Degrees of Freedom of the <i>K</i>-User Interference Channel Assisted by Active and Passive IRSs

Bafghi, Ali H. Abdollahi; Jamali, Vahid; Nasiri-Kenari, Masoumeh; Schober, Robert

doi:10.1109/tcomm.2022.3159658

Cited by 22 publications

(31 citation statements)

References 42 publications

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“…where the expressions of F lb,r A k,k,k (v k , µ k ) and G lb,r A i,k,j (v j ) can be found in Section III-B after (22) with index "t" replaced by "r". By direct inspection, problem (32) is a convex QCQP that can be solved exactly using readily available solvers such as CVX [37].…”

Section: Proposed Algorithms For (P2) and (P2-d)mentioning

confidence: 99%

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Exploiting Intelligent Reflecting Surfaces for Interference Channels with SWIPT

Gao¹,

Wu²,

Chen³

et al. 2023

Preprint

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This paper considers intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) in a multi-user multiple-input single-output (MISO) interference channel (IFC), where multiple transmitters (Txs) serve their corresponding receivers (Rxs) in a shared spectrum with the aid of IRSs. Our goal is to maximize the sum rate of the Rxs by jointly optimizing the transmit covariance matrices at the Txs, the phase shifts at the IRSs, and the resource allocation subject to the individual energy harvesting (EH) constraints at the Rxs. Towards this goal and based on the well-known power splitting (PS) and time switching (TS) receiver structures, we consider three practical transmission schemes, namely the IRS-aided hybrid TS-PS scheme, the IRS-aided time-division multiple access (TDMA) scheme, and the IRS-aided TDMA-D scheme. The latter two schemes differ in whether the Txs employ deterministic energy signals known to all the Rxs. Despite the non-convexity of the three optimization problems corresponding to the three transmission schemes, we develop computationally efficient algorithms to address them suboptimally, respectively, by capitalizing on the techniques of alternating optimization (AO) and successive convex approximation (SCA). Moreover, we conceive feasibility checking methods for these problems, based on which the initial points for the proposed algorithms are constructed. Simulation results demonstrate that our proposed IRS-aided schemes significantly outperform their counterparts without IRSs in terms of sum rate and maximum EH requirements that can be satisfied under various setups. In addition, the IRS-aided hybrid TS-PS scheme generally achieves the best sum rate performance among the three proposed IRS-aided schemes, and if not, increasing the number of IRS elements can always accomplish it.

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Section: Proposed Algorithms For (P2) and (P2-d)mentioning

confidence: 99%

“…{{S i,j }, {τ j }} by only applying the change of variables (i.e., defining W i,j = τ j S i,j , ∀i, j ∈ K). On the other hand, similar to (32), the subproblem of (P2-D) w.r.t. {v j } can be approximated as a convex QCQP, which differs from (32) in that the first constraint…”

Section: B Proposed Algorithm For (P2-d)mentioning

confidence: 99%

Exploiting Intelligent Reflecting Surfaces for Interference Channels with SWIPT

Gao¹,

Wu²,

Chen³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Θ ∈ C M ×M is a diagonal matrix representing the IRS phase profile. Throughout this paper, we assume that the IRS is passive and therefore for each element we have |θ m | ≤ 1 [31]. Furthermore, the channels from the source to the IRS and from the IRS to the destination are also assumed to be partially known and denoted as H SI and H ID respectively.…”

Section: System Modelmentioning

confidence: 99%

Robust Transceiver Design for IRS-Assisted Cascaded MIMO Systems

Esmaeili¹,

Kariminezhad²,

Sezgin³

2022

Preprint

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Robust transceiver design against unresolvable system uncertainties is of crucial importance for reliable communication. We consider a MIMO multi-hop system, where the source, the relay, and the destination are equipped with multiple antennas. Further, an intelligent reconfigurable surface (IRS) is established to cancel the RSI as much as possible. The considered decode-and-forward (DF) hybrid relay can operate in either half-duplex or full-duplex mode, and the mode changes adaptively depending on the RSI strength. We investigate a robust transceiver design problem, which maximizes the throughput rate corresponding to the worst-case RSI under a self-interference channel uncertainty bound constraint. To the best of our knowledge, this is the first work that uses the IRS for RSI cancellation in MIMO full-duplex DF relay systems. The yielded problem turns out to be a non-convex optimization problem, where the non-convex objective is optimized over the cone of semidefinite matrices. We propose a closed-from lower bound for the IRS worst case RSI cancellation. Eventually, we show an important result that, for the worst case scenario, IRS can be helpful only if the number of IRS elements are at least as large as the size of the interference channel. Moreover, a novel method based on majorization theory is proposed to find the best response of the transmitters and relay against worst case RSI. Furthermore, we propose a multi-level water-filling algorithm to obtain a locally optimal solution iteratively. Finally, we obtain insights on the optimal antenna allocation at the relay input-frontend and output-frontend, for relay reception and transmission, respectively.

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“…We consider an orthogonal multiple access and so the users are communicated with the BS on orthogonal sub-channels. 1 For notation simplicity, we denote the sets of users, tiles per IRS, and passive reflection units per tile as K {1, 2, . .…”

Section: B System Modelmentioning

confidence: 99%

“…W ITH the goal of maintaining service for different types of applications, the sixth-generation (6G) wireless communication network (WCN) is aimed to satisfy more stringent requirements than the fifth-generation (5G) in different aspects, such as energy efficiency, ultra-high data rate, global coverage, low latency, and extremely high reliability. Recently both academia and industry are concertedly looking into beyond 5G (B5G) [1]. In order to explore the new technologies to make the above requirements feasible intelligent reflecting surface (IRS) is one of the promising technologies to construct the proper infrastructure in 6G networks.…”

Section: Introductionmentioning

confidence: 99%

IRS-User Association in IRS-Aided MISO Wireless Networks: Convex Optimization and Machine Learning Approaches

Amiriara¹,

Ashtiani²,

Mirmohseni³

et al. 2022

Preprint

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This paper concentrates on the problem of associating an intelligent reflecting surface (IRS) to multiple users in a multiple-input single-output (MISO) downlink wireless communication network. The main objective of the paper is to maximize the sum-rate of all users by solving the joint optimization problem of the IRS-user association, IRS reflection, and BS beamforming, formulated as a non-convex mixed-integer optimization problem. The variable separation and relaxation are used to transform the problem into three convex sub-problems, which are alternatively solved through the convex optimization (CO) method. The major drawback of the proposed CO-based algorithm is high computational complexity. Thus, we make use of machine learning (ML) to tackle this problem. To this end, first, we convert the optimization problem into a regression problem. Then, we solve it with feed-forward neural networks (FNNs), trained by CO-based generated data. Simulation results show that the proposed ML-based algorithm has a performance equivalent to the CO-based algorithm, but with less computation complexity due to its offline training procedure.

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Degrees of Freedom of the K-User Interference Channel Assisted by Active and Passive IRSs

Cited by 22 publications

References 42 publications

Exploiting Intelligent Reflecting Surfaces for Interference Channels with SWIPT

Exploiting Intelligent Reflecting Surfaces for Interference Channels with SWIPT

Robust Transceiver Design for IRS-Assisted Cascaded MIMO Systems

IRS-User Association in IRS-Aided MISO Wireless Networks: Convex Optimization and Machine Learning Approaches

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