Joint optimization of physical layer parameters and routing in wireless mesh networks

Tobagi, Fouad A.; Hira, Mukesh

doi:10.1109/medhocnet.2010.5546855

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…10 shows the result when the distance is set to 20 meters, and we observed a similar trend: the throughput gap between the Minstrel algorithm and the throughputoptimizing and throughput and energy-optimizing agents further widens to about 500Mbps and 400Mbps, respectively. Figures 11,12, and 13 are the results when we introduce a second jammer to the network topology. In Fig.…”

Section: Testing Resultsmentioning

confidence: 99%

“…In addition to power and carrier sense management, when rate control is taken into consideration, it introduces a trade-off between spatial reuse and the transmission rate that can be sustained [12]. A new idea, spatial back-off, was introduced in [13], which allows for dynamic tweaking of the carrier sensing threshold in conjunction with the Auto-Rate Fallback (ARF) algorithm in order to achieve high throughput.…”

Section: Introductionmentioning

confidence: 99%

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Optimal wireless rate and power control in the presence of jammers using reinforcement learning

Raji¹,

Liu²

2022

ITU J-FET

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Future wireless networks require high throughput and energy efficiency. This paper studies using Reinforcement Learning (RL) to do transmission rate and power control for maximizing a joint reward function consisting of both throughput and energy consumption. We design the system state to include factors that reflect packet queue length, interference from other nodes, quality of the wireless channel, battery status, etc. The reward function is normalized and does not involve unit conversion. It can be used to train three different types of agents: throughput-critical, energy-critical, and throughput and energy balanced. Using the NS-3 network simulation software, we implement and train these agents in an 802.11ac network with the presence of a jammer. We then test the agents with two jamming nodes interfering with the packets received at the receiver. We compare the performance of our RL optimal policies with the popular Minstrel rate adaptation algorithm: our approach can achieve (i) higher throughput when using the throughput-critical reward function; (ii) lower energy consumption when using the energy-critical reward function; and (iii) higher throughput and slightly higher energy when using the throughput and energy balanced reward function. Although our discussion is focused on 802.11ac networks, our method is readily applicable to other types of wireless networks.

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Section: Testing Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal wireless rate and power control in the presence of jammers using reinforcement learning

Raji¹,

Liu²

2022

ITU J-FET

View full text Add to dashboard Cite

show abstract

“…New trade-offs are introduced when rate control is considered in addition to power and carrier sense control [22], [23]. Here, it becomes necessary to understand the trade-off between spatial reuse and the rate that can be supported.…”

Section: Related Workmentioning

confidence: 99%

Practical Power and Rate Control for WiFi

Huehn

Sengul

2012

2012 21st International Conference on Computer Communications and Networks (ICCCN)

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While there has been extensive theoretical work on sophisticated joint resource allocation algorithms for wireless networks, their applicability to WiFi (IEEE 802.11) networks is very limited. One of the main reasons is the limitations in changing MAC parameters in current driver implementations. To this end, in this work, we developed a general cross-layer communication interface in the Linux kernel between the IEEE 802.11 PHY and MAC to enable per packet TPC. Based on this implementation, we realize an decentralized rate-power controller (Minstrel-Piano). Our initial evaluation shows that Minstrel-Piano is able to significantly decrease the power levels while maintaining the same link performance. These results are encouraging for a better interference management and consequently, better resource allocation in WiFi networks.

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Network Coded MIMO Aided Cooperative Communications in the Ambulance-and-emergency Area

Nguyen

Babar

et al. 2014

Procedia Computer Science

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Joint optimization of physical layer parameters and routing in wireless mesh networks

Cited by 8 publications

References 7 publications

Optimal wireless rate and power control in the presence of jammers using reinforcement learning

Optimal wireless rate and power control in the presence of jammers using reinforcement learning

Practical Power and Rate Control for WiFi

Network Coded MIMO Aided Cooperative Communications in the Ambulance-and-emergency Area

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