This paper proposes a double phase shifter–based hybrid beamforming (DPS-HBF) framework for millimeter-wave NOMA systems, enabling the simultaneous realization of beam steering and beam focusing within a unified analog architecture. By superposing two independent phase-shifter vectors per beam, DPS-HBF flexibly supports heterogeneous near-field and far-field users without requiring full channel state information. To exploit this capability, a hierarchical scheduling framework combining Bitmask Dynamic Programming, Maximum Weight Matching, andk-best Semi-Greedy User Scheduling is developed to balance optimality, scalability, and computational complexity. The proposed design relies solely on low-overhead SINR feedback, making it suitable for practical large-scale deployments. Simulation results show that DPS-HBF consistently outperforms existing hybrid beamforming and orthogonal multiple access baselines in terms of sum-rate and fairness, achieving up to 30–35% throughput gains over the strongest benchmark under moderate-to-high SNR conditions.

We investigate a heterogeneous network (HetNet) including sub-6GHz base stations (BSs), mmWave BSs, and THz BSs to support enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users. We particularly investigate a user-centric network in which the users locally and dynamically select and switch among BSs over time to achieve their highest utility. Two types of users have different Quality of Service (QoS) requirements. Thus, we design two types of utility functions specifically for the eMBB users and URLLC users. Then, to model the dynamic selection behavior of the users, we propose to use a fractional game with the power-law memory. The fractional game allows the eMBB users and the URLLC users to incorporate their past strategies into their current selection, thus improving their utility. Furthermore, we consider the case that the BSs communicate the system state with each other, and we model the network selection of the users as a multi-agent problem. Then, we propose to use a multi-agent deep reinforcement learning (MADRL) algorithm that enables the URLLC users and eMBB users to make their network selection decision online to achieve their long-term utility. Various simulation results are provided to demonstrate the scalability and effectiveness of the proposed approaches. Particularly, compared with the classical game, the fractional game is able to achieve a higher utility but incurs a higher network adaptation cost. Moreover, the different types of URLLC users (in terms of latency and reliability requirements) and the number of URLLC users in the network significantly affect the total utility and the network selection strategies of the eMBB users. Importantly, given the full observations, the MADRL outperforms both classical and fractional games in terms of total network utility.

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.

In this paper, we propose a coded distributed computing (CDC)-based vehicular edge computing (VEC) framework. The framework allows a task vehicle (TV) equipped with the dual-function radar communication (DFRC) to offload its computing tasks to the nearby service vehicles (SVs) using the (m, k) maximum distance separable (MDS). The framework is thus able to address the straggler effect that is typically caused by the high mobility of the vehicles. We then formulate an optimization problem for the TV that aims to i) minimize the overall computing latency, ii) minimize the offloading cost, and iii) maximize the radar range subject to the connection duration. For this, we optimize the MDS parameters, i.e., the number of selected SVs (m) and the number of subtasks for coding (k), and the fractions of power allocated to the radar and communication functions. Under the high dynamic vehicular environment, the uncertainty of the SVs’ computing resource and networking resources, we propose a deep reinforcement learning (DRL) algorithm based on Double Deep Q-Network (DDQN) to solve the TV’s problem. To further improve the performance, we propose to incorporate a parameter norm penalty in the loss function. Simulation results show that the proposed DDQN algorithm outperforms both the DQN algorithm and the non-learning algorithm in terms of computation latency, radar range, and offloading cost.

In this study, the performance of wirelessly powered relay‐aided non‐orthogonal multiple access networks is investigated in terms of outage probability. Specifically, two relay selection strategies, i.e. two‐stage relay selection (TRS) and maximum energy harvesting relay selection (MEHS), and two energy harvesting scenarios, i.e. time switching (TS) and power splitting (PS) are considered. In each setup, outage probabilities' analytical expressions and their asymptotics are derived. Monte‐Carlo simulations are also carried out to verify the correctness of the analysis. The results show that regardless of relay selection and energy harvesting strategies, increasing transmit power can improve the proposed system performance. However, TRS can achieve a full diversity order, while MEHS has a unit diversity order. Besides, the results recommend parameter selections of PS and TS coefficients for optimal performance in term of outage probability.