Extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems introduce a new communication paradigm called near-field communications, which identifies users’ location within the near-field (Fresnel’s region). In the near-field, beams can be steered in the angle and distance dimensions, resulting in an enormous codebook and a prolonged two-dimensional beam alignment (BA) process. To keep a low BA overhead while achieving low BA error, in this paper, we design a novel hierarchical codebook and a BA scheme for near-field XL-MIMO systems. Specifically, we first propose a novel spatial partition where the angle-offset effect is revealed and leveraged to improve the beam gain inside the coverage area. Based on the partition, we design distance-coarse and focusing beams. Distance-coarse beams are leveraged to construct the high level of the codebook for angle dimension alignment. In contrast, focusing beams construct the last level codebook for distance dimension alignment. Corresponding to the proposed codebook structure, our BA scheme is a tree search consisting of two stages: the angle aligning stage and the distance aligning stage. Next, we formulate the desired codebook design problem as difference convex optimization problems, where three beam design guidelines are considered to minimize the BA error rate raised by the near-field angle-offset effect. After that, the proposed optimization problem is solved by the constrained concave-convex procedure. Numerical simulations verify the angle offset effect and our designed near-field beam. Furthermore, we show that our BA scheme only utilizes one percent of overhead but achieves a lower BA error rate than exhaustive searching.

Large antenna arrays and beamforming are necessary for the mmWave communication system, resulting in heavy time and energy consumption in the beam training stage. Therefore, dual-band operations are expected to be deployed in future communication systems, where low-frequency channels are used to meet basic communication needs, and millimeter wave (mmWave) channels are exploited when the high-rate transmission is required. Existing works utilize deep learning methods to extract low-frequency channel state information (CSI) to reduce the mmWave beam training overheads. However, an important limitation of deep learning approaches is that the model is usually trained in a given environment. When employed in an unseen environment, it usually requires a large amount of data to retrain. In this paper, a model-agnostic optimization algorithm based on meta-learning is proposed to provide a general mmWave beam prediction model. This model can be deployed to edge base stations and effectively adapted to the environment without the need for a heavy collection of data. Simulation results demonstrate that the proposed approach could reduce the model adaptation overheads. The meta-learning-based beam prediction model is robust and achieves high prediction accuracy and spectral efficiency in different signal-to-noise ratio (SNR) regimes.

Learning-based resource allocation (LRA) is envisioned as an integral element of 6G. This article proposes a novel learning-based paradigm to address resource allocation problems in heterogeneous ultradense networks (HUDNs). Our paradigm is a highly efficient realization for utilizing the inherence properties in HUDN, which comprise the local validity and correlation attenuation. Concretely, we formulate the HUDNs as a heterogeneous bipartite graph model and propose the corresponding heterogeneous bipartite graph neural network (HBGNN). The local sampling characteristic of HBGNN matches the inherence properties. Meanwhile, our paradigm combines data-driven and model-driven learnings and employs online and offline trainings. Hence, two of LRA’s obstacles: 1) the overreliance on the perfect data set and 2) the low calculation efficiency are mitigated, and the realizability of our paradigm is improved. Besides, entropy regularization is utilized to guarantee the effectiveness of exploration in the configuration space. We apply our approach to a representative resource allocation problem, the jointly user association (UA) and power allocation (JUAPA) problem. We formulate JUAPA as a combination classification and regression problem and adopt a dynamical hyperparameter output layer to address the discrete variable of UA. Simulation results demonstrate that the proposed method has better performance and higher computational efficiency than traditional optimization algorithms.

Federated learning (FL) is an emerging paradigm for distributed machine learning that uses the data and the computational power of user devices while maintaining user privacy (e.g., position and motion track). It has been proved a promising way to help the learning-based millimeter wave (mmWave) system achieve efficient link configuration. However, FL systems have an inherent vulnerability to backdoor attacks during training, and this has not received attention in current FL-based beam selection research. The goal of a backdoor attacker is to implant a backdoor in the model such that at test time, the model will mispredict a certain family of inputs, and corrupt the performance of the trained model on specific sub-tasks. We study backdoor attacks in an FL-based beam selection system based on a deep neural network that utilizes user location information. Specifically, we propose a backdoor attack scheme that can be configured in the real world. The attacker’s trigger is an obstacle placed in certain locations. When the model encounters an input with these obstacles, the backdoor will be triggered, and the model will output the beam specified by the attacker. Through experiments, we show that the proposed attack can achieve a high attack success rate in a system without a defense mechanism. Moreover, we show that the traditional norm-clipping defense method cannot effectively defend against our attack. Furthermore, we propose a new backdoor attack defense method and verify the effectiveness of this scheme through experiments. In addition, we propose a backdoor detection method: the federated noise titration method, which can diagnose whether the model has a backdoor. Overall, our work explored backdoor attacks, defenses, and detection of the FL-based mmWave beam selection system.

In this paper, we investigate the navigation for unmanned aerial vehicle (UAV)s in the secure communication system, where we design the UAV's navigation/trajectory to ensure the Quality of Service (QoS) with the Base Station (BS) in the existence of multiple unknown-location dynamical eavesdroppers and jammers. To this end, we formulate a UAV trajectory optimization problem to minimize its mission completion time with QoS and security constraints. The imperfect information, dynamic communication environment, and non-convexity make the problem intractable. For these reasons, we propose a novel solution approach, namely Model-Assisted Reinforcement Learning (MARL) algorithm, where the communication system model is embedded into Deep Reinforcement Learning (DRL) framework to ensure secure communication and shorten the learning process. Numerical results show that our proposed methodology can safeguard security and find the shortest way to finish the mission.

Ultra-Dense Network (UDN) has become a key technology in 5G communication systems. By deploying low power micro base stations (BSs) densely and flexibly to reduce the distance between access nodes and user equipments (UEs), the spectrum efficiency and energy efficiency of the network can be improved effectively. But at the same time, it also poses new challenges for power control and user association. In this paper, the joint optimization problem of user association and power control of the downlink in a UDN scenario is considered. To make full use of channel information, we build a graph model with UEs as nodes and leverage the Spectral Clustering algorithm for user association. Then we build a graph model with BSs as nodes for the UDN scenario and train an unsupervised graph neural network to achieve power allocation. The analysis of the simulation results verifies the convergence of the proposed scheme which is effective in achieving user association and power control in UDN.