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

In this paper, we investigate an intelligent reflecting surface (IRS) assisted multi-user multiple-input multiple-output (MIMO) full-duplex (FD) system. We jointly optimize the active beamforming matrices at the access point (AP) and uplink users, and the passive beamforming matrix at the IRS to maximize the weighted sum-rate of the system. Since it is practically difficult to acquire the channel state information (CSI) for IRS-related links due to its passive operation and large number of elements, we conceive a mixed-timescale beamforming scheme. Specifically, the high-dimensional passive beamforming matrix at the IRS is updated based on the channel statistics while the active beamforming matrices are optimized relied on the low-dimensional real-time effective CSI at each time slot. We propose an efficient stochastic successive convex approximation (SSCA)-based algorithm for jointly designing the active and passive beamforming matrices. Moreover, due to the high computational complexity caused by the matrix inversion computation in the SSCA-based optimization algorithm, we further develop a deep-unfolding neural network (NN) to address this issue. The proposed deep-unfolding NN maintains the structure of the SSCA-based algorithm but introduces a novel non-linear activation function and some learnable parameters induced by the first-order Taylor expansion to approximate the matrix inversion. In addition, we develop a black-box NN as a benchmark. Simulation results show that the proposed mixed-timescale algorithm outperforms the existing single-timescale algorithm and the proposed deep-unfolding NN approaches the performance of the SSCA-based algorithm with much reduced computational complexity when deployed online.

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS.

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 ε-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.

In this paper, we study a resource allocation problem in orthogonal frequency division multiple access (OFDMA)-based Device-to-Device (D2D) communications. To this end, we propose a multi-objective optimization problem (MOOP) framework, which jointly maximizes the sum rate of D2D users (DUs) and cellular users (CUs) in uplink communications and minimizes the total transmit power. The proposed problem formulation takes into account the minimum data-rates and the maximum transmitted power budget for both DUs and CUs. We transform this MOOP into a single-objective optimization problem (SOOP) using the weighted sum method and then propose an approach to solve this SOOP via a monotonic approach yielding an efficient optimal solution. Furthermore, a suboptimal solution based on the successive convex approximation (SCA) is presented to compromise complexity and performance gain. This is done to reveal that the proposed suboptimal solution closely approaches an optimal solution through simulation analysis. Numerical results unveil an interesting tradeoff between D2Ds CUs and demonstrate the superiority of our proposed solution compared to other baseline schemes.

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.

In this article, a dual-function intelligent reflecting surface (IRS) assisted cognitive radio (CR) nonorthogonal multiple access (NOMA) networks for secure transmission is studied. Considering the scenario of both internal and multiple external Eves in the system, our goal is to maximize the sum achievable secrecy rate through jointly optimizing the transmit beamforming of cognitive base station (CBS), the mode selection of each IRS element and the phase vector of IRS, while both the passive dual-function IRS and active dual-function IRS are investigated. For passive dual-function IRS setup, we decouple the proposed nonconvex optimization problem into two subproblems, the transmit beamforming subproblem and the phase vector subproblem. For transmit beamforming subproblem, we first use arithmetic geometric mean (AGM) inequality and linear matrix inequality (LMI) to deal with the nonconvex fraction form in the objective function and in constraints. Then, successive convex approximation (SCA) is applied to iteratively solve it. For the phase vector subproblem, we utilize the penalty function to handle the rank one constraint and the binary constraint. By alternately optimizing these two subproblems, we obtain a local optimal solution. Then, we extend the proposed algorithm to the secure transmission scheme with active dual-function IRS. The simulation results demonstrate that the proposed scheme with the help of a dual-function IRS effectively enhances the security of the CR-NOMA network compared to other benchmark schemes. Moreover, when the maximum transmitting power <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{\max }\ge 15$ </tex-math></inline-formula> dBm, the proposed active IRS scheme is superior to the proposed passive IRS scheme.

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

In this paper, we investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming vectors across time and resource allocation. To this end, we first study two typical active IRS aided MA schemes, namely time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), by analytically comparing their achievable sum throughput and proposing corresponding algorithms. Interestingly, we prove that given only one available IRS beamforming vector, the NOMA-based scheme generally achieves a larger throughput than the TDMA-based scheme, whereas the latter can potentially outperform the former if multiple IRS beamforming vectors are available to harness the favorable time selectivity of the IRS. To strike a flexible balance between the system performance and the associated signaling overhead incurred by more IRS beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with device grouping, where the devices in the same group transmit simultaneously via NOMA while devices in different groups occupy orthogonal time slots. By controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for any given number of IRS beamforming vectors available. Despite of the non-convexity of the considered optimization problem, we propose an efficient algorithm based on alternating optimization, where each subproblem is solved optimally. Simulation results illustrate the practical superiorities of the active IRS over the passive IRS in terms of the coverage extension and supporting multiple energy-limited devices, and demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly balancing the performance-cost tradeoff.

