Interference Mitigation Based on Radio Aware Channel Assignment for Wireless Mesh Networks

Jayaraman, Ramkumar; Raja, Gunasekaran; Chauhdary, Sajjad Hussain; Hassan, Ali; Alqarni, Mohammed A.

doi:10.1007/s11277-018-5776-4

Cited by 13 publications

(8 citation statements)

References 40 publications

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“…Most of those studies aimed at minimizing global network interference. The link interferences were estimated using either protocol [ 21 , 34 ] or physical interference [ 53 ] models. The hypothesis behind those solutions was to reduce global network interference leading to improving the utilization of network capacity, throughput, goodput, and delay, among other desired characteristics [ 3 , 7 , 52 ].…”

Section: Related Workmentioning

confidence: 99%

Fairness‐Oriented Semichaotic Genetic Algorithm‐Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks

Ghaleb

Al‐rimy

Boulila

et al. 2021

Computational Intelligence and Neuroscience

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Wireless mesh networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and Internet of Vehicles (IoV). Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth distribution, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms equally distribute the interference reduction on the links to achieve fairness which neither guarantees a fair distribution of the network bandwidth nor eliminates node starvation. In addition, the metaheuristic-based solutions such as genetic algorithm, which is commonly used for WMN, use randomness in creating initial population and selecting the new generation usually leading the search to local minima. To this end, this study proposes a Fairness-Oriented Semichaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) to solve node starvation problem in wireless mesh networks. FA-SCGA-CAA maximizes link fairness while minimizing link interference using a genetic algorithm (GA) with a novel nonlinear fairness-oriented fitness function. The primary chromosome with powerful genes is created based on multicriterion links ranking channel assignment algorithm. Such a chromosome was used with a proposed semichaotic technique to create a strong population that directs the search towards the global minima effectively and efficiently. The proposed semichaotic technique was also used during the mutation and parent selection of the new genes. Extensive experiments were conducted to evaluate the proposed algorithm. A comparison with related work shows that the proposed FA-SCGA-CAA reduced the potential node starvation by 22% and improved network capacity utilization by 23%. It can be concluded that the proposed FA-SCGA-CAA is reliable to maintain high node-level fairness while maximizing the utilization of the network resources, which is the ultimate goal of many wireless networks.

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Section: Related Workmentioning

confidence: 99%

Fairness‐Oriented Semichaotic Genetic Algorithm‐Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks

Ghaleb

Al‐rimy

Boulila

et al. 2021

Computational Intelligence and Neuroscience

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“…The main advantage of this paper is to mitigate the channel interference to raise the network throughput and the disadvantage of this paper is that it allows more transmission channels among the neighboring nodes, which is leads to decreases the performance of network. Jayaraman, Ramkumar, et al [7] in 2018, has proposed a Radio aware -Channel Allocation (Ra-CA) is proposed in this paper, which is based on the direct graphical model. This method can be used to mitigate the channel interference in Multi -radio Multichannel (MR-MC) wireless mesh network.…”

Section: Related Work: a Brief Reviewmentioning

confidence: 99%

“…The joint link-channel selection and rateallocation (LCR) is introduced for channel allocation in the overall mesh networks, which helps to mitigate the channel interference problem with the minimum rate or minimum power consumption rate [6]. Moreover, a direct graphical model based Radio aware -Channel Allocation (Ra-CA) algorithm is proposed [7]. This method can be used to mitigate the channel interference in Multi -radio Multichannel (MR-MC) wireless mesh network.…”

Section: Introductionmentioning

confidence: 99%

Q-Learning and MADMM Optimization Algorithm Based Interference Aware Channel Assignment Strategy for Load Balancing in WMNS

