit, and 115% in model efficiency when compared to the Cte, ARF, and AMC algorithms, respectively.

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

“…In [5], three supervised learning techniques, k-NN, SVM and RF, are applied. In [6], is proposed a mapping between the channel state information (CSI) and parameters such as rank indicator (RI) and channel quality indicator (CQI) feedback. The authors first propose an unsupervised artificial neural network called autoencoder and multi-class SVM to select MCS through SNRs (Signal-to-noise ratio) for MIMO-OFDM systems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Bayesian Approach Applied in Link Adaptation Problem for Fifth Generation Network

Nascimento¹,

Cavalcanti²,

Almeida³

et al. 2020

With technological development, wireless communication has been one of the fastest growing fields of Computing and Engineering in recent years. This fact requires that new approaches be developed to ensure better performance and reliability in wireless communication. In this paper a new approach has been proposed as a solution to the problem of adaptive modulation and coding (AMC), through the development of an extension of the method naive Bayesian classifier, known as dynamic naive Bayesian classifier, to maximize spectral efficiency. The proposed approach exhibits a better performance than k-nearest neighbours algorithm and the traditional Look-Up table solution, with average classification error 2.85%, which represents approximately 10% with respect to the most similar method.