Deep Learning for Optimized Multiuser OFDMA Energy-efficient Wireless Transmission

Xing, Yuan; Qian, Yuchen; Shi, Wei; Liu, Xuejun; Zhao, T.; Tapparello, Cristiano

doi:10.1109/ccwc51732.2021.9376055

Cited by 2 publications

(5 citation statements)

References 8 publications

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“…The work in [130] uses a DL approach to optimise cell sleeping and beamforming, showing great improvements in performance. The authors in [131] utilises DL tools to subchannel assignment and power allocation strategies to optimise energy efficiency while maintaining bitrate conditions for the users. Also in [132], DL is used to evaluate the quality of candidate radio bands based on the spatial and spectral correlation between radio frequency signals.…”

Section: A Technology Improvementsmentioning

confidence: 99%

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Larsen

Christiansen

Ruepp

et al. 2023

IEEE Open J. Commun. Soc.

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Mobile network traffic is increasing and so is the energy consumption. The Radio Access Network (RAN) part is responsible for the largest share of the mobile network energy consumption, and thus; an important consideration when expanding mobile networks to meet traffic demands. This work analyses how the energy consumption of future mobile networks can be minimised by using the right RAN architecture, share the network with other operators and implementing the most efficient energy minimising technologies in the RAN. It is explored how the different approaches can be realised in real life networks as well as the research state of the art is highlighted. Furthermore, this work provides an overview of future research directions for 6G energy saving potentials. Different energy saving contributions are evaluated by a common methodology for more realistic comparison, based on the potential energy saving of the overall mobile network consumption. Results show that implementing selected technologies and architectures, the mobile network overall energy consumption can be reduced by approximately 30%, corresponding to almost half of the RAN energy consumption. Following this, a set of guidelines towards an energy optimised mobile network is provided, proposing changes to be made initially and in the longer run for brownfield network operators as well as a target network for greenfield network operators.

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Section: A Technology Improvementsmentioning

confidence: 99%

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Larsen

Christiansen

Ruepp

et al. 2023

IEEE Open J. Commun. Soc.

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show abstract

“…(i) based on the Shannon formula considered among others in [62]- [69]. In [61]- [67], [70] the subcarriers are considered independently (are not grouped into RBs), thus the resource allocation is determined per subcarrier. Whereas in [68], [69] the subcarriers are grouped into resource blocks as in some practical wireless communication systems, e.g., LTE.…”

Section: A Transmission Rate Estimation In a Multi-user Ofdma Networkmentioning

confidence: 99%

“…• 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 subcarrier/resource block allocation constraint examined in [61]- [67], [69], [70], [72]- [74], [76]. This constraint guarantees that a given subcarrier/resource block can be assigned to maximally one user, in order to avoid the inter-user interference.…”

Section: Constraints In a Multi-user Ofdma Networkmentioning

confidence: 99%

“…Another suboptimal methods have been proposed in [61], [63], [64], [72]. A suboptimal method based on deep learning is proposed in [70]. Nevertheless, the review of all proposed methods is not the goal of this paper because these depend on the system model and do not have universal nature.…”

Section: Ee Optimization In a Multi-user Ofdma Systemmentioning

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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Deep Learning for Optimized Multiuser OFDMA Energy-efficient Wireless Transmission

Cited by 2 publications

References 8 publications

Toward Greener 5G and Beyond Radio Access Networks—A Survey

Toward Greener 5G and Beyond Radio Access Networks—A Survey

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

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