Energy-Efficient Resource Allocation for IRS-Assisted Multi-Antenna Uplink Systems

Intelligent reflecting surfaces (IRSs) are a promising technology for future-generation wireless networks by extending coverage region to blind spots and increasing mmWave propagation paths in non-line of sight environments. User association (UA) in dense millimeter wave (mmWave) networks is vital to characterizing connections among base stations (BSs) and mobile users for load balancing, interference management, and maximizing network utility. However, it has yet to be examined thoroughly in a multi-IRS-aided network. This paper presents a new UA scheme that takes cell interference into account for a multi-cell mmWave cellular network aided with multiple IRSs. We formulate a network spectral efficiency maximization problem by jointly optimizing active beamforming (AB) at the BSs, passive beamforming (PB) at the IRSs, and user-BS association with consideration of the impact of IRSs. We then propose a computationally efficient iterative algorithm based on alternating optimization (AO) to resolve this intractable mixed-integer non-convex problem. A fractional programming technique is used to optimize active beamforming at the BSs and passive beamforming at the IRSs, and a penalization method combined with successive convex programming is applied for UA optimization, which is shown to reach the optimal solution. Simulation results show significant performance improvements obtained by the proposed algorithm, providing higher spectral efficiency compared to several benchmark algorithms, while having a low computational complexity.

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

This letter studies intelligent reflecting surface (IRS)-aided full-duplex (FD) integrated access and backhaul (IAB) network, where IRSs are deployed near small-cell base stations (SBSs) to mitigate several types of interference including self-interference in FD system. An optimization problem is formulated to maximize network throughput by jointly optimizing precoder at base stations (BSs) for establishing wireless backhaul and access links, and passive beamforming at IRSs. To solve this non-convex problem, we divide the problem into two subproblems by using the relationship between data rate and weighted rate-minimum mean square error (WMMSE). Then, we propose the block coordinate descent (BCD) algorithm, where the BS precoder is optimized using quadratically constrained quadratic programming (QCQP) and the IRS passive beamforming is obtained via semi-definite relaxation (SDR) method. Simulation results illustrate that our proposed algorithm can effectively mitigate interference with the help of IRSs and achieve a better sum rate performance compared to conventional schemes.

Intelligent Reflecting Surfaces (IRS) is a groundbreaking innovative technique to boost energy and spectral efficiency in upcoming wireless systems, enabling simultaneous wireless information and power transfer (SWIPT). This paper optimizes Energy Harvesting Efficiency (EHE) in a multi-input multi-output (MIMO) SWIPT system, where an access point (AP)/base station (BS) uses an IRS to serve multi energy receivers (ERs) and information receivers (IRs). We formulate an EHE optimization problem, designing active and passive beamforming while satisfying the AP/BS power constraints, IRS unit-modulus constraints and IR rate demands in a linear EH model. The Linear Matrix Inequalities (LMIs) are approximated for efficient solutions. And second-order cone programming (SOCP) is solved via convex optimization (CVX), while the penalty convex-concave procedure (CCP) guarantees convexity to non-convex problems. Next, an alternating optimization (AO)-reliant iterative algorithm derived sub-optimal solutions with slack variables. According to simulation results, for all ERs within a linear EH design/model, the proposed algorithm improves total EHE by 40% to 45% compared to randomize, No-IRS, and decode-and-forward (DF) relay strategies. Ultimately, highlighting IRS’s benefit in improving quality of service (QoS) by tweaking its elements and AP/BS antennas.

Intelligent reflecting surface (IRS), which uses passive reflective elements instead of active radio frequency (RF) chains, is a cost and energy-efficient solution to improve the wireless system performance. With a similar objective, transmit antenna selection (AS) reduces the number of RF chains at the base station while harnessing the benefits of multiple antennas. In our work, we focus on joint optimization of antenna subset and transmit beamforming at the base station, and passive beamforming at the IRS to maximize the receive signal power. For single AS, we first derive a closed-form optimal rule. We then propose a simpler AS rule, which significantly reduces the computational complexity and the number of pilot transmissions required. For a system with Nt antennas at the base station and N IRS elements, the optimal AS rule requires N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> + N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> N pilots. However, the proposed simpler rule requires only 2N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> +N pilots. For subset AS, we develop a manifold optimization based algorithm. To reduce its subset search complexity, which is exponential in the number of RF chains at the base station, we propose an alternating optimization based iterative algorithm. Our numerical results show that the proposed simpler AS rules are near optimal.

Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network’s capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS’s antenna radome to directly reconfigure the signal radiation to/from the BS’s antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS’s antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS’s reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS’s reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.

