Papers by Mohammed Hassan
IEEE Access, 2019
Varying channel conditions, dynamic traffic flows, interference and congestion are the main chall... more Varying channel conditions, dynamic traffic flows, interference and congestion are the main challenges to achieve high-throughput data delivery in multi-radio multi-channel Wireless Mesh Networks (WMNs). The performance of existing solutions are limited either for using statically computed end-to-end relay paths or myopic forwarding decisions. In this paper, we consider the problem of high-throughput traffic forwarding that involves good quality link selection, channel allocation and power control at each forwarding router. Every router chooses a set of outgoing link-channel pairs and their power allocations through a mixedinteger-non-linear programming (MINLP) solution that maximizes its sum total outgoing flow rate while keeping interference and congestion at minimum. Since, the MINLP optimization function is an NP-hard one, a Reinforcement learning based system for Link-Channel selection and Power allocation, namely RLCP, is developed. A comprehensive design of the RLCP system has been presented containing portrayal of the system state, design of a reputation metric and a mechanism to learn the control policy. We have carried out exhaustive simulations on NS-3 and found the proposed RLCP system to prove its efficacy in terms of aggregated throughput, flow fairness and packet delivery delay. INDEX TERMS High-throughput, link-channel selection, power allocation, reinforcement learning, wireless mesh networks.
Case Studies in Construction Materials, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Papers by Mohammed Hassan