Energy Efficient Resource Allocation for 5G Heterogeneous Networks Using Genetic Algorithm

Qi, Xiaomin; Khattak, Shahid; Zaib, Alam; Khan, Imdad

doi:10.1109/access.2021.3131823

Cited by 26 publications

(13 citation statements)

References 26 publications

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“…On the other hand, the proposed algorithm introduces delays that could cause difficulties in the deployment of the scheme in real-world scenarios. In the same context, authors in [61] examine the influence of intercell interference in the design of effective RRM strategies. More specifically, they formulated an EE maximization RRM problem for a downlink OFDMA HetNet, and solved it via a two-step generic algorithm.…”

Section: B Representative Recent Non-ml Approachesmentioning

confidence: 99%

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

et al. 2022

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In this survey, a comprehensive study is provided, regarding the use of machine learning (ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) wireless cellular networks. The ever-increasing user requirements, their diverse nature in terms of performance metrics and the use of various novel technologies, such as millimeter wave transmission, massive multipleinput-multiple-output configurations and non-orthogonal multiple access, render the multi-constraint nature of the radio resource management (RRM) problem. In this context, ML and mobile edge computing (MEC) constitute a promising framework to provide improved quality of service (QoS) for end users, since they can relax the RMM-associated computational burden. In our work, a state-of-the-art analysis of ML-based RRM algorithms, categorized in terms of learning type and potential applications as well as MEC implementations,is presented, to define the best-performing solutions for various RRM sub-problems. To demonstrate the capabilities and efficiency of ML-based algorithms in RRM, we apply and compare different ML approaches for throughput prediction, as an indicative RRM task. We investigate the problem, either as a classification or as a regression one, using the corresponding metrics in each occasion. Finally, open issues, challenges and limitations concerning AI/ML approaches in RRM for 5G and B5G networks, are discussed in detail.

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Section: B Representative Recent Non-ml Approachesmentioning

confidence: 99%

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

et al. 2022

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“…• the maximum transmission power constraint which has been considered in [61]- [67], [70]- [72], [74]- [76]. In the case of the downlink transmission this constraint ensures that the sum of transmission powers allocated in the base station is less than or equal to the maximum allowed value.…”

Section: Constraints In a Multi-user Ofdma Networkmentioning

confidence: 99%

“…• the minimum data rate constraint considered in [61]- [65], [67], [72], [74]- [76]. In both (downlink and uplink) scenario it means that the data rate of a given user has to be not smaller than assumed value.…”

Section: Constraints In a Multi-user Ofdma Networkmentioning

confidence: 99%

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Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey

2022

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In this paper, we review radio resource optimization methods for energy-efficient wireless communication in links and networks using the Orthogonal Frequency Division Multiplexing (OFDM) and Orthogonal Frequency Division Multiple Access (OFDMA) techniques. We first consider the energyefficiency metrics and optimization goals. We discuss the increasingly complex systems, starting from (i) a single OFDM link, (ii) an OFDMA single-hop network to (iii) multi-hop relay OFDMA interference networks.In each case, we elaborate on the transmission rate estimation, power consumption modelling, existing optimization constraints and the optimization solutions. Specifically, in the power-consumption modelling, we include the signal-processing (and related computing) power. We discuss the practicality of the considered solutions. We also touch upon the problem of nonlinear power amplifier characteristics (causing distortions typical for OFDM signals) to be taken into account for energy-efficient resource allocation. We discuss trade-offs and provide recommendations for future energy-efficient OFDM networks design. We also discuss the future works and challenges in the context of energy efficiency resource allocation for OFDM/OFDMA and their derivative techniques. We conclude that the presented design practices should include computational awareness in the networks to trade-off between information communication, information processing and the required network management energy-efficiency.

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“…B ROADBAND cellular networks based on the current 4G-Long Term Evolution (LTE) or the emerging 5G-New Radio (NR) wireless standards use universal frequency reuse or reuse-1 [1], [2]. In such OFDM/OFDMA blockmodulated networks, all cell towers and sectors use the same frequency resource 1 to provide a higher sum throughput.…”

Section: Introductionmentioning

confidence: 99%

Interference-Aware Accurate Signal Recovery in Sub-1 GHz UHF Band Reuse-1 Cellular OFDMA Downlinks

Giridhar

2022

IEEE Open J. Commun. Soc.

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Reuse-1 systems operating in the sub-1 GHz UHF band are limited by substantial co-channel interference (CCI). In such orthogonal frequency division multiple access (OFDMA) cellular systems, the inter-sector or inter-tower interference (ITI) makes accurate signal recovery quite challenging as sub-1 GHz bands only support single-input single-output (SISO) links. Interference-aware receiver algorithms are essential to mitigate the ITI in such low-frequency bands. Such algorithms enable ubiquitous mobile broadband access over the entire homeland, say with ą 95% geographical coverage with quality of service guarantees. One element of the interference-aware signal recovery is the least-squares-based joint channel estimation scheme that uses non-orthogonal pilot subcarriers. This estimator is then compared with a variant that uses orthogonal pilot subcarriers to bring out the advantage of this joint estimator. It is shown that the proposed joint estimator requires fewer pilots to be well-determined when compared to its under-determined orthogonal counterpart. Moreover, it is easy to implement and does not require any knowledge of channel statistics. This work also derives a compensation factor needed for the interference-aware detector in the presence of inter-carrier interference (ICI) originating from multiple transmitters. Simulation results show that the proposed joint channel estimator outperforms traditional estimators at moderate to high frequency selectivity. The proposed compensation factor to the joint detector is found to be essential for recovering the transmitted signal in the absence of phase-tracking pilots.

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Energy Efficient Resource Allocation for 5G Heterogeneous Networks Using Genetic Algorithm

Cited by 26 publications

References 26 publications

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey

Interference-Aware Accurate Signal Recovery in Sub-1 GHz UHF Band Reuse-1 Cellular OFDMA Downlinks

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