Intelligent reflecting surface (IRS) has emerged as an innovative and disruptive solution to boost the spectral and energy efficiency and enlarge the coverage of wireless communication systems. However, the existing literature on IRS mainly concentrates on wireless communication systems assisted by single or multiple distributed IRSs, which are not always effective. In view of this issue, this paper considers a special double-IRS-assisted wireless communication system, where IRS1 and IRS2 are deployed near the base station (BS) and the user, respectively, and the transmitted signals reach the user via the cascaded BS-IRS1-IRS2-user channel only. We cooperatively optimize transmit and passive beamforming on the two IRSs based on the particle swarm optimization (PSO) algorithm to maximize the received signal power. Simulation indicates that despite no direct line-of-sight (LoS) path from the BS to the user, an excellent signal-to-noise ratio (SNR) is available at the receiver with the aid of two IRSs, which demonstrates that it is feasible to assist communication by double reflection links composed of two IRSs. Additionally, we unexpectedly find that when the positions of the two IRSs are fixed, by exchanging the positions of the BS and the user, the obtainable SNRs are similar.

This paper optimizes the energy consumption of the downlink of a multiple-antenna base station (BS) transmitting to several single-antenna users. The BS utilizes simultaneous wireless information and power transfer (SWIPT) while receivers apply power-splitting (PS) with a nonlinear energy harvesting model leading to PS-SWIPT. We use an intelligent reflecting surface (IRS) and propose a joint design to optimize the active data and the BS's energy beamformers, IRS's passive beamformers, and the receivers' PS ratios under perfect and imperfect CSI availability. In particular, the total BS transmit power is minimized while guaranteeing a minimum rate and harvested energy for each receiver. We apply the block coordinate descent (BCD) method to optimize active and passive beamformers iteratively. We enforce the rank-one constraint and solve the corresponding optimization problem via successive convex approximation (SCA) for accurate semidefinite relaxations. Furthermore, we propose a worst-case robust design for the imperfect CSI case and reformulate this problem with infinitely many constraints. With the BCD method, the problem is iteratively solved via semidefinite programming (SDP) and a second sub-problem with a linear objective and quadratic matrix inequalities, which is also solved via SCA. Numerical results show significant improvements (e.g., 30% decrease in transmit power) than those of no-IRS and IRS with random phase shifts.

This letter studies the joint active and passive beamforming in intelligent reflecting surface (IRS)-enhanced wireless powered mobile edge computing (MEC) networks, in which a hybrid access point (HAP) is deployed to charge multiple wireless devices (WDs) and assist in computation of the offloaded tasks from the WDs. An IRS is deployed to enhance the downlink wireless power transfer (WPT) and the uplink tasks offloading (TO). To maximize the systems’ sum computation rate, an optimization problem is formulated by jointly optimizing the time assignment of WPT and TO, the active beamforming at the HAP, the transmit power and computing frequency of the WDs, and the passive beamforming at the IRS. To tackle the non-convex problem, we present an efficient algorithm by utilizing the alternating optimization (AO) and semidefinite relaxation (SDR) techniques. Simulation results show that by deploying IRS in the system, a significant computation rate gain can be achieved. Especially, when the IRS is closer to the WDs or the HAP, the much higher computation rate can be reached.

This study investigates the integration of an intelligent reflecting surface (IRS) into an unmanned aerial vehicle (UAV) platform to utilize the advantages of these leading technologies for sixth-generation communications, e.g., improved spectral and energy efficiency, extended network coverage, and flexible deployment. In particular, we investigate a downlink IRS–UAV system, wherein single-antenna ground users (UEs) are served by a multi-antenna base station (BS). To assist the communication between UEs and the BS, an IRS mounted on a UAV is deployed, in which the direct links are obstructed owing to the complex urban channel characteristics. The beamforming at the BS, phase shift at the IRS, and the 3D placement of the UAV are jointly optimized to maximize the sum rate. Because the optimization variables, particularly the beamforming and IRS phase shift, are highly coupled with each other, the optimization problem is naturally non-convex. To effectively solve the formulated problem, we propose an iterative algorithm that employs block coordinate descent and inner approximation methods. Numerical results demonstrate the effectiveness of our proposed approach for a UAV-mounted IRS system on the sum rate performance over the state-of-the-art technology using the terrestrial counterpart.

While millimeter-wave (mmWave) communications can enjoy abundant bandwidth resource, their high susceptibility to blockage poses serious challenges to low-latency services. In this paper, a novel intelligent reflecting surface (IRS)-assisted mmWave scheme is proposed to overcome the impact of blockage. The scheme minimizes the user power of a multi-user mmWave system by jointly optimizing the transmit powers of the devices, the multi-user detector at the base station, and the passive beamforming at the IRS, subject to delay requirements. An alternating optimization framework is developed to decompose the joint optimization problem into three subproblems iteratively optimized till convergence. In particular, closed-form expressions are devised for the update of the powers and multi-user detector. The IRS configuration is formulated as a sum-of-inverse minimization (SIMin) fractional programming problem and solved by exploiting the alternating direction method of multipliers (ADMM). The configuration is also interpreted as a latency residual maximization problem, and solved efficiently by designing a new complex circle manifold optimization (CCMO) method. Numerical results corroborate the effectiveness of our scheme in terms of power saving, as compared with a semidefinite relaxation-based alternative.

