Joint Beamforming Design and Blocklength Optimization for Low-Latency Multiuser MISO URLLC Systems

To satisfy the requirements of many industrial applications, realizing ultrareliable low-latency communication (URLLC) has become one of the major challenges for future wireless networks. This article considers a downlink multiuser multiple-input-single-output (MISO) system in the Internet of Things (IoT) networks, in which a multiantenna base station (BS) serves multiple delay-sensitive IoT users, each equipped with a single antenna. To minimize the overall end-to-end delay, we jointly optimize the beamforming vectors and the packet blocklength to balance the queuing delay and the transmission delay. The problem is formulated as a Markov decision process (MDP), whose optimal solution can be theoretically found. However, the complexity on finding the optimal resource allocation and blocklength selection strategy is prohibitively high for real-system deployments due to the large state and action space. To overcome this issue, we simplify the original problem and develop an iterative algorithm to solve the simplified problem based on the uplink-downlink duality theory. Since solving the simplified problem would result in suboptimal solutions and may degrade the latency performance, we further develop a deep-reinforcement-learning (DRL)-based beamforming and blocklength selection framework to efficiently learn the optimal strategy of the original MDP. Simulation results demonstrate that the proposed algorithms can effectively improve the latency performance compared with the benchmark algorithm.