Resource Allocation for Hybrid RF/FSO Multi-Channel Multi-Radio Wireless Mesh Networks

Zhao, Yan; Shi, Wenxiao; Shi, Hanyang; Liu, Wei; Wang, Zhuo; Zhang, Jiadong

doi:10.1109/access.2020.2965081

Cited by 10 publications

(4 citation statements)

References 41 publications

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“…In [240], a heuristicbased backhaul link selection scheme to minimize cost under data rate, connectivity, and reliability constraints was proposed for an Int-RF/FSO multi-band backhaul network. In [241], the authors developed an RF/FSO multi-band wireless mesh network and focused on sum rate optimization via route selection, topology control, channel allocation, and interface assignment under flow conservation, routing, FSO link allocation, logical topology, link capacity, delay, and fairness constraints. Due to the nonconvex nature of the optimization problem, the authors relied on the commercial solver Gurobi and metaheuristics to propose an iterative local search algorithm.…”

Section: Rf/fso Mbnsmentioning

confidence: 99%

Multi-Band Wireless Communication Networks: Fundamentals, Challenges, and Resource Allocation

Aboagye,

Amin Saeidi,

Tabassum

et al. 2024

IEEE Trans. Commun.

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The sub-6 GHz spectrum and the millimeter wave frequency band, with its huge spectrum, have been exploited in the past years to meet the traffic demands of wireless communication networks. However, the limited and licensed spectrum of these bands cannot support the massive connectivity requirements, the exponential growth of data traffic, and the strict quality-of-service requirements for 6G and beyond wireless systems. Extremely high frequencies, such as optical and terahertz, which offer much wider transmission bandwidths with extreme data rate capabilities, are expected to play key roles in the 6G and beyond era. As a result, futuregeneration wireless networks will transition from single-band and heterogeneous networks to multi-band networks (MBNs), where various frequency bands coexist. Despite the great potential of MBNs, they face novel challenges from channel modeling, transceiver and antenna design, programmable simulation platforms, standardization activities, and resource allocation. This paper provides a tutorial overview from the communication design perspective of the various frequency bands, elaborating on the above issues. Then, we introduce and examine typical MBN architectures for future networks and provide a detailed overview of state-of-the-art resource allocation problems for existing MBNs that typically operate on two frequency bands. The considered resource allocation optimization problems and solution techniques are discussed comprehensively. We then identify key performance metrics and constraint sets that should be considered for resource allocation optimization in future MBNs and provide numerical results to depict how various system parameters and user behaviors can influence their performance. Finally, we present several potential research issues as future work for the design and performance optimization of MBNs.

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Section: Rf/fso Mbnsmentioning

confidence: 99%

Multi-Band Wireless Communication Networks: Fundamentals, Challenges, and Resource Allocation

Aboagye,

Amin Saeidi,

Tabassum

et al. 2024

IEEE Trans. Commun.

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“…The client coverage α(G) is given by (11). In addition, for the purpose of creating the fitness function, the connectivity factor between the routers is considered and denoted as θ(G) and given by (12). Therefore, the weighted sum method is used, which turns a multi-objective problem into a single-objective problem by adding up each goal and multiplying it by a weight set by the user.…”

Section: ) the Fitness Functionmentioning

confidence: 99%

“…The challenge of effectively deploying WMNs might, in some respects, be understood as an issue pertaining to the location of the facilities involved [11], [12], [13], [14]. Prior research conducted over a long time period outlined and confirmed the NP-hardness of this subject [13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

et al. 2023

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The optimal performance of a wireless mesh network (WMN) can be greatly improved by strategically placing wireless mesh routers. As a result, it is crucial to optimally locate the WMN routers for better coverage and connectivity. Besides the optimal placement, the network congestion due to overlaying routers has to be taken into consideration. These issues have become a motivation for researchers to identify a variety of approaches to optimize WMN performance. Multiple metaheuristic algorithms have been employed for identifying the trade-offs between coverage and connectivity in WMN. Consequently, a novel hybrid Harris Hawks optimization with the sine cosine algorithm (HHOSCA) is presented in this work to tackle the aforementioned WMN optimization problems. The proposed HHOSCA seeks optimal router placement that leads to significantly increased network coverage and achieves full connectivity between the mesh routers. In addition, the proposed HHOSCA produces a cost-effective WMN by reducing the congestion in the network to the minimum number of routers whilst ensuring maximum coverage and connectivity. The superiority of the proposed HHOSCA in comparison to the other algorithm was validated by using 33 benchmark functions. It was compared against four well-known algorithms including Sine Cosine Algorithm (SCA), Harris Hawks optimization (HHO), Gray Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are statistically analyzed and compared to the simulated results of the proposed method. In addition, the performance of HHOSCA is compared to the state-of-the-art to highlight the efficacy of the proposed algorithm. The statistical analyses and simulation findings confirm that the HHOSCA outperforms the other algorithms in terms of network connectivity, coverage, network reduction, and convergence. The experimental results reveal that the proposed HHOSCA method achieves favourable optimization results compared with other relevant methods.INDEX TERMS Optimal node placement, reliable wireless networks, network deployment optimization, particle swarm optimization, gray wolf optimization, Harris Hawks optimization, sine cosine optimization, industrial wireless mesh networks.The associate editor coordinating the review of this manuscript and approving it for publication was Xiaolong Li .

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“…They obtained lower bounds on the overall network interference by considering the channel assignment problem as a semi-definite program and a linear programming formulation of their optimization problem. Y. Zhao et al focused on resource allocation in WMNs considering multi-channel and multi-radio approaches [ 21 ]. They proposed an optimization model which formulates each phase as a mixed integer linear problem.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Intelligent Simulation System for Building IoT Networks: Performance Comparison of Different Router Replacement Methods for WMNs Considering Stadium Distribution of IoT Devices

Barolli

Sakamoto

Bylykbashi

et al. 2022

Sensors

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As the Internet of Things (IoT) devices and applications proliferate, it becomes increasingly important to design robust networks that can continue to meet user demands at a high level. Wireless local area networks (WLANs) can be a good choice as IoT infrastructure when high throughput is required. On the other hand, wireless mesh networks (WMNs), which are WLANs with mesh topology following the IEEE802.11s standard, have many advantages compared to conventional WLANs. Nevertheless, there are some problems that need solutions. One of them is the node placement problem. In this work, we propose and implement a hybrid intelligent system that solves this problem by determining the position of mesh nodes by maximizing the mesh connectivity and the coverage of IoT devices. The system is based on particle swarm optimization (PSO), simulated annealing (SA), and distributed genetic algorithm (DGA). We compare the performance of three router replacement methods: constriction method (CM), random inertia weight method (RIWM), and rational decrement of Vmax method (RDVM). The simulation results show that RIWM achieves better performance compared to CM and RDVM because it achieves the highest connectivity while covering more clients than the other two methods.

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Resource Allocation for Hybrid RF/FSO Multi-Channel Multi-Radio Wireless Mesh Networks

Cited by 10 publications

References 41 publications

Multi-Band Wireless Communication Networks: Fundamentals, Challenges, and Resource Allocation

Multi-Band Wireless Communication Networks: Fundamentals, Challenges, and Resource Allocation

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

A Hybrid Intelligent Simulation System for Building IoT Networks: Performance Comparison of Different Router Replacement Methods for WMNs Considering Stadium Distribution of IoT Devices

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