Intelligent reflecting surface (IRS) has emerged as a promising technique to enhance wireless communication performance cost-effectively. The existing literature has mainly considered IRS being deployed near user terminals to improve their performance. However, this approach may incur a high cost if IRSs need to be densely deployed in the network to cater to random user locations. To avoid such high deployment cost, in this paper we consider a new IRS aided wireless network architecture, where IRSs are deployed in the vicinity of each base station (BS) to assist in its communications with distributed users regardless of their locations. Besides significantly enhancing IRSs’ signal coverage, this scheme helps reduce the IRS-associated channel estimation overhead as compared to conventional user-side IRSs, by exploiting the nearly static BS-IRS channels over short distance. For this scheme, we propose a new two-stage transmission protocol to achieve IRS channel estimation and reflection optimization for uplink data transmission efficiently. In addition, we propose effective methods for solving the user-IRS association problem based on long-term/statistical channel knowledge and the selected user-IRS-BS cascaded channel estimation problem. Finally, all IRSs’ passive reflections are jointly optimized with the BS’s multi-antenna receive combining to maximize the minimum achievable rate among all users for data transmission. Numerical results show that the proposed co-site-IRS empowered BS scheme can achieve significant performance gains over the conventional BS without co-site IRS and existing schemes for IRS channel estimation and reflection optimization, thus enabling an appealing low-cost and high-performance BS design for future wireless networks.

We consider an intelligent reflecting surface (IRS)-assisted communication system as a cost-effective technique to enhance secrecy rate performance in physical-layer security (PLS) systems. In this letter, the randomized radiation (RR)-based PLS algorithm, which uses active antenna arrays to randomize the radiation pattern, is adopted in IRS through passive beamforming. We show that RR cannot be simply applied to IRS because the secrecy rate is subject to the phase-dependent amplitude variations in IRS. Therefore, we provide an exact and closed-form expression for the secrecy rate of RR using IRS. Based on the analysis, we introduce a constant phase shift into the IRS elements and identify it as a key parameter to optimize the secrecy performance through a genetic algorithm.

Intelligent reflecting surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. In this paper, an IRS-aided secure spatial modulation (SM) is proposed, where the IRS perform passive beamforming and information transfer simultaneously by adjusting the on-off states of the reflecting elements. We formulate an optimization problem to maximize the average secrecy rate (SR) by jointly optimizing the passive beamforming at IRS and the transmit power at transmitter under the consideration that the direct pathes channels from transmitter to receivers are obstructed by obstacles. As the expression of SR is complex, we derive a newly fitting expression (NASR) for the expression of traditional approximate SR (TASR), which has simpler closed-form and more convenient for subsequent optimization. Based on the above two fitting expressions, three beamforming methods, called maximizing NASR via successive convex approximation (Max-NASR-SCA), maximizing NASR via dual ascent (Max-NASR-DA) and maximizing TASR via semi-definite relaxation (Max-TASR-SDR) are proposed to improve the SR performance. Additionally, two transmit power design (TPD) methods are proposed based on the above two approximate SR expressions, called Max-NASR-TPD and Max-TASR-TPD. Simulation results show that the proposed Max-NASR-DA and Max-NASR-SCA IRS beamformers harvest substantial SR performance gains over Max-TASR-SDR. For TPD, the proposed Max-NASR-TPD performs better than Max-TASR-TPD. Particularly, the Max-NASR-TPD has a closed-form solution.

This article introduces a novel strategy for wireless communication security utilizing intelligent reflecting surfaces (IRS). The IRS is strategically deployed to mitigate jamming attacks and eavesdropper threats while improving signal reception for legitimate users (LUs) by redirecting jamming signals toward desired communication signals leveraging physical layer security (PLS). By integrating the IRS into the backscatter communication system, we enhance the overall secrecy rate of LU, by dynamically adjusting IRS reflection coefficients and active beamforming at the base station (BS). A design problem is formulated to jointly optimize IRS reflecting beamforming and BS active beamforming, considering time-varying channel conditions and desired secrecy rate requirements. We propose a novel approach based on deep reinforcement learning (DRL) named Deep-PLS. This approach aims to determine an optimal beamforming policy capable of thwarting eavesdroppers in evolving environmental conditions. Extensive simulation studies validate the efficacy of our proposed strategy, demonstrating superior performance compared to traditional IRS approaches, IRS backscattering-based anti-eavesdropping methods, and other benchmark strategies in terms of secrecy performance.