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, subject to the requirement of minimum rate for legitimate users. To tackle this non-convex problem, we first decompose it into three subproblems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization.

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 paper investigates secure broadcast communications in intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) symbiotic radio (SR) networks, where the IRS and the base station (BS) are cooperating. Specifically, the IRS assists secure broadcasting for the BS by smartly reconfiguring the phase shifts of the signals from the BS, while delivering its own information to an Internet-of-Things device (IoD). We aim at maximizing the minimum secrecy rate of multiple users by jointly designing the hybrid precoder at the BS and the passive beamformer at the IRS, subject to the rate constraints of both the multiple users and the IoD. The design is formulated as a non-convex optimization problem, which is intractable. To handle it, we develop a computationally efficient iterative algorithm based on the successive convex approximation, the efficiency and superiority of which have been validated by simulation results.

In this paper, we investigate an unmanned aerial vehicle (UAV) mounted intelligent reflecting surface (IRS) assisted systems, where an access point (AP) sends short packets to multiple users with the help of an UAV mounted IRS (U-IRS). The maximum average age of information (AoI) among users is minimized by jointly optimizing the time interval allocation, power allocation and UAV trajectory. However, the formulated problem is difficult to solve due to the non-convex objective function and coupled variables. Thus, we propose an alternating optimization (AO) based algorithm by dividing the problem into three sub-problems, which thus can be efficiently solved via the successive convex approximation (SCA) and block coordinate descent (BCD) techniques. Simulation results show that the proposed U-IRS design can significantly improve the AoI performance as compared to the UAV relay design and the packet error rate (PER) minimized U-IRS design. Furthermore, we find that increasing the number of IRS elements has a compromise effect on the AoI performance. On the one hand, it helps to achieve higher beamforming gain for further improving the transmission reliability and thus better compensating for the packet error owing to using short packet. On the other hand, it leads to higher channel eatimation overhead for reaping the beamforming gain, which however introduces unfavorable larger delay. As a result, while the IRS elements number exceeds a certain value, the AoI performance no more improves but degrades instead.

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.

As an emerging technology, intelligent reflecting surface (IRS) has attached attention of academia because it can customize a favorable wireless propagation environment. However, for the traditional passive structure, IRS can merely alter the phase of the incident signal, which limits the maximum attainable beamforming gain. In order to give full play to the potential of IRS, in this paper, we employ an active IRS, which is able to alter the phase and amplify the amplitude of the incident signals simultaneously because there exists an additional power supply. So as to improve the performance of active IRS-assisted communication system, the reflection matrix of the IRS and the beamforming vector of the base station (BS) are jointly considered and optimized to minimize the transmit power of the BS. Design the algorithm of power resource allocation usually be converted into an optimization problem, and must meet the maximum power margin of active IRS and satisfy the quality of service (QoS) requirements of users. Aiming at solving the nonconvex problem, we designed a high calculation efficiency algorithm on the strength of the internal approximation (IA) method and bilinear transformation. The algorithm guarantees convergence to the local optimal solution of the problem under consideration. Simulation indicates that this scheme is effective compared with the two blank control schemes. In addition, the results indicate that compared with passive IRS, deploying active IRS has great advantages for boosting the performance of communication systems, especially in the presence of strong direct links.

In this paper, we study a device to device (D2D) communications underlaying cellular network where D2D pairs can reuse a cellular user (CU) resource block (RB) simultaneously, to share multimedia files. It is well known that the delay plays an important role in real-time services. Here, to ensure the fairness between D2D pairs while satisfying the quality of service (QoS) requirements, we maximize the minimum utility function of D2D users under delay and interference restrictions. The sigmoid function is used as the utility function in the optimization problem. We show that this optimization problem is NP-hard and thus, we introduce an effective iterative method to solve it. Simulation results reveal significant improvement in comparison to other power allocation schemes and demonstrate that the proposed scheme can effectively guarantee the fairness between D2D pairs under QoS conditions of CU and D2D pairs. Moreover, simulation results also indicate that the convergence rate of our proposed algorithm is quite fast.