Deep Reinforcement Learning MAC for Backscatter Communications Relying on Wi-Fi Architecture

Xiao, Cao; Song, Zuxun; Yang, Bo; Du, Xunsheng; Qian, Lijun; Han, Zhu

doi:10.1109/globecom38437.2019.9013445

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…In [3], the conventional CSMA/CA protocol was improved for densely deployed WiFi networks, in which DRL was adopted to learn the optimal CW for each WiFi node to improve overall throughput. Cao et al [9] proposed a DRL-based MAC protocol to assist the backscatter communications for IoT networks, where the DRL was introduced to learn the reserved information and make decisions accordingly.…”

Section: Related Workmentioning

confidence: 99%

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

Pasandi

Nadeem

2021

IEEE Access

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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.

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Section: Related Workmentioning

confidence: 99%

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

Pasandi

Nadeem

2021

IEEE Access

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“…As such, the unsupervised learning-based MAC design is suitable for the practical wireless network scenarios where no prior knowledge about the outcomes exists. Specifically, recent years have witnessed the wide study on deep reinforcement learning (DRL) in the field of dynamic spectrum access problems in wireless networks [22]- [26]. In particular, Nguyen et al [22] proposed to use the deep Q-learning method to learn a state-action value function that determines an access policy from the observed states of all channels.…”

Section: B Unsupervised Learning-based Mac Designmentioning

confidence: 99%

“…Furthermore, Yu et al [25] investigated a DRL-based MAC protocol to learn an optimal channel access strategy to achieve a certain pre-specified global objective for heterogeneous wireless networking. Cao et al [26] proposed a DRL-based MAC protocol to assist the backscatter communications for Internet-of-Things (IoT) networks, where the DRL was introduced to learn the reserved information and make decisions accordingly.…”

Section: B Unsupervised Learning-based Mac Designmentioning

confidence: 99%

Improving Medium Access Efficiency With Intelligent Spectrum Learning

et al. 2020

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Through machine learning, this paper changes the fundamental assumption of the traditional medium access control (MAC) layer design. It obtains the capability of retrieving the information even the packets collide by training a deep neural network offline with the historical radio frequency (RF) traces and inferring the STAs involved collisions online in near-real-time. Specifically, we propose a MAC protocol based on intelligent spectrum learning for the future wireless local area networks (WLANs), called SL-MAC. In the proposed MAC, an access point (AP) is installed with a pre-trained convolutional neural network (CNN) model to identify the stations (STAs) involved in the collisions. In contrast to the conventional contention-based random medium access methods, e.g., IEEE 802.11 distributed coordination function (DCF), the proposed SL-MAC protocol seeks to schedule data transmissions from the STAs suffering from the collisions. To achieve this goal, we develop a two-step offline training algorithm enabling the AP to sense the spectrum with the aid of the CNN. In particular, on receiving the overlapped signal(s), the AP firstly predicts the number of STAs involving collisions and then further identifies the STAs' ID. Furthermore, we analyze the upper bound of throughput gain brought by the CNN predictor and investigate the impact of the inference error on the achieved throughput. Extensive simulations show the superiority of the proposed SL-MAC and allow us to gain insights on the trade-off between performance gain and the inference accuracy. INDEX TERMS Medium access control (MAC), spectrum sensing, deep learning, convolutional neural network (CNN).

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“…There are prior works that exploit DNNs technique to optimize MAC protocol design using supervised learning [34,38,59,61,62,78,79,81,83] and unsupervised learning techniques [10,14,51,92]. [55] [86].…”

Section: Related Workmentioning

confidence: 99%

Towards A Machine Learning-Based Framework For Automated Design of Networking Protocols

Pasandi

2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Networking protocols are designed through long-time and hard-work human eorts. Machine Learning (ML)-based solutions have been developed for communication protocol design to avoid manual eorts to tune individual protocol parameters. While other proposed ML-based methods mainly focus on tuning individual protocol parameters (e.g., adjusting contention window), our main contribution is to propose a novel Deep Reinforcement Learning (DRL)-based framework to systematically design and evaluate networking protocols. We decouple a protocol into a set of parametric modules, each representing a main protocol functionality that is used as DRL input to better understand the generated protocols design optimization and analyze them in a systematic fashion. As a case study, we introduce and evaluate Deep-MAC a framework in which a MAC protocol is decoupled into a set of blocks across popular avors of 802.11 WLANs (e.g., 802.11 b/a/g/n/ac). We are interested to see what blocks are selected by DeepMAC across dierent networking scenarios and whether DeepMAC is able to adapt to network dynamics.

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Deep Reinforcement Learning MAC for Backscatter Communications Relying on Wi-Fi Architecture

Cited by 14 publications

References 14 publications

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

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

Improving Medium Access Efficiency With Intelligent Spectrum Learning

Towards A Machine Learning-Based Framework For Automated Design of Networking Protocols

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