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Register a new userMulti-Objective Resource Allocation for IRS-Aided SWIPT
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In this letter, we study the resource allocation for a multiuser intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, a multi-antenna base station (BS) transmits energy and information signals simultaneously to multiple energy harvesting receivers (EHRs) and information decoding receivers (IDRs) assisted by an IRS. Under this setup, we introduce a multi-objective optimization (MOOP) framework to investigate the fundamental trade-off between the data sum-rate maximization and the total harvested energy maximization, by jointly optimizing the energy/information beamforming vectors at the BS and the phase shifts at the IRS. This MOOP problem is first converted to a single-objective optimization problem (SOOP) via the $epsilon$-constraint method and then solved by majorization minimization (MM) and inner approximation (IA) techniques. Simulation results unveil a non-trivial trade-off between the considered competing objectives, as well as the superior performance of the proposed scheme as compared to various baseline schemes.
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This paper studies intelligent reflecting surface (IRS)-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and meanwhile receives information signals in the uplink (UL) with the help of IRS. Particularly, we propose three types of IRS beamforming configurations to strike a balance between the system performance and signaling overhead as well as implementation complexity. We first propose the fully dynamic IRS beamforming, where the IRS phase-shift vectors vary with each time slot for both DL wireless energy transfer (WET) and UL wireless information transmission (WIT). To further reduce signaling overhead and implementation complexity, we then study two special cases, namely, partially dynamic IRS beamforming and static IRS beamforming. For the former case, two different phase-shift vectors can be exploited for the DL WET and the UL WIT, respectively, whereas for the latter case, the same phase-shift vector needs to be applied for both DL and UL transmissions. We aim to maximize the system throughput by jointly optimizing the time allocation, HAP transmit power, and IRS phase shifts for the above three cases. Two efficient algorithms based on alternating optimization and penalty-based algorithms are respectively proposed for both perfect self-interference cancellation (SIC) case and imperfect SIC case by applying successive convex approximation and difference-of-convex optimization techniques. Simulation results demonstrate the benefits of IRS for enhancing the performance of FD-WPCN, and also show that the IRS-aided FD-WPCN is able to achieve significantly performance gain compared to its counterpart with half-duplex when the self-interference (SI) is properly suppressed.
We investigate the joint uplink-downlink design for time-division-duplexing (TDD) and frequency-division-duplexing (FDD) multi-user systems aided by an intelligent reflecting surface (IRS). We formulate and solve a multi-objective optimization problem to maximize uplink and downlink rates as a weighted-sum problem (WSP) that captures the trade-off between achievable uplink and downlink rates. We propose a resource allocation design that optimizes the WSP by jointly optimizing the beamforming vectors, power control and IRS phase shifts where the same IRS configuration is used for assisting uplink and downlink transmissions. In TDD, the proposed IRS design reduces the overhead associated with IRS configuration and the need for quiet periods while updating the IRS. In addition, a joint IRS design is critical for supporting concurrent uplink and downlink transmissions in FDD. We investigate the effect of different user-weighting strategies and different parameters on the performance of the joint IRS design and the resultant uplink-downlink trade-off regions. In all FDD scenarios and some TDD scenarios, the joint design significantly outperforms the heuristic of using the IRS configuration optimized for uplink (respectively, downlink) to assist the downlink (respectively, uplink) transmissions and substantially bridges the gap to the upper bound of allowing different IRS configurations in uplink and downlink.
It is known that the capacity of the intelligent reflecting surface (IRS) aided cellular network can be effectively improved by reflecting the incident signals from the transmitter in a low-cost passive reflecting way. Nevertheless, in the actual network operation, the base station (BS) and IRS may belong to different operators, consequently, the IRS is reluctant to help the BS without any payment. Therefore, this paper investigates price-based reflection resource (elements) allocation strategies for an IRS-aided multiuser multiple-input and single-output (MISO) downlink communication systems, in which all transmissions over the same frequency band. Assuming that the IRS is composed with multiple modules, each of which is attached with a smart controller, thus, the states (active/idle) of module can be operated by its controller, and all controllers can be communicated with each other via fiber links. A Stackelberg game-based alternating direction method of multipliers (ADMM) is proposed to jointly optimize the transmit beamforming at the BS and the passive beamforming of the active modules. Numerical examples are presented to verify the proposed algorithm. It is shown that the proposed scheme is effective in the utilities of both the BS and IRS.
We study the problem of optimal power allocation in single-hop multi-antenna ad-hoc wireless networks. A standard technique to solve this problem involves optimizing a tri-convex function under power constraints using a block-coordinate-descent (BCD) based iterative algorithm. This approach, termed WMMSE, tends to be computationally complex and time consuming. Several learning-based approaches have been proposed to speed up the power allocation process. A recent work, UWMMSE, learns an affine transformation of a WMMSE parameter in an unfolded structure to accelerate convergence. In spite of achieving promising results, its application is limited to single-antenna wireless networks. In this work, we present a UWMMSE framework for power allocation in (multiple-input multiple-output) MIMO interference networks. Through an empirical study, we illustrate the superiority of our approach in comparison to WMMSE and also analyze its robustness to changes in channel conditions and network size.
In this paper, we focus on intelligent reflecting surface (IRS) assisted multi-antenna communications with transceiver hardware impairments encountered in practice. In particular, we aim to maximize the received signal-to-noise ratio (SNR) taking into account the impact of hardware impairments, where the source transmit beamforming and the IRS reflect beamforming are jointly designed under the proposed optimization framework. To circumvent the non-convexity of the formulated design problem, we first derive a closed-form optimal solution to the source transmit beamforming. Then, for the optimization of IRS reflect beamforming, we obtain an upper bound to the optimal objective value via solving a single convex problem. A low-complexity minorization-maximization (MM) algorithm was developed to approach the upper bound. Simulation results demonstrate that the proposed beamforming design is more robust to the hardware impairments than that of the conventional SNR maximized scheme. Moreover, compared to the scenario without deploying an IRS, the performance gain brought by incorporating the hardware impairments is more evident for the IRS-aided communications.
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