Rathan¹,

Roslin²

2021

IJIES

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Wireless Mesh Networks (WMNs) have been considered one of the main technologies for configuring wireless machines since they appeared. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts on the network and allow access to the internet through the gateway routers. These wireless routers are normally equipped with the multiple radios in the wireless mesh network that operate on multiple channels with the multiple interference, which is caused to reduce the network performance and end-to-end delay. In this paper, we proposed an efficient optimization algorithm to solve the channel assignment problem which cause due to the multichannel multiradios in WMN’s. The main objective of our paper is to minimize the channel interference among networked devices. So, initially we construct a multicast tree with minimum interference by using Q-Learning algorithm, which is helps to minimize the end-to-end delay of packet delivery. From the constructed multicast tree, we intend to develop a channel assignment strategy with the minimum interference by using Modified version of Alternative Direction Method of Multipliers (MADMM) optimization algorithm, which is helps to increase the network throughput and packet delivery ratio. The proposed strategy was implemented by using NS-2 (Network Simulator-2) and the experimental result show that the performance of the proposed method is very high compared to the other method and the performance was calculated by using the feature metrics such as average throughput, packet delivery ratio, end-toend delay and total cost, which is compared with the other existing channel assignment strategies such as Learning Automata and Genetic Algorithm (LA-GA), GA-based approach, link-channel selection and rate-allocation (LCR) and learning automata based multicast routing (LAMR) channel assignment methods.

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“…When artificial intelligence is deployed on the C‐RAN by integrating HetNets, base stations use dedicated backhaul to provide guaranteed backhaul and small cells deployment offers seamless connectivity to offload the data in traffic. If C‐RAN is automated or made intelligent, then RRH in backhaul connection of RAN can be combined with the BBU pool to create a centralized processing unit …”

Section: Related Workmentioning

confidence: 99%

An optimal multitier resource allocation of cloud RAN in 5G using machine learning

Bashir

Arul

Basheer

et al. 2019

Trans Emerging Tel Tech

Self Cite

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The networks are evolving drastically since last few years in order to meet user requirements. For example, the 5G is offering most of the available spectrum under one umbrella. In this work, we will address the resource allocation problem in fifth-generation (5G) networks, to be exact in the Cloud Radio Access Networks (C-RANs). The radio access network mechanisms involve multiple network topologies that are isolated based on the spectrum bands and it should be enhanced with numerous access technology in the deployment of 5G network. The C-RAN is one of the optimal technique to combine all the available spectral bands. However, existing C-RAN mechanisms lacks the intelligence perspective on choosing the spectral bands. Thus, C-RAN mechanism requires an advanced tool to identify network topology to allocate the network resources for substantial traffic volumes. Therefore, there is a need to propose a framework that handles spectral resources based on user requirements and network behavior. In this work, we introduced a new C-RAN architecture modified as multitier Heterogeneous Cloud Radio Access Networks in a 5G environment. This architecture handles spectral resources efficiently. Based on the simulation analysis, the proposed multitier H-CRAN architecture with improved control unit in network management perspective enables augmented granularity, end-to-end optimization, and guaranteed quality of service by 15 percentages over the existing system.Trans Emerging Tel Tech. 2019;30:e3627.wileyonlinelibrary.com/journal/ett spectrum like WiFi. 2 The advanced antennas with multiple input and multiple output technologies can be adopted in the evolving 5G to provide higher data rate. 3,4 The colossal requirements of future 5G networks cannot be accomplished by the existing radio access networks (RANs), wherein the base band units (BBUs) and radio units are consolidated. 2 Meanwhile, the cloud-enabled services in 5G ecosystem cater heterogeneous communication framework utilizing advanced virtualization techniques. The network virtualization stimulates multiservice and multitenancy for efficient network operations and service provisions, which, in turn, offers new-fangled service-oriented and edge-cloud 5G architectures with enhanced Quality of Experience (QoE). 5 Thus, the current RAN architectures need to be further extended with various virtualization techniques to develop Cloud RAN (C-RAN). Cloud RANs abutments the correlation of Access Points (APs) to the pool of BBU via a troop of transport links in the Control Unit (CU), 6 which conquers the limitations in the traditional RAN. 7 However, C-RAN requires an intelligent technique to recognize the topology of the network in the mobility stage and to locate the resources, as the vast traffic volume created by the sampled radio signals are directly transported to the CU. 8 The next-generation networks are expected to learn consistently based on the varied user behavior and spectral stability that depends on both the user's environment and network behavior. 9 Thereby, sea...

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Interference Mitigation Based on Radio Aware Channel Assignment for Wireless Mesh Networks

Cited by 13 publications

References 40 publications

Fairness‐Oriented Semichaotic Genetic Algorithm‐Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks

Fairness‐Oriented Semichaotic Genetic Algorithm‐Based Channel Assignment Technique for Node Starvation Problem in Wireless Mesh Networks

Q-Learning and MADMM Optimization Algorithm Based Interference Aware Channel Assignment Strategy for Load Balancing in WMNS

An optimal multitier resource allocation of cloud RAN in 5G using machine learning

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