On Topology Optimization and Routing in Integrated Access and Backhaul Networks: A Genetic Algorithm-Based Approach

Madapatha, Charitha; Makki, Behrooz; Muhammad, Ajmal; Dahlman, Erik; Alouini, Mohamed‐Slim; Svensson, Tommy

doi:10.1109/ojcoms.2021.3114669

Cited by 25 publications

(8 citation statements)

References 55 publications

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“…GA is used in the proposed scheme for the optimization problem along with achievement of both the satisfactory service and user delays. In [71], sensor node placement in integrated access and backhaul (IAB) network and link distribution in non-IAB network is studied which uses GA based approaches. This study also evaluates the bypass of temporal blockages by considering the effect of routing mechanisms.…”

Section: ) Genetic Algorithm (Ga)mentioning

confidence: 99%

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A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

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The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.

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Section: ) Genetic Algorithm (Ga)mentioning

confidence: 99%

“…Optimization of server deployment and user offloading with satisfactory delay requirements [71] Optimum sensor node deployment and routing Optimum network coverage [72] Routing optimization by selecting optimum sensor nodes…”

Section: Algorithmsmentioning

confidence: 99%

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

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“…Network deployment, routing and topology adaptation were studied in [14], [28], [32], [35]- [48]. In general, for a broad range of coverage constraints/blockage densities, the impact of meshed communication to increase redundancy may be negligible if the network is well planned.…”

Section: ) Network Deployment Routing and Topology Adaptationmentioning

confidence: 99%

Paving the Way Toward Mobile IAB: Problems, Solutions and Challenges

Monteiro

Lima

Moreira

et al. 2022

IEEE Open J. Commun. Soc.

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Deploying access and backhaul as wireless links, a.k.a. integrated access and backhaul (IAB), is envisioned as a viable approach to enable flexible and dense networks. Even further, mobile IAB (mIAB) is a candidate solution to enhance the connectivity of multiple user equipment (UE) moving together. In this context, different of other works from the literature, the present work overviews the basis for the deployment of mIAB by presenting: 1) the current status of IAB standardization in the fifth generation (5G) new radio (NR); 2) a new taxonomy for state-of-the-art works regarding fixed IAB and mIAB; 3) an extensive performance analysis of mIAB based on simulation results; and 4) open challenges and potential future prospects of mIAB. Specifically, the proposed taxonomy classifies IAB works according to different perspectives and categorizes mIAB works according to the type of mobile node. For each type of mobile node, the main studied topics are presented. Regarding the performance evaluation, we consider an urban macro scenario where mIAB nodes are deployed in buses in order to improve the passengers' connection. The results show that, compared to other network architectures, the deployment of mIAB nodes remarkably improves the passengers' throughput and latency in both downlink and uplink.

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“…As a result, to satisfy throughput and latency targets, efficient resource utilization at the IAB node is of utmost importance and has led to a variety of studies on route selection [9], [18], [19], link scheduling [8], [20], load balancing [21], and topology optimization [11], [22]. In [8], for latency gain.…”

Section: A Motivation Background and Related Workmentioning

confidence: 99%

System-Level Analysis of Full-Duplex Self-Backhauled Millimeter Wave Networks

Gupta¹,

Roberts²,

Andrews³

2021

Preprint

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Integrated access and backhaul (IAB) facilitates cost-effective deployment of millimeter wave (mmWave) cellular networks through multihop self-backhauling. Full-duplex (FD) technology, particularly for mmWave systems, is a potential means to overcome latency and throughput challenges faced by IAB networks. We derive practical and tractable throughput and latency constraints using queueing theory and formulate a network utility maximization problem to evaluate both FD-IAB and half-duplex (HD)-IAB networks. We use this to characterize the network-level improvements seen when upgrading from conventional HD IAB nodes to FD ones by deriving closed-form expressions for (i) latency gain of FD-IAB over HD-IAB and (ii) the maximum number of hops that a HD-and FD-IAB network can support while satisfying latency and throughput targets. Extensive simulations illustrate that FD-IAB can facilitate reduced latency, higher throughput, deeper networks, and fairer service. Compared to HD-IAB, FD-IAB can improve throughput by 8× and reduce latency by 4× for a fourth-hop user. In fact, upgrading IAB nodes with FD capability can allow the network to support latency and throughput targets that its HD counterpart fundamentally cannot meet. The gains are more profound for users further from the donor and can be achieved even when residual self-interference is significantly above the noise floor.

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On Topology Optimization and Routing in Integrated Access and Backhaul Networks: A Genetic Algorithm-Based Approach

Cited by 25 publications

References 55 publications

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

Paving the Way Toward Mobile IAB: Problems, Solutions and Challenges

System-Level Analysis of Full-Duplex Self-Backhauled Millimeter Wave Networks

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