Energy Efficiency Maximization for IRS-Assisted Uplink Systems: Joint Resource Allocation and Beamforming Design

Forouzanmehr, Maliheh; Akhlaghi, Soroush; Khalili, Ata; Wu, Qingqing

doi:10.1109/lcomm.2021.3115812

Cited by 17 publications

(8 citation statements)

References 13 publications

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“…Although the use of IRSs in wireless networks has been developed in many applications with different objectives [7], [8], [11]- [15], the application of IRSs to improve wireless energy efficiency received less attention [6], [16], [17]. The authors have suggested a new strategy in [16], [17] to maximize energy efficiency of a multi-user MISO system. However, only one IRS was considered in [16].…”

Section: A Related Workmentioning

confidence: 99%

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Energy-Efficient Resource Allocation for Multi-IRS-Aided Indoor 6G Networks

Khorasgani¹,

Tabataba²,

Soorki³

et al. 2023

Preprint

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In this paper, we propose a distributed intelligent reflecting surface (IRS) assisted single-user and multi-user millimeter wave (mmWave) system. Then, we formulate the resource allocation problem as an optimization to maximize energy efficiency under individual quality of service (QoS) constraints. We first propose a centralized algorithm, and further a low-complexity distributed one where the access point (AP) and IRSs independently adjust the transmit beamforming of AP, the phase shifts, and the on-off status of IRSs in an alternating manner until the convergence is reached. In a multi-user scenario, in the first stage, the successive convex approximation (SCA) and fractional programming (FP) approaches are applied to achieve a solution for optimization subproblems of the phase-shift coefficients and element on-off status of IRSs. Then, for the beamforming subproblem, a modified nested FP approach is proposed that finds an optimal solution for the beamforming vectors of AP. Our performance analysis on a practical scenario shows that the proposed centralized and distributed approach respectively enhances the energy efficiency up to 55%, 42% for single-user and up to 984% for multi-user scenarios, in comparison to the case where the on-off status and phase-shift coefficients of IRS elements are not selected optimally.

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Section: A Related Workmentioning

confidence: 99%

“…However, here, in this paper, we provide more degree of freedom by allowing each element of IRSs to be turned on or off. In [17], a MIMO IRS-assisted uplink network is investigated to maximize energy efficiency (EE). Furthermore, in [6], [17], subproblems are often handled using SCA, and majorization minimization (MM), respectively.…”

Section: A Related Workmentioning

confidence: 99%

Energy-Efficient Resource Allocation for Multi-IRS-Aided Indoor 6G Networks

Khorasgani¹,

Tabataba²,

Soorki³

et al. 2023

Preprint

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“…Note that, applying the SDR to constraint (43c) does not guarantee a rank-one solution for matrix Θ. Instead, to tackle the rank-one constraint, we first rewrite constraint (43c) as the following D.C. equivalent form [27] Equation (43c…”

Section: Sub-problem 2: Optimizing θ For Predetermined Value Of { W P...mentioning

confidence: 99%

“…Note that, applying the SDR to constraint () does not guarantee a rank‐one solution for matrix

\tilde{\bm {\Theta }}

. Instead, to tackle the rank‐one constraint, we first rewrite constraint () as the following D.C. equivalent form [27]

\begin{eqnarray} &&\text{Equation\nobreakspace }(\text{{43c}}):\mathrm{Tr}(\widetilde{\bm {\Theta }})-\sigma _{\max }(\widetilde{\bm {\Theta }})\le 0, \end{eqnarray}

where

\sigma _{\max }

is the largest singular value of matrix

\widetilde{\bm {\Theta }}

. However, constraint () is still non‐convex.…”

Section: Solutions Of the Optimization Problemmentioning

confidence: 99%

Resource allocation for multi‐IRS‐aided D2D communication underlying cellular networks

