Energy-Efficient Joint Resource Allocation With Reconfigurable Intelligent Surfaces in Symbiotic Radio Networks

Li, Jing; Li, Xin; Bi, Yong; Ma, Jing

doi:10.1109/tccn.2022.3188986

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…In [137], the authors develop a multi-armed bandit-based algorithm to increase fairness while maintaining the performance gain in RIM-assisted networks. In [138], an energy-efficient RA method is proposed for a RIM-assisted symbiotic radio communication network. The method aims to improve EE and decrease outage by jointly optimizing the power allocation for primary users and PBF for RIMs.…”

Section: Conventional Optimizationmentioning

confidence: 99%

A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks

Das,

Champagne,

Psaromiligkos

et al. 2024

IEEE Open J. Commun. Soc.

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Wireless networks are increasingly relying on machine learning (ML) paradigms to provide various services at the user level. Yet, it remains impractical for users to offload their collected data set to a cloud server for centrally training their local ML model. Federated learning (FL), which aims to collaboratively train a global ML model by leveraging the distributed wireless computation resources across users without exchanging their local information, is therefore deemed as a promising solution for enabling intelligent wireless networks in the data-driven society of the future. Recently, reconfigurable intelligent metasurfaces (RIMs) have emerged as a revolutionary technology, offering a controllable means for increasing signal diversity and reshaping transmission channels, without implementation constraints traditionally associated with multiantenna systems. In this paper, we present a comprehensive survey of recent works on the applications of FL to RIM-aided communications. We first review the fundamental basis of FL with an emphasis on distributed learning mechanisms, as well as the operating principles of RIMs, including tuning mechanisms, operation modes, and deployment options. We then proceed with an in-depth survey of literature on FL-based approaches recently proposed for the solution of three key interrelated problems in RIM-aided wireless networks, namely: channel estimation (CE), passive beamforming (PBF) and resource allocation (RA). In each case, we illustrate the discussion by introducing an expanded FL (EFL) framework in which only a subset of active users partake in the distributed training process, thereby allowing to reduce transmission overhead. Lastly, we discuss some current challenges and promising research avenues for leveraging the full potential of FL in future RIM-aided extremely large-scale multiple-input-multiple-output (XL-MIMO) networks.

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Section: Conventional Optimizationmentioning

confidence: 99%

A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks

Das,

Champagne,

Psaromiligkos

et al. 2024

IEEE Open J. Commun. Soc.

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“…First, the complexity of solving problem P N0 is O(|N 0 |). Second, as mentioned above, the complexity of Algorithm 1 is O( n∈N1 |M opt n |), and the complexity of Algorithm 2 is O((3|N 1 |) 3.5 ) by the interior-point method according to [33]. When we use Algorithm 1 for solving P N1 and use Search-based algorithm for optimizing offloading policy, the computational complexity is O(2 N n∈N |M opt n |).…”

Section: Algorithm Computational Complexity Analysismentioning

confidence: 99%

Joint Optimization of Video-Based AI Inference Tasks in MEC-Assisted Augmented Reality Systems

Pan

Zhang

et al. 2023

IEEE Trans. Cogn. Commun. Netw.

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The high computational complexity and energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. However, mobile edge computing (MEC) makes it possible to solve this problem. This paper considers the scene of completing video-based AI inference tasks in the MEC system. We formulate a mixed-integer nonlinear programming problem (MINLP) to reduce inference delays, energy consumption and to improve recognition accuracy. We give a simplified expression of the inference complexity model and accuracy model through derivation and experimentation. The problem is then solved iteratively by using alternating optimization. Specifically, by assuming that the offloading decision is given, the problem is decoupled into two sub-problems, i.e., the resource allocation problem for the devices set that completes the inference tasks locally, and that for the devices set that offloads tasks. For the problem of offloading decision optimization, we propose a Channel-Aware heuristic algorithm. To further reduce the complexity, we propose an alternating direction method of multipliers (ADMM) based distributed algorithm. The ADMMbased algorithm has a low computational complexity that grows linearly with the number of devices. Numerical experiments show the effectiveness of proposed algorithms. The trade-off relationship between delay, energy consumption, and accuracy is also analyzed.

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“…However, it is worth noting that there are only very limited works on EE study for RIS-BD based SR communication systems. A RIS-assisted SR communication network with multiple primary users (PUs) and multiple clusters of IoT devices linked with a RIS is considered in [32].…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Energy-Efficiency Region of Symbiotic Radio Communications

Wang

Zeng

2022

2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)

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Symbiotic radio (SR) is a promising technology of spectrum-and energy-efficient wireless systems, for which the key idea is to use cognitive backscattering communication to achieve mutualistic spectrum and energy sharing with passive backscatter devices (BDs). In this paper, a reconfigurable intelligent surface (RIS) based SR system is considered, where the RIS is used not only to assist the primary active communication, but also for passive communication to transmit its own information. For the considered system, we investigate the EE trade-off between active and passive communications, by characterizing the EE region. To gain some insights, we first derive the maximum achievable individual EEs of the primary transmitter (PT) and RIS, respectively, and then analyze the asymptotic performance by exploiting the channel hardening effect. To characterize the non-trivial EE trade-off, we formulate an optimization problem to find the Pareto boundary of the EE region by jointly optimizing the transmit beamforming, power allocation and the passive beamforming of RIS. The formulated problem is nonconvex, and an efficient algorithm is proposed by decomposing it into a series of subproblems by using alternating optimization (AO) and successive convex approximation (SCA) techniques. Finally, simulation results are presented to validate the effectiveness of the proposed algorithm. Index TermsSymbiotic radio (SR), reconfigurable intelligent surface (RIS), active and passive communication, energy efficiency (EE) region.

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Energy-Efficient Joint Resource Allocation With Reconfigurable Intelligent Surfaces in Symbiotic Radio Networks

Cited by 9 publications

References 27 publications

A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks

A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks

Joint Optimization of Video-Based AI Inference Tasks in MEC-Assisted Augmented Reality Systems

Characterizing the Energy-Efficiency Region of Symbiotic Radio Communications

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