Improving Medium Access Efficiency With Intelligent Spectrum Learning

Yang, Bo; Xiao, Cao; Omotere, Oluwaseyi; Li, Xiangfang; Han, Zhu; Qian, Lijun

doi:10.1109/access.2020.2995398

Cited by 10 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Case-2, the number of RISs is smaller than the number of users in M , i.e., K < M , so the elements of each RIS can be divided into multiple groups [50]. In this case, multiple users are allowed to reuse one RIS in such a manner that the association is no longer constrained by (17b), while does not violate the SNR constraint in (17c) 8 . Thus, this matching game is considered as a one-to-many matching with externalities for the user-RIS association.…”

Section: Case-2: User-ris Association Via One-to-many Matchingmentioning

confidence: 99%

Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks

Yang¹,

Xiao²,

Huang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Increasing concerns on intelligent spectrum sensing call for efficient training and inference technologies. In this paper, we propose a novel federated learning (FL) framework, dubbed federated spectrum learning (FSL), which exploits the benefits of reconfigurable intelligent surfaces (RISs) and overcomes the unfavorable impact of deep fading channels. Distinguishingly, we endow conventional RISs with spectrum learning capabilities by leveraging a fully-trained convolutional neural network (CNN) model at each RIS controller, thereby helping the base station to cooperatively infer the users who request to participate in FL at the beginning of each training iteration. To fully exploit the potential of FL and RISs, we address three technical challenges: RISs phase shifts configuration, user-RIS association, and wireless bandwidth allocation. The resulting joint learning, wireless resource allocation, and user-RIS association design is formulated as an optimization problem whose objective is to maximize the system utility while considering the impact of FL prediction accuracy. In this context, the accuracy of FL prediction interplays with the performance of resource optimization. In particular, if the accuracy of the trained CNN model deteriorates, the performance of resource allocation worsens. The proposed FSL framework is tested by using real radio frequency (RF) traces and numerical results demonstrate its advantages in terms of spectrum prediction accuracy and system utility: a better CNN prediction accuracy and FL system utility can be achieved with a larger number of RISs and reflecting elements.

show abstract

Section: Case-2: User-ris Association Via One-to-many Matchingmentioning

confidence: 99%

Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks

Yang¹,

Xiao²,

Huang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…An interesting approach is available in [193], where the authors propose a MAC protocol based on intelligent spectrum learning for future WLAN networks. An access point (AP) is installed with a pre-trained CNN model able to identify the number of stations (STAs) involved in the collisions based on RF traces.…”

Section: Referencementioning

confidence: 99%

“…), non-adaptive modulation scheme, static non-application cognizant MAC, etc. • Wireless interference identification [128,169,[177][178][179][180][181][182][183][184][185][186][187][188][189][190][191][192][193] MAC analysis…”

mentioning

confidence: 99%

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

et al. 2021

View full text Add to dashboard Cite

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

show abstract

“…Among ML techniques, Reinforcement Learning (RL) is suitable for the unknown environments where decision-making ability is crucial. Recently, Deep learning (DL) and Deep Reinforcement Learning (DRL) [29] techniques have been applied to various protocol and radio optimization tasks including routing [19], congestion control [62], MAC [46], [63] and frequency estimation in PHY layer [69], just to name a few.…”

Section: Introductionmentioning

confidence: 99%

Towards a Learning-Based Framework for Self-Driving Design of Networking Protocols

Pasandi

Nadeem

2021

IEEE Access

View full text Add to dashboard Cite

Networking protocols are designed through long-standing and hard-working human efforts. Machine Learning (ML)-based solutions for communication protocol design have been developed to avoid manual effort to adjust individual protocol parameters. While other proposed ML-based methods focus mainly on tuning individual protocol parameters (e.g. contention window adjustment), our main contribution is to propose a new Deep Reinforcement Learning (DRL) framework to systematically design and evaluate networking protocols. We decouple the protocol into a set of parametric modules, each representing the main protocol functionality that is used as a DRL input to better understand and systematically analyze the optimization of generated protocols. As a case study, we introduce and evaluate DeepMAC a framework in which the MAC protocol is decoupled into a set of blocks across popular 802.11 WLANs (e.g. 802.11 a/b/g/n/ac). We are interested to see which blocks are selected by DeepMAC across different networking scenarios and whether DeepMAC is capable of adapting to network dynamics.INDEX TERMS Communication protocols, deep learning, machine-generated algorithm, protocol design, reinforcement learning.This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3061729, IEEE Access H. B. Pasandi, T. Nadeem: Towards a Learning-Based Framework for Self-Driving Design of Networking Protocols II. BACKGROUND HANNANEH BARAHOUEI PASANDI (Student Member, IEEE) received the master's degree from Université Grenoble Alpes. She is currently pursuing the Ph.D. degree with the Department of Computer Science, Virginia Commonwealth University. Her research interests include wireless communication, with an emphasis on the design and implementation of data-driven MAC protocols that not only consider conventional factors but also extract and include important environmental factors in designing protocols for IEEE WLANs.

show abstract

Improving Medium Access Efficiency With Intelligent Spectrum Learning

Cited by 10 publications

References 31 publications

Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks

Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

Towards a Learning-Based Framework for Self-Driving Design of Networking Protocols

Contact Info

Product

Resources

About