In this paper, we study a robust beamforming design in a downlink reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) communication system with consideration of bounded channel uncertainty. The goal of the design is to minimize the transmit power by employing the symbol-level precoding (SLP) at the base station (BS), while satisfying the quality-of-service requirements of the primary users (PU) and secondary user (SU). Unlike most existing works where RIS is only a signal reflector, in this paper, RIS also operates as a transmitter and delivers the secondary information to SU by switching the reflecting beamformers. In single-PU scenario, the secondary information recovery (SIR) scheme is proposed and a power minimization problem is formulated. To tackle the non-convex problem, we decompose the problem into two sub-problems to alternately optimize the transmit and reflecting beamformers. Then, two algorithms namely the penalty-based algorithm and the monotone accelerated projected gradient-based algorithm are proposed to address the unit-modulus constraint on the reflecting beamformer. The problem is further extended to the multi-PU scenario and the extended SIR scheme is provided. Finally, the simulation results demonstrate the effectiveness of our proposed algorithms and exhibit the advantages of the SIR scheme.

The millimeter-wave (mmWave) train-to-ground (T2G) communications is an essential enabling technology for future intelligent railways, where the acquisition of beam alignment information is one of the most challenging and significant issues. Hence, in this paper, we investigate the optimal beamformers and performance bounds for the mmWave T2G beam alignment with the aid of train location information. We first propose a mmWave T2G system model, which can be used by arbitrary array geometry and identifies a clear relationship between the T2G scenario and the wireless channel. Then, based on the Cramer Rao bound (CRB), we provide two bounds characterizing the average and worst minimum mean square error (MMSE) of beam alignment with the bounded error model of train location. Next, two non-convex optimization problems are formulated aiming to find the transmitting beamformers that can minimize the bounds, which is solved optimally by relaxation and recovery techniques. Finally, numerical simulations are conducted to validate the proposed beamformers and bounds for the mmWave T2G beam alignment. In particular, the results show that the MSE performance of optimal beamformers converges to the bounds by applying the maximum likelihood estimator (MLE) in the high SNR regime.

In this paper, we consider a beampattern design problem for a dual-functional radar-communication (DFRC) system with the aid of the reconfigurable intelligent surface (RIS). The goal of the design is to meet the communication requirements of the users while the beampattern can match the ideal beampattern. We formulate the beampattern design as a non- convex optimization problem by jointly optimizing the transmit and passive beamformers. To solve the problem, three methods, namely the semidefinite relaxation (SDR)-based method, penalty- based method, and Riemannian conjugate gradient (RCG)-based method are proposed. The simulation results illustrate that: i) the radar performance can be significantly enhanced with the aid of RIS; ii) a tradeoff should be made between the communication performance and radar performance; iii) 4 control bits are sufficient to make the performance close to that of continuous phase shift.

This paper considers a positioning problem of high-speed trains (HST) in railway wireless network by utilizing the 5G new radio (NR) positioning reference signals (PRS). It is assumed that the train runs along the railroad at a fixed velocity and receives PRS signals from the base stations (BSs) deployed on the side of the railroad. To deal with the positioning problem, an iterative two-phase weighted least squares (I2WLS) method based on range difference of arrival (RDOA) measurements is proposed, which linearizes the RDOA equations to pseudo-linear ones. The accuracy gap between RMSE of the proposed approach and Cramer-Rao lower bound (CRLB) is small when the RDOA measurements noise level is sufficiently small. Furthermore, the simulation results illustrate that a high, centimeter-level accuracy can be achieved for small PRS intervals, velocities and base station distances.

In this paper, we investigate a robust beamforming design in a millimeter wave (mmWave) communication network with consideration of the random blockages. The network with multiple remote radio units (RRUs) is taken into account, where the joint transmission coordinated multi-point (JT-CoMP) scheme is adopted to improve the spectrum efficiency. To further enhance the communication performance and maintain the reliability of the network, an active intelligent reflecting surface (IRS) panel is deployed. We formulate the robust beamforming design of the mmWave communication network as an optimization problem aiming at minimizing the total transmit power at the RRUs subject to the average signal-to-interference-plus-noise ratio (SINR) constraints and power constraint over the active IRS. To deal with the challenge caused by the variables coupling, an algorithm based on the alternating optimization (AO) and semidefinite programming (SDP) is proposed. Numerical results illustrate that: i) the transmit power of the network can be reduced by deploying both the passive and active IRSs; ii) the integration of the active IRS and CoMP scheme can obtain a significant performance gain compared to that of the conventional passive IRS and CoMP.

For the ultra-reliable low-latency communication (URLLC), the existing joint design algorithms of channel training and data transmission are not applicable due to the stringent reliability requirement and limited blocklength. To address this issue, we develop a low-complexity joint design framework for MISO communication based on the finite blocklength code (FBC). Specifically, an approximate bound of the packet error probability (PEP) is first derived and validated by practical modulation and coding schemes. It reveals the inherent tension between reliability, latency, and information bit number. Then, we formulate the joint design into a nonconvex optimization problem with the objective to maximize the information bit number. By exploiting the monotonicity of the PEP approximate bound, we provide closed-form solutions of power and blocklength allocation. Thereby, we develop a low-complexity algorithm to support the URLLC services aiming at the information bit number maximization. Furthermore, we investigate the joint designs to optimize the reliability, latency, and total energy, respectively, to fully meet the diverse demands of URLLC services. Finally, numerical results are provided to validate the proposed joint designs. The results show the outage capacity-based design severely underestimates the required wireless resources.