Optimization Techniques in Reconfigurable Intelligent Surface Aided Networks

Feng, Biqian; Gao, Junyuan; Wu, Yongpeng; Zhang, Wenjun; Xia, Xiang-Gen; Xiao, Chengshan

doi:10.1109/mwc.001.2100196

Cited by 17 publications

(5 citation statements)

References 13 publications

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“…wherein ξ = K + M − 1, to take into account the cyclic prefix loss 1 , p ∈ R K is the power allocated to the k-th subcarrier, such that P = ⟨p⟩; P being the total transmission power 2 , f i represents the i-th row of the DFT matrix F, B is the total bandwidth occupied by K subcarriers, and N 0 is the additive white Gaussian noise (AWGN) power. 1) Convex Optimization: In order to perform the RIS configuration, one alternative is to cast the capacity maximization problem as a convex optimization problem [3], [4], [8]. Firstly, let the achievable rate of (8) be rewritten as an objective function of a general optimization problem, such that maximize p,ω θ R subject to ⟨p⟩ ≤ P , (9)…”

Section: B Ris Configurationmentioning

confidence: 99%

“…and for which the power allocation, p, and the RIS phaseshifts, ω θ , are jointly optimized. More specifically, the joint optimization is performed by way of the alternating optimization (AO) framework [3], [4], where an approximate solution to ( 9) is reached by iteratively optimizing each variable p or ω θ at a time, that is, one of these variables is optimized while the other maintains a fixed value. This framework can be described by Algorithm 1.…”

Section: B Ris Configurationmentioning

confidence: 99%

“…Therefore, in this work we discuss different methods for doing so, also considering wideband channels and the particular set of challenges brought by them. Additionally, we discuss the practical case where there is a finite number of phase shift configurations available [3], [4]. This represents a scenario where phase-shifts are discrete, instead of the commonly assumed continuous RIS configuration.…”

Section: Introductionmentioning

confidence: 99%

“…Note that this algorithm employs the successive convex approximation (SCA) technique [3], [4], [8] to solve (10) and also the well-known water filling algorithm [2], [8], [9] to calculate the optimum power allocation. Moreover, the initial phases used for calculating the first power allocation values can be defined according to a heuristic method [8], given by…”

mentioning

confidence: 99%

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Continuous and Discrete Phase-shifts for RIS-aided Systems in Wideband Communication Channels

Souza,

Khazaee,

Mendes

2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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This work investigates the performance of the reconfigurable intelligent surface (RIS) for wideband communication systems, also considering line of sight (LOS) channels. We evaluate the achievable rate of methods such as the alternating optimization (AO) and strongest tap maximization (STM), which are used to configure the RIS. Additionally, the impact of continuous and discrete phase-shifts is also analyzed in the context of novel proposed metrics, denoted as the relative and efficiency rate. It is demonstrated that discrete or quantized phase-shifts can achieve a rate close to that of continuous phaseshifts, if enough quantization bits are used.

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Section: B Ris Configurationmentioning

confidence: 99%

Section: B Ris Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Continuous and Discrete Phase-shifts for RIS-aided Systems in Wideband Communication Channels

Souza,

Khazaee,

Mendes

2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…Many different IRS phase shift models are suggested in literature[12],[15], however, the unit-modulus model is the most frequently used one[5]-[11],[14] 4. In a practical IRS-MGMC system, M ≫ max{N, K, G}, and therefore we neglect the lower-order terms.…”

mentioning

confidence: 99%

On the Achievable Rate of IRS-Assisted Multigroup Multicast Systems

Farooq¹,

Kumar²,

Juntti³

et al. 2022

Preprint

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Intelligent reflecting surfaces (IRSs) have shown huge advantages in many potential use cases and thus have been considered a promising candidate for next-generation wireless systems. In this paper, we consider an IRS-assisted multigroup multicast (IRS-MGMC) system in a multiple-input single-output (MISO) scenario, for which the related existing literature is rather limited.In particular, we aim to jointly design the transmit beamformers and IRS phase shifts to maximize the sum rate of the system under consideration. In order to obtain a numerically efficient solution to the formulated non-convex optimization problem, we propose an alternating projected gradient (APG) method where each iteration admits a closed-form and is shown to be superior to a known solution that is derived from the majorizationminimization (MM) method in terms of both achievable sum rate and required complexity, i.e., run time. In particular, we show that the complexity of the proposed APG method grows linearly with the number of IRS tiles, while that of the known solution in comparison grows with the third power of the number of IRS tiles. The numerical results reported in this paper extend our understanding on the achievable rates of large-scale IRS-assisted multigroup multicast systems.

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