Hybrid RIS-Empowered Reflection and Decode-and-Forward Relaying for Coverage Extension

Yildirim, Ibrahim; Kilinc, Fatih; Başar, Ertuğrul; Alexandropoulos, George C.

doi:10.1109/lcomm.2021.3054819

Cited by 92 publications

(42 citation statements)

References 8 publications

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“…The remaining task is therefore to obtain the expectation of the SNRs at the relay and user. Now we define the channels through the IRSs as (9) and applying the binomial expansion theorem, 3 we get…”

Section: A Tight Upper Boundmentioning

confidence: 99%

“…The authors in [10] then discussed the feasibility of the coexistence of DF relaying and IRS. The results in [9] and [10] are encouraging and deserve attention. That said, multi-IRS systems are much less understood.…”

mentioning

confidence: 94%

“…Additionally, decode-and-forward (DF) relaying is known to outperform amplify-and-forward (AF) relaying in terms of rate performance [8]. In [9], communication with mixed DF relaying and IRS was studied to improve the coverage performance. The authors in [10] then discussed the feasibility of the coexistence of DF relaying and IRS.…”

mentioning

confidence: 99%

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Ergodic Rate Analysis and IRS Configuration for Multi-IRS Dual-Hop DF Relaying Systems

et al. 2021

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Intelligent reflecting surface (IRS) has emerged as a promising and low-cost technology for improving wireless communications by collecting dispersed radio waves and redirecting them to the intended receivers. In this letter, we characterize the achievable rate when multiple IRSs are utilized in the manner of decode-and-forward (DF) relaying. Our performance analysis is based on the Nakagami-m fading model with perfect channel state information (CSI). Tight upper bound expressions for the ergodic rate are derived. Moreover, we compare the performance of the multi-IRS DF relaying system with that of the one with a single IRS and confirm the gain. We then optimize the IRS configuration considering the numbers of IRSs and IRS reflecting elements, which provides useful insights for practical design.

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Section: A Tight Upper Boundmentioning

confidence: 99%

mentioning

confidence: 94%

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Ergodic Rate Analysis and IRS Configuration for Multi-IRS Dual-Hop DF Relaying Systems

et al. 2021

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“…where ȳG ∈ C N 1 ×1 and ȳI 1 ∈ C M 1 ×1 . According to (26), existing AoA estimation algorithms such as multiple signal classification (MUSIC) can be applied to estimate AoA pairs (ϑ G , ϕ G )…”

Section: A Distributed Channel Estimationmentioning

confidence: 99%

Intelligent Reflecting Surface-Aided LEO Satellite Communication: Cooperative Passive Beamforming and Distributed Channel Estimation

Zheng¹,

Lin²,

Zhang³

2022

Preprint

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Low-earth orbit (LEO) satellite communication plays an important role in assisting/complementing terrestrial communications by providing worldwide coverage, especially in harsh environments such as high seas, mountains, and deserts which are uncovered by terrestrial networks. Traditionally, the passive reflect-array with fixed phase shifts has been applied in satellite communications to compensate for the high path loss due to long propagation distance with low-cost directional beamforming; however, it is unable to flexibly adapt the beamforming direction to dynamic channel conditions. In view of this, we consider in this paper a new intelligent reflecting surface (IRS)-aided LEO satellite communication system, by utilizing the controllable phase shifts of massive passive reflecting elements to achieve flexible beamforming, which copes with the time-varying channel between the high-mobility satellite (SAT) and ground node (GN) cost-effectively. In particular, we propose a new architecture for IRSaided LEO satellite communication where IRSs are deployed at both sides of the SAT and GN, and study their cooperative passive beamforming (CPB) design over line-of-sight (LoS)-dominant singlereflection and double-reflection channels. Specifically, we jointly optimize the active transmit/receive beamforming at the SAT/GN as well as the CPB at two-sided IRSs to maximize the overall channel gain from the SAT to each GN. Interestingly, we show that under LoS channel conditions, the highdimensional SAT-GN channel can be decomposed into the outer product of two low-dimensional vectors.By exploiting the decomposed SAT-GN channel, we decouple the original beamforming optimization problem into two simpler subproblems corresponding to the SAT and GN sides, respectively, which are both solved in closed-form. Furthermore, we propose an efficient transmission protocol to conduct channel estimation and beam tracking, which only requires independent processing of the SAT and GN

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“…Therefore, to amalgamate the benefits of both IRS and cooperative communications, the authors in [23] and [24] investigated the IRS phase shifts optimization to maximize the achievable rate for hybrid IRS with HD and FD relay networks. Two hybrid relay and IRS-assisted transmission scheme were proposed in [25] to achieve higher error performance and achievable rate, compared with IRS-only and relay-only transmissions. In [26], to further enhance the transmission quality, relay selection was investigated in IRS-assisted cooperative networks.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning-Based Buffer-Aided Relay Selection in IRS-Assisted Secure Cooperative Networks

Huang

Chen

Wong

2021

IEEE Trans.Inform.Forensic Secur.

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This paper proposes a multi-agent deep reinforcement learning-based buffer-aided relay selection scheme for an intelligent reflecting surface (IRS)-assisted secure cooperative network in the presence of an eavesdropper. We consider a practical phase model where both phase shift and reflection amplitude are discrete variables to vary the reflection coefficients of the IRS. Furthermore, we introduce the buffer-aided relay to enhance the secrecy performance, but the use of the buffer leads to the cost of delay. Thus, we aim to maximize either the average secrecy rate with a delay constraint or the throughput with both delay and secrecy constraints, by jointly optimizing the buffer-aided relay selection and the IRS reflection coefficients. To obtain the solution of these two optimization problems, we divide each of the problems into two sub-tasks and then develop a distributed multi-agent reinforcement learning scheme for the two cooperative sub-tasks, each relay node represents an agent in the distributed learning. We apply the distributed reinforcement learning scheme to optimize the IRS reflection coefficients, and then utilize an agent on the source to learn the optimal relay selection based on the optimal IRS reflection coefficients in each iteration. Simulation results show that the proposed learning-based scheme uses an iterative approach to learn from the environment for approximating an optimal solution via the exploration of multiple agents, which outperforms the benchmark schemes.

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Hybrid RIS-Empowered Reflection and Decode-and-Forward Relaying for Coverage Extension

Cited by 92 publications

References 8 publications

Ergodic Rate Analysis and IRS Configuration for Multi-IRS Dual-Hop DF Relaying Systems

Ergodic Rate Analysis and IRS Configuration for Multi-IRS Dual-Hop DF Relaying Systems

Intelligent Reflecting Surface-Aided LEO Satellite Communication: Cooperative Passive Beamforming and Distributed Channel Estimation

Multi-Agent Reinforcement Learning-Based Buffer-Aided Relay Selection in IRS-Assisted Secure Cooperative Networks

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