To exploit the potential of the reconfigurable intelligent surface (RIS) in supporting integrated sensing and communication (ISAC), this paper proposes a novel joint active and passive beamforming design for RIS-enabled ISAC system in consideration of the target size. First, the detection probability for target sensing is derived in closed-form based on the illumination power on an approximated scattering surface area of the target, and a new concept of ultimate detection resolution (UDR) is defined for the first time to measure the target detection capability. Then, an optimization problem is formulated to maximize the signal-to-noise ratio (SNR) at the user-equipment (UE) under a minimum detection probability constraint. To solve this non-convex problem, a novel alternative optimization approach is developed. In this approach, the solutions of the communication and sensing beamformers are obtained by our proposed bisection-search based method. The optimal receive combining vector is derived from an equivalent Rayleigh-quotient problem. To optimize the RIS phase shifts, the Charnes-Cooper transformation is conducted to cope with the fractional objective, and a novel convexification process is proposed to convexify the detection probability constraint with matrix operations and a real-valued first-order Taylor expansion. After the convexification, a successive convex approximation (SCA) based algorithm is designed to yield a suboptimal phase-shift solution. Finally, the overall optimization algorithm is built, followed by detailed analyses on its computational complexity, convergence behavior and problem feasibility condition. Extensive simulations are carried out to testify the analytical properties of the proposed beamforming design, and to reveal two important trade-offs, namely, communication vs. sensing trade-off and UDR vs. sensing-duration trade-off. In comparison with several existing benchmarks, our proposed approach is validated to be superior when detecting targets with practical sizes.

Intelligent reflecting surface (IRS) is envisioned as a promising hardware solution to hardware cost and energy consumption in the fifth-generation (5G) mobile communication network. It exhibits great advantages in enhancing data transmission, but may suffer from performance degradation caused by inherent hardware impairment (HWI). For analysing the achievable rate (ACR) and optimizing the phase shifts in the IRS-aided wireless communication system with HWI, we consider that the HWI appears at both the IRS and the signal transceivers. On this foundation, first, we derive the closed-form expression of the average ACR and the IRS utility. Then, we formulate optimization problems to optimize the IRS phase shifts by maximizing the signal-to-noise ratio (SNR) at the receiver side, and obtain the solution by transforming non-convex problems into semidefinite programming (SDP) problems. Subsequently, we compare the IRS with the conventional decode-and-forward (DF) relay in terms of the ACR and the utility. Finally, we carry out simulations to verify the theoretical analysis, and evaluate the impact of the channel estimation errors and residual phase noises on the optimization performance. Our results reveal that the HWI reduces the ACR and the IRS utility, and begets more serious performance degradation with more reflecting elements. Although the HWI has an impact on the IRS, it still leaves opportunities for the IRS to surpass the conventional DF relay, when the number of reflecting elements is large enough or the transmitting power is sufficiently high.

This paper investigates a dual-unmanned aerial vehicle (UAV)-enabled secure communication system, in which, a UAV moves around to send confidential messages to a mobile user while another cooperative UAV transmits artificial noise signals to confuse malicious eavesdroppers. Both UAVs have energy constraints and the location information of eavesdroppers is imperfect. We consider a worst-case secrecy rate maximization problem of the mobile user over all time slots. This optimization problem is solved by jointly designing the three-dimensional (3D) trajectory of UAVs and the time allocation (recharging and service or jamming) under practical constraints including maximum UAV speed, UAV collision avoidance, UAV positioning error, and UAV energy harvesting. Specifically, we adopt a more practical UAV-ground channel model with both large-scale and small-scale fading components. Due to the non-convex feasible region constructed by the complicated constraints, directly finding the optimal solution of the original problem is intractable. To address this issue, we decouple the original optimization problem into three subproblems and develop an iterative algorithm to find its suboptimal solution by using the block coordinate descent technique. To solve each subproblem, certain advanced optimization tools, such as integer relaxation, S-procedure, and successive convex approximation techniques, are utilized. Numerical simulation results are provided to corroborate the theoretical derivations and to evaluate the performance of the proposed algorithm. Additionally, the numerical results assist to draw new insights on the 3D UAV trajectory by comparing the performance with conventional two-dimensional (2D) schemes.

In this paper, we study the user localization and tracking problem in the reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) system, where a multi-antenna base station (BS) and multiple RISs are deployed to assist the localization and tracking of a multi-antenna user. By establishing a probability transition model for user mobility, we develop a message-passing algorithm, termed the Bayesian user localization and tracking (BULT) algorithm, to estimate and track the user position and the angles-of-arrivals (AoAs) at the user in an online fashion. We also derive the Bayesian Cramér Rao bound (BCRB) to characterize the fundamental performance limit of the considered tracking problem. To improve the tracking performance, we optimize the beamforming design at the BS and the RISs to minimize the derived BCRB. Simulation results show that our BULT algorithm can perform close to the derived BCRB, and significantly outperforms the counterpart algorithms without exploiting the temporal correlation of the user location.

Joint pushing and caching are commonly considered an effective way to adapt to tidal effects in networks. However, the problem of how to precisely predict users’ future requests and push or cache the proper content remains to be solved. In this paper, we investigate a joint pushing and caching policy in a general mobile edge computing (MEC) network with multiuser and multicast data. We formulate the joint pushing and caching problem as an infinite-horizon average-cost Markov decision process (MDP). Our aim is not only to maximize bandwidth utilization but also to decrease the total quantity of data transmitted. Then, a joint pushing and caching policy based on hierarchical reinforcement learning (HRL) is proposed, which considers both long-term file popularity and short-term temporal correlations of user requests to fully utilize bandwidth. To address the curse of dimensionality, we apply a divide-and-conquer strategy to decompose the joint base station and user cache optimization problem into two subproblems: the user cache optimization subproblem and the base station cache optimization subproblem. We apply value function approximation Q-learning and a deep Q-network (DQN) to solve these two subproblems. Furthermore, we provide some insights into the design of deep reinforcement learning in network caching. The simulation results show that the proposed policy can learn content popularity very well and predict users’ future demands precisely. Our approach outperforms existing schemes on various parameters including the base station cache size, the number of users and the total number of files in multiple scenarios.

The intelligent reflecting surface (IRS) is promising in assisting user localization and wireless communication in the future wireless networks. In this paper, a novel IRS-aided joint localization and communication (L&C) scheme is designed in a millimeter-wave transmission system. For the proposed scheme, the user position/orientation estimation error bound (POEB) and the effective achievable data rate (EADR) are derived in closed-form as L&C performance metrics, which reveal the inherent trade-off between L&C capabilities. To achieve the joint optimal point of the POEB and EADR in consideration of the localization errors, a worst-case robust beamforming and time allocation optimization problem is formulated. To solve the original non-convex problem, a novel joint optimization approach is developed. Specifically, from an equivalent minimax problem, the local optimal solutions of the transceiver beamformers, the IRS phase-shift matrix, and the time allocation ratio between user localization stage (ULS) and effective data transmission stage (EDTS), are obtained in closed-form with respect to the localization errors. Then, the worst-case localization error is iteratively found by a dedicated majorize-minimization (MM) based algorithm. Subsequently, potential extensions to general wireless channels and discrete phase-shift models are discussed in detail. Finally, simulations are carried out to show the optimization results and the L&C performance trade-off. In comparison with the conventional non-robust method, the proposed approach is validated to be robust against the user localization uncertainty.