2022

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This paper investigates the resource allocation design in device-to-device (D2D) communication underlying cellular networks, which is assisted by multiple intelligent reflecting surfaces (IRSs) deployed at the cell boundary to enhance desired signals and mitigate interference between D2D pairs and CUs. In this regard, a multi-objective optimization problem (MOOP) framework is formulated to jointly maximize the downlink sum-rate of the D2D pairs and cellular users (CUs). To doing so, the MOOP is first converted into a single-objective optimization problem (SOOP) by invoking the weighted sum method. Next, to facilitate the high-coupled non-convex SOOP formulation, it is decomposed into two sub-problems through the alternative optimization method. For the first sub-problem, the inner approximation and successive convex approximations are adopted for jointly allocating D2D transmission power, subcarrier assignment, and transmit beamforming matrix at the BS. For the second sub-problem, an iterative algorithm based on the penalty-based method as well as Fenchel's duality approach is adopted to design the passive beamforming matrices at IRSs, which is guaranteed to rapidly converge to a stationary point. Simulation results reveal that a non-trivial trade-off between the total throughput of CUs and D2D pairs exist. Furthermore, the proposed framework significantly outperforms existing works addressed in the literature.

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“…The authors in [18] investigate the max‐min EE problem in an IRS‐assisted SWIPT system, while the power minimization problem of the RIS‐enabled unmanned aerial vehicles (UAV) system is studied in [19]. The authors in [20] aim to maximize EE in an uplink multi‐user multiple‐input multiple‐output (MIMO) IRS‐powered system. The effect of limited phase shifts on the data rate of uplink RIS‐ aided system is investigated in [21].…”

Section: Introductionmentioning

confidence: 99%

Resource allocation for IRS‐assisted MC MISO‐NOMA system

et al. 2022

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In this paper, a downlink multi-user communication of an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) power-domain non-orthogonal multiple access (NOMA) system is investigated. Considering multi-carrier (MC) transmission and to enhance user fairness, two users are assigned to the same subcarrier. For such a system, the authors optimize active beamforming at the base station (BS), subcarrier allocation policy, and phase shifts at the IRS to maximize the system throughput. A semi-definite relaxation (SDR) is applied to tackle the non-convex optimization problem, and an alternating optimization (AO) algorithm is proposed to obtain a suboptimal solution. Numerical results illustrate the higher throughput of the proposed MC multi-user IRS-aided MISO-NOMA system as compared to the conventional IRS-assisted orthogonal multiple access (OMA) system. INTRODUCTIONRecently, the use of intelligent reflecting surface (IRS) has been shown to be a promising technology to enhance the capacity and energy efficiency (EE) of beyond-the-fifth-generation (B5G) communication systems. An IRS is composed of a large number of low-cost reflecting elements. By adjusting the phase shifts introduced by these elements, the incident signal can be steered via passive beamforming. Thus, different from conventional communication systems, the use of an IRS can change the channel response [1,2]. On the other hand, non-orthogonal multiple access (NOMA) is another emerging technology to overcome the spectrum scarcity problem in B5G networks. In contrast to the traditional orthogonal multiple access (OMA), the same resource block can be allocated to multiple users in a NOMA system. The key difference between OMA and NOMA is how multiple users are served in given resource blocks. In particular, while resource blocks are assigned to different users in a non-overlapped manner (hence orthogonal), the same resource block can be allocated to multiple users in a NOMA system. Such highly-efficient allocation of resource blocks isThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Energy Efficiency Maximization for IRS-Assisted Uplink Systems: Joint Resource Allocation and Beamforming Design

Cited by 17 publications

References 13 publications

Energy-Efficient Resource Allocation for Multi-IRS-Aided Indoor 6G Networks

Energy-Efficient Resource Allocation for Multi-IRS-Aided Indoor 6G Networks

Resource allocation for multi‐IRS‐aided D2D communication underlying cellular networks

Resource allocation for IRS‐assisted MC MISO‐NOMA system

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