As a low-cost and low-power-consumption passive reflector, intelligent reflecting surface (IRS) can make a significant rate improvement by building a programmable wireless environment. To improve the rate performance and coverage range of wireless networks, an IRS-aided decode-and-forward (DF) relay network is proposed with multiple antennas at relay station (RS). To achieve a high rate, an alternately iterative structure (AIS) of maximizing receive power (Max-RP) at RS is proposed to jointly optimize the beamforming vectors at RS and phase shifts at IRS. Considering its high-complexity, two low-complexity Max-RP schemes of null-space projection (NSP) plus maximum ratio combining (MRC) and IRS element selection (IRSES) plus MRC are presented to reduce this complexity, respectively. For the former, NSP is used to separate the reflected signal from IRS and the direct transmitted signal from source and MRC is adopted to combine the two signals at RS. For the latter, the basic concept of IRSES is as follows: IRS is partitioned into ${M}$ subsets of elements and adjusting the phases of all elements per subset make all reflected signals and the direct signal from source phase alignment (PA) at the corresponding antenna of relay. Simulation results show that the proposed three methods perform much better than the existing network with single-antenna relay in terms of rate performance. In particular, a 85% rate gain over existing scheme is achieved in the high signal-to-noise ratio region. Moreover, it is verified that the positions of RS and IRS have a substantial impact on rate performance, and there exists an optimal positions of RS and IRS.

In this article, an effective energy-efficiency optimization problem is investigated in the cognitive unmanned aerial vehicle (UAV) communication system, where the moving following UAV (FUAV) transmits collected data to the leading UAV (LUAV) by reusing the spectrum of the ground primary user. For this scenario, a novel joint UAV trajectory and resource allocation optimization algorithm is proposed. We aim to maximize the energy efficiency of the cognitive UAV communication systems under interference, shortest step size, collision prevention, and speed constraints. However, this optimization problem is difficult to be solved, as it is nonconvex and involves strong relations among many variables. To address this issue, we first decompose the original optimization problem into four subproblems: 1) spectrum sensing duration subproblem; 2) spectrum sensing threshold subproblem; 3) transmitted power subproblem; and 4) FUAV trajectory optimization subproblem. For spectrum sensing duration subproblem and spectrum sensing threshold subproblem, their optimal solutions can be efficiently solved via a golden section search method. For the transmitted power subproblem, the closed-form expression of the optimal transmitted power is deduced. For the nonconvex FUAV trajectory optimization subproblem, we consider the shortest step size design under known starting and destination positions and then obtain an approximate solution of FUAV trajectory via the successive convex optimization (SCO) technique. Finally, an alternate iterative framework with fast convergence is designed to solve the original optimization problem. The simulation results show that the proposed algorithm has 18 times improvement in energy efficiency compared with the straight flight scheme with lower energy consumption.

In the unmanned aerial vehicle (UAV) enabled wireless communication system, the rapid channel change caused by high-speed UAV may cause the beam misalignment problem. It is a challenge to fast establish and maintain the communications link between UAV and ground base stations. To address this challenge, an IRS-aided high-speed UAV adaptive beam alignment (IRS-UAV-ABA) scheme utilizing double-layer long short-term memory (DL-LSTM) spatial angle prediction model is proposed to improve the effectiveness and reliability of the UAV communication. Firstly, DL-LSTM enabled spatial angle prediction is modeled to facilitate the calculation of the beam coverage adapted to the UAV real-time flight trajectory. Secondly, the non-convex optimization problem for maximizing the received signal-to-noise ratio of moving UAV is formulated to obtain the suboptimal solutions of the BS’ beamforming vector and the IRS’ phase shift matrix. Finally, with the aforementioned beamforming vectors, the phase shift matrix and the adaptive beam coverage, the adaptive dual-beams alignment from BS to UAV and from IRS to UAV can be achieved by the proposed DL-LSTM-enabled IRS-UAV-ABA algorithm. Simulation results verify that the proposed scheme not only can effectively avoid the beam misalignment caused by the mobility and perturbation of UAV, but also has superior spectrum efficiency and stable beamforming gain.

In this letter, the novel intelligent reflecting surface (IRS)-assisted cognitive unmanned aerial vehicle systems (CUAVS) are investigated, where the UAV serves as a mobile node sensing spectrum state of primary user (PU) and transmitting data to ground secondary users (SUs), while IRS is exploited to assist both the UAV sensing and transmission in CUAVS. Our aim is to maximize the effective throughput of CUAVS by jointly optimizing the spectrum sensing duration and the UAV’s trajectory, as well as IRS phase shift matrices in the sensing and transmission stages, where UAV needs to balance its relative location among IRS, primary transmitter, PU and SUs. To solve this challenging optimization problem, the original problem is divided into four sub-problems, and these sub-problems are solved by exploiting the bisection search, semidefinite relaxation, Gaussian randomization and successive convex approximation techniques, respectively. Numerical results demonstrate that our proposed design can significantly enhance the effective throughput of CUAVS.

A novel intelligent reflecting surface (IRS)-assisted multi-antenna uncrewed aerial vehicle (UAV) covert communication (IRS-UAVCC) with a finite blocklength is studied, in which the IRS is exploited to enhance the covert transmission capability of moving multi-antenna UAV and weaken the detection capability of a warden. Our goal is to maximize the UAV covert transmission rate by jointly optimizing the multi-antenna UAV’s trajectory (UAVTR) and transmit beamforming (UAVTB), and the IRS’s phase shift matrix (PSM). To tackle this non-convex problem, we decompose it into three sub-problems. Firstly, the semidefinite relaxation (SDR) technique is employed to solve the two sub-problems of UAVTB and IRS’s PSM. Secondly, by applying the successive convex approximation (SCA) technique, the non-convex multi-antenna UAVTR optimization sub-problem is reformulated into a convex one. Finally, an efficient block coordinate descent (BCD) structure is proposed to obtain a suboptimal solution for the original optimization problem. To further reduce the complexity, a low complexity penalty dual decomposition with gradient projection (PDDGP) method is developed to obtain the optimal moving multi-antenna UAVTB and the IRS’s PSM. Numerical results demonstrate the effectiveness and superiority of the proposed algorithm in IRS-UAVCC with a finite blocklength.

In this article, to enhance the communication quality of cognitive unmanned aerial vehicle networks (CUAVNs), we investigate the intelligent reflecting surface (IRS)-assisted CUAVNs, where a leading UAV (LUAV) is deployed for sensing spectrum and a group of following UAVs (FUAVs) transmit data to LUAV with the aid of IRS. Our objective is to maximize the achievable throughput of CUAVNs by jointly optimizing sensing duration, IRS passive beamforming, and LUAV’s 3-D location, where LUAV’s 3-D location is restricted by IRS, FUAVs, and primary user. For the IRS-assisted single FUAV case, the intractable nonconvex optimization problem is resolved into three subproblems, which are solved by utilizing the bisection search method, the closed-form expression of the optimal IRS phase shift matrix and the successive convex approximation method, respectively. Finally, an efficient alternating optimization algorithm is developed to obtain a high-quality suboptimal solution. By exploiting the solutions of single FUAV, we further propose the throughput weighted sum (TWS) algorithm to solve the intricate nonconvex problem in IRS-assisted multiple FUAVs case. To further reduce the complexity of TWS, the low-complexity location weighted sum (LWS) algorithm is proposed. Numerical results show that compared to the scheme without IRS assistance, the achievable throughput increases about 102% with the proposed single FUAV scheme, and over 88% with the proposed TWS-based multiple FUAVs scheme. Moreover, the performance gap between the low-complexity LWS and the TWS is less than 7%.