Exploiting Reconfigurable Intelligent Surfaces in Edge Caching: Joint Hybrid Beamforming and Content Placement Optimization

Chen, Yingyang; Wen, Miaowen; Başar, Ertuğrul; Wu, Yik-Chung; Wang, Li; Liu, Weiping

doi:10.1109/twc.2021.3087912

Cited by 33 publications

(15 citation statements)

References 54 publications

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“…Ding et al proposed an IRS-assisted NOMA transmission such that the network can serve more users compared with spatial division multiple access [241]. Article [245] introduces an IRS-aided edge caching system to realize the maximum benefit of caching. Here, the authors formulated a network cost minimization problem regarding backhaul capacity and the transmission power to optimize the content placement.…”

Section: G Intelligent Reflecting Surfacesmentioning

confidence: 99%

A Survey on Cache-Aided NOMA for 6G Networks

Bepari¹,

Mondal²,

Chandra³

et al. 2022

Preprint

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Contrary to orthogonal multiple-access (OMA), non-orthogonal multiple-access (NOMA) schemes can serve a pool of users without exploiting the scarce frequency or time domain resources. This is useful in meeting the sixth generation (6G) network requirements, such as, low latency, massive connectivity, users' fairness, and high spectral efficiency. On the other hand, content caching restricts duplicate data transmission by storing popular contents in advance at the network edge which reduces 6G data traffic. In this survey, we focus on cache-aided NOMA-based wireless networks which can reap the benefits of both cache and NOMA; switching to NOMA from OMA enables cache-aided networks to push additional files to content servers in parallel and improve the cache hit probability. Beginning with fundamentals of cache-aided NOMA technology, we summarize the performance goals of cache-aided NOMA systems, present the associated design challenges, and categorize related recent literature based on their application verticals. Concomitant standardization activities and open research challenges are highlighted as well.

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Section: G Intelligent Reflecting Surfacesmentioning

confidence: 99%

A Survey on Cache-Aided NOMA for 6G Networks

Bepari¹,

Mondal²,

Chandra³

et al. 2022

Preprint

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“…• Computation offloading in the Internet of Things (IoT) networks [13]: In the downlink transmission of an RIS-aided cache-enabled radio access network, a multi-antenna BS transmits signals to a number of single-antenna users, as shown in Fig. 2(c).…”

Section: Ris Resource Allocation Examples and General Formulationmentioning

confidence: 99%

Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

Li¹,

Wang²,

Li³

et al. 2022

Preprint

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Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources, such as beamformers, transmit power, and computation time. However, due to the unique constraints on the phase shift, and massive numbers of reflecting units and users in large-scale networks, the resulting optimization problems are challenging to solve. This paper provides a review of current optimization methods and artificial intelligence-based methods for handling the constraints imposed by RIS and compares them in terms of solution quality and computational complexity. Future challenges in phase shift optimization involving RISs are also described and potential solutions are discussed. I. INTRODUCTIONIt is well-known that line-of-sight (LoS) propagation is a desirable but rarely occurring scenario for wireless communications. Traditional techniques for addressing this issue is to deploy more

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“…For edge computing and caching, the IoT devices offload partial or all of their computational tasks to the dedicated access point (AP), equipped with a local cache and connected with a MEC server. Also, the local cache is utilized to prefetch the IoT data and facilitate edge computing for computational tasks [4]. Nevertheless, IoT devices typically lock into severe channel attenuations with the dedicated PS and AP, which significantly limits their performance of wireless energy transfer (WET), wireless information transfer (WIT)/computational offloading.…”

Section: Introduction a Motivation And Scopementioning

confidence: 99%

Utility Maximization for IRS Assisted Wireless Powered Mobile Edge Computing and Caching (WP-MECC) Networks

Chu

Xiao

Shojafar

et al. 2023

IEEE Trans. Commun.

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This paper exploits an intelligent reflecting surface (IRS) assisted wireless powered mobile edge computing and caching (WP-MECC) network. In particular, an IRS is utilized to reflect energy signals from a power station (PS) to various IoT devices for energy harvesting during uplink wireless energy transfer (WET). These devices collect energy to support their own partially local computing for computational tasks and their offloading capabilities to an access point (AP), with the help of IRS via time or frequency division multiple access (TDMA or FDMA). The AP is equipped with a local cache connected with a MEC server via a backhaul link, which prefetches the data to facilitate edge computing capabilities. The maximization of a utility function is formulated to evaluate the overall network performance, which is defined as the difference between the sum of computational bits (offloading bits and local computing bits) and total backhaul cost. Due to multiple coupled variables, we first design the optimal caching strategy. Then, an auxiliary vector is introduced to coordinate the energy consumption of local computing and offloading, where its optimal solution can be achieved by an exhaustive search. Moreover, we utilize the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling for the TDMA scheme or the optimal bandwidth allocation for the FDMA counterpart in closed form. The IRS phase shifts are iteratively designed by employing the quadratic transformation (QT) and the Riemannian Manifold Optimization (RMO). Finally, simulation results are demonstrated to validate the network utility performance and confirm the advantage of the employment of IRS, the optimal IRS phase shift design and caching strategy, in comparison to the benchmark schemes.Index Terms-Intelligent reflecting surface (IRS), wireless powered mobile edge computing and caching (WP-MECC), utility

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Exploiting Reconfigurable Intelligent Surfaces in Edge Caching: Joint Hybrid Beamforming and Content Placement Optimization

Cited by 33 publications

References 54 publications

A Survey on Cache-Aided NOMA for 6G Networks

A Survey on Cache-Aided NOMA for 6G Networks

Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods

Utility Maximization for IRS Assisted Wireless Powered Mobile Edge Computing and Caching (WP-MECC) Networks

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