Multi-antenna millimeter-wave (mmWave) communication systems have become a promising approach to improve throughput. However, mmWave narrow beams are susceptible to blockages, making it difficult to guarantee continuous services for ultra-reliable and low-latency communications (URLLC). To address this issue, we propose a novel dual-path beam tracking framework and develop a Recurrent Neural Network-based Constrained Deep Reinforcement Learning (RCDRL) algorithm to optimize the beam search sets of the beam tracking algorithm. The objective is to minimize the total time and frequency resources allocated for beam sweeping, beam tracking, and data transmission, subject to the constraint on the service interruption probability of URLLC. A pre-training method is developed to improve the initial performance and stability of the RCDRL algorithm. Comprehensive evaluation results indicate that the proposed approach outperforms other baseline methods in terms of the tradeoff between service interruption probability and resource utilization efficiency. Specifically, the RCDRL algorithm reduces the service interruption probability by three orders of magnitude compared with a standardized single-beam tracking method at the cost of sacrificing the resource utilization efficiency by 14.6%. In addition, our policy achieves lower service interruption probability and higher resource utilization efficiency compared with an existing dual-beam tracking algorithm.

We propose a new spectrum allocation strategy, aided by unsupervised learning, for multiuser terahertz communication systems. In this strategy, adaptive sub-band bandwidth is considered such that the spectrum of interest can be divided into sub-bands with unequal bandwidths. This strategy reduces the variation in molecular absorption loss among the users, leading to the improved data rate performance. We first formulate an optimization problem to determine the optimal sub-band bandwidth and transmit power, and then propose the unsupervised learning-based approach to obtaining the near-optimal solution to this problem. In the proposed approach, we first train a deep neural network (DNN) while utilizing a loss function that is inspired by the Lagrangian of the formulated problem. Then using the trained DNN, we approximate the near-optimal solutions. Numerical results demonstrate that comparing to existing approaches, our proposed unsupervised learning-based approach achieves a higher data rate, especially when the molecular absorption coefficient within the spectrum of interest varies in a highly non-linear manner.

We propose to use channel inversion power control (CIPC) to achieve one-way ultra-reliable and lowlatency communications (URLLC), where only the transmission in one direction requires ultra reliability and low latency. Based on channel reciprocity, our proposed CIPC schemes guarantee the power of received signal that is used to decode the information to be a constant value$Q$, by varying the transmit signal and power, which relaxes the assumption of knowing channel state information (CSI) at the user. Thus, the CIPC schemes eliminate the overhead of CSI feedback, reduce communication latency, and explore the benefits of multiple antennas to significantly improve transmission reliability. We derive analytical expressions for the packet loss probability of the proposed CIPC schemes, based on which we determine a closed interval and a convex set for optimizing$Q$in CIPC with imperfect and perfect channel reciprocity, respectively. Our results show that CIPC is an effective means to achieve one-way URLLC. The tradeoff among reliability, latency, and required resources (e.g., transmit antennas) is further revealed, which provides novel principles for designing one-way URLLC systems.

Physical layer security (PLS) is promising for secure short-packet transmissions in ultra-reliable and low-latency communications. The bottlenecks of applying PLS in practice include 1) lack of accurate channel state information (CSI) of both the intended user and the eavesdropper; 2) high computational complexity for solving optimization problems. To address the first issue, we compare the secure transmission rates of short packets in different scenarios (i.e., with/without eavesdropper’s instantaneous CSI and with/without channel estimation errors) and derive the closed-form optimal power control policy in a special case. To find numerical solutions in general cases, we apply an unsupervised deep learning method, which has low complexity after the training stage. Through numerical results, we obtain the following three key findings: 1) The learning-based power control policy approaches the closed-form optimal policy in the special case and outperforms two existing power control policies in general cases. 2) Knowing the instantaneous CSI of the eavesdropper only provides a marginal gain of the secure data rate in the high signal-to-noise ratio regime. 3) In the presence of channel estimation errors, the learning-based policy trained by the estimated channels can guarantee the average transmit power constraint, while the closed-form policy cannot.