Multi-rate medium access protocol based on reinforcement learning

Al-Saadi, Ahmed; Setchi, Rossitza; Hicks, Yulia; Allen, Stuart M.

doi:10.1109/smc.2014.6974366

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…and channel errors in case of transmission failure. The work in [89] presents a new RL to adapt the rate of transmission for the dynamic conditions of WMNs referred to as (RARE). The proposed algorithm uses the access probability of the wireless channel to determine if the transmission rate will be updated or not.…”

Section: Planning Actionsmentioning

confidence: 99%

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Hussien¹

2023

Preprint

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<p>The last decade, (2012 - 2022), saw an unprecedented advance in machine learning (ML) techniques, particularly deep learning (DL). As a result of the proven capabilities of DL, a large amount of work has been presented and studied in almost every field. Since 2012, when the convolution neural networks have been reintroduced in the context of \textit{ImagNet} competition, DL continued to achieve superior performance in many challenging tasks and problems. Wireless communications, in general, and medium access control (MAC) techniques, in particular, were among the fields that were heavily affected by this improvement. MAC protocols play a critical role in defining the performance of wireless communication systems. At the same time, the community lacks a comprehensive survey that collects, analyses, and categorizes the recent work in ML-inspired MAC techniques. In this work, we fill this gap by surveying a long line of work in this era. We solidify the impact of machine learning on wireless MAC protocols. We provide a comprehensive background to the widely adopted MAC techniques, their design issues, and their taxonomy, in connection with the famous application domains. Furthermore, we provide an overview of the ML techniques that have been considered in this context. Finally, we augment our work by proposing some promising future research directions and open research questions that are worth further investigation.</p>

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Section: Planning Actionsmentioning

confidence: 99%

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Hussien¹

2023

Preprint

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“…In this paper, the WMN uses a recently proposed rate adaptation algorithm based on reinforcement learning (RARE) [41]. It has been developed for multi-hop WMNs where the nodes that are competing to access the shared channel are considered in the calculation of the transmission rate.…”

Section: B Fuzzy-based Knowledge Basementioning

confidence: 99%

Semantic Reasoning in Cognitive Networks for Heterogeneous Wireless Mesh Systems

Al-Saadi

Setchi

Hicks

2017

IEEE Trans. Cogn. Commun. Netw.

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The next generation of wireless networks is expected to provide not only higher bandwidths anywhere and at any time but also ubiquitous communication using different network types. However, several important issues including routing, selfconfiguration, device management, and context awareness have to be considered before this vision becomes reality. This paper proposes a novel cognitive network framework for heterogeneous wireless mesh systems to abstract the network control system from the infrastructure by introducing a layer that separates the management of different radio access networks from the data transmission. This approach simplifies the process of managing and optimizing the networks by using extendable smart middleware that automatically manages, configures, and optimizes the network performance. The proposed cognitive network framework, called FuzzOnto, is based on a novel approach that employs ontologies and fuzzy reasoning to facilitate the dynamic addition of new network types to the heterogeneous network. The novelty is in using semantic reasoning with cross-layer parameters from heterogeneous network architectures to manage and optimize the performance of the networks. The concept is demonstrated through the use of three network architectures: 1) wireless mesh network; 2) long-term evolution (LTE) cellular network; and 3) vehicular ad hoc network (VANET). These networks utilize nonoverlapped frequency bands and can operate simultaneously with no interference. The proposed heterogeneous network was evaluated using ns-3 network simulation software. The simulation results were compared with those produced by other networks that utilize multiple transmission devices. The results showed that the heterogeneous network outperformed the benchmark networks in both urban and VANET scenarios by up to 70% of the network throughput, even when the LTE network utilized a high bandwidth.

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“…These rate adaptation algorithms are developed for infrastructurebased wireless networks and not for WMNs, and therefore, they do not consider the competing nodes accessing shared channels. The recently developed rate adaptation algorithm based on reinforcement learning (RARE) [22] sets the transmission rate based on the link quality of the neighboring nodes and the load on the Wi-Fi device. Thus, the transmission rate estimates the amount of interference and collision with other nodes and the load on the node.…”

Section: B Routing Metrics In Wmnsmentioning

confidence: 99%

“…In case a HetNode selects a Wi-Fi device, it uses the routing table to send the packets to the next hop on the path of the selected mesh gateway. CHR adopts a multirate medium access control (MAC) protocol for 802.11 called RARE [22]. RARE was developed for a WMN-only environment to consider the collision and interference in the neighboring nodes.…”

Section: B Chr Algorithmmentioning

confidence: 99%

“…is the Wi-Fi channel quality for node d at time t i , and RW d (t i ) is the transmission rate for the Wi-Fi device at node d at time t i . According to RARE [22], which is the rate adaptation algorithm adopted by CHR, the node increases the transmission rate if the estimated interference in the neighboring nodes is low. Thus, a higher transmission rate means lower interference on the node and higher probability of sending the packets successfully.…”

Section: Q(t Imentioning

confidence: 99%

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Routing Protocol for Heterogeneous Wireless Mesh Networks

Al-Saadi

Setchi

Hicks

et al. 2016

IEEE Trans. Veh. Technol.

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The introduction of heterogeneous wireless mesh technologies provides an opportunity for higher network capacity, wider coverage, and higher quality of service (QoS). Each wireless device utilizes different standards, data formats, protocols, and access technologies. However, the diversity and complexity of such technologies create challenges for traditional control and management systems. This paper proposes a heterogeneous metropolitan area network architecture that combines an IEEE 802.11 wireless mesh network (WMN) with a long-term evolution (LTE) network. In addition, a new heterogeneous routing protocol and a routing algorithm based on reinforcement learning called cognitive heterogeneous routing are proposed to select the appropriate transmission technology based on parameters from each network. The proposed heterogeneous network overcomes the problems of sending packets over long paths, island nodes, and interference in WMNs and increases the overall capacity of the combined network by utilizing unlicensed frequency bands instead of buying more license frequency bands for LTE. The work is validated through extensive simulations that indicate that the proposed heterogeneous WMN outperforms the LTE and Wi-Fi networks when used individually. The simulation results show that the proposed network achieves an increase of up to 200% in throughput compared with Wi-Fi-only networks or LTE-only networks.

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Multi-rate medium access protocol based on reinforcement learning

Cited by 9 publications

References 12 publications

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

Semantic Reasoning in Cognitive Networks for Heterogeneous Wireless Mesh Systems

Routing Protocol for Heterogeneous Wireless Mesh Networks

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