Intelligent reflecting surface (IRS) is gradually being recognized as a promising technology for improving spectral and energy efficiency of wireless systems. In this letter, we study an IRS-assisted multiple-input single-output (MISO) system where a base station (BS) with multiple antennas arranged in a uniform rectangular array (URA) serves a single-antenna user with the help of an IRS with multiple elements arranged in a URA. We consider a Rician fading model, where the non-line of sight (NLoS) components vary slowly and the line of sight (LoS) components do not change. To reduce costs for channel estimation and phase adjustment, we adopt fixed maximum-ratio transmission (MRT) at the BS, fixed phase shifts at the IRS and constant rate transmission. First, we obtain the expression of the outage probability. Then, we minimize the outage probability by optimizing the phase shifts of the IRS. Finally, we obtain the expression of the asymptotically optimal outage probability in the high signal-to-noise ratio (SNR) regime. We also characterize the impacts of several key system parameters on the optimal outage probability to reveal important design insights.

Integrated sensing and communication (ISAC) is an essential technique for overcoming spectrum congestion. However, the performance of traditional ISAC systems with fixed-position-antennas (FPA) is limited due to insufficient spatial degree of freedom (DoF) exploration. Recently, fluid antenna (FA) with reconfigurable antenna position is developed to enhance the sensing and communication performance by reshaping the channel. This paper investigates an FA-enhanced ISAC system where a base station is equipped with multiple FAs to communicate with multiple single-antenna users and with FPAs to sense a point target. In this paper, we consider both perfect and imperfect channel state information (CSI) of the communication channel and sensing channel. In two cases, we focus on the maximization of the sensing signal-to-noise (SNR) by optimizing the positions of FAs and the dual-functional beamforming under the constraints of the FA moving region, the minimum FA distance and the minimum signal-to-interference-plus-noise (SINR) per user. Specifically, for the ideal case of perfect CSI, an iterative alternating optimization (AO) algorithm is proposed to tackle the formulated problem where the dual-functional beamforming and the FA positions are obtained via semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques. Then, for the imperfect CSI case, we propose an AO-based iterative algorithm where $\mathcal {S}-$Procedure and SCA are applied to obtain the dual-functional beamforming and the FA positions. Furthermore, we analytically and numerically prove the convergence of the proposed algorithms. Numerical results demonstrate the notable gains and effectiveness of the proposed algorithms over baseline schemes.

Caching and multicasting are two promising methods to support massive content delivery in multi-tier wireless networks. In this paper, we consider a random caching and multicasting scheme with caching distributions in the two tiers as design parameters, to achieve efficient content dissemination in a two-tier large-scale cache-enabled wireless multicasting network. First, we derive tractable expressions for the successful transmission probabilities in the general region as well as the high signal-to-noise ratio (SNR) and high user density region, respectively, utilizing tools from stochastic geometry. Then, for the case of a single operator for the two tiers, we formulate the optimal joint caching design problem to maximize the successful transmission probability in the asymptotic region, which is nonconvex in general. By using the block successive approximate optimization technique, we develop an iterative algorithm, which is shown to converge to a stationary point. Next, for the case of two different operators, one for each tier, we formulate the competitive caching design game where each tier maximizes its successful transmission probability in the asymptotic region. We show that the game has a unique Nash equilibrium (NE) and adopt an iterative algorithm, which is shown to converge to the NE under a mild condition. Finally, by numerical simulations, we show that the proposed designs achieve significant gains over existing schemes.

In this letter, we consider optimal hybrid beamforming design to minimize the transmission power under individual signal-to-interference-plus-noise ratio (SINR) constraints in a multiuser massive multiple-input-multiple-output (MIMO) system. This results in a challenging non-convex optimization problem. We consider two cases. In the case where the number of users is smaller than or equal to that of radio frequency (RF) chains, we propose a low-complexity method to obtain a globally optimal solution and show that it achieves the same transmission power as an optimal fully-digital beamformer. In the case where the number of users is larger than that of RF chains, we propose a low-complexity globally convergent alternating algorithm to obtain a stationary point.