This article investigates simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted downlink multiuser multiple-input–single-output (MU-MISO) networks with the rate splitting multiple access (RSMA) scheme. A base station (BS) desires to simultaneously transmit messages to multiple users with the assistance of an STAR-RIS to enhance communication quality as well as extend the coverage of users. An optimization problem is formulated to maximize the achievable sum rate of the networks on the premise of satisfying the constraints on power budget at the BS, total common-stream rate of users, and individual users’ minimum rate requirements, via jointly optimizing the beamforming vectors, the common-stream rate allocation vector, and the transmission and reflection coefficients (TARCs) matrix. Due to the dynamic changes of communication links and the coupling of multiple variables, it is challenging to solve such a nonconvex optimization problem by utilizing traditional methods. Therefore, a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm is proposed, where the reward function, the action space and the state space are designed properly. A constraint-satisfaction-processing (CSP) method is employed to further adjust the optimized transmit power to make sure that the obtained optimized results satisfy the power budget constraint. Simulation results show that the proposed PPO-based DRL algorithm converges well and achieves much better performance than several baselines, such as the soft actor–critic (SAC), the deep deterministic policy gradient (DDPG), the genetic algorithm (GA), the maximum ratio transmission (MRT), the zero-forcing (ZF), and the random methods. Moreover, it demonstrates that deploying STAR-RIS greatly enhances the system sum rate and user fairness compared to deploying traditional reflecting-only RIS (RO-RIS) and without RIS. Besides, it also shows that adopting the RSMA scheme achieves more notable performance gains than the nonorthogonal multiple access (NOMA) scheme in such a network.

With the widespread deployment of various emerging intelligent applications, information timeliness is crucial for intelligent decision-making in vehicular networks, where vehicular edge computing (VEC) has become an important paradigm to enhance computing capabilities by offloading tasks to edge nodes. To promote the information timeliness in VEC, an optimization problem is formulated to minimize the age of information (AoI) by jointly optimizing task offloading and subcarrier allocation. Due to the time-varying channel and the coupling of the continuous and discrete optimization variables, the problem exhibits non-convexity, which is difficult to solve using traditional mathematical optimization methods. To efficiently tackle this challenge, we employ a hybrid proximal policy optimization (HPPO)-based deep reinforcement learning (DRL) method by designing the mixed action space involving both continuous and discrete variables. Moreover, an action masking mechanism is designed to filter out invalid actions in the action space caused by limitations in the effective communication distance between vehicles. As a result, a mask-assisted HPPO (MHPPO) method is proposed by integrating the action masking mechanism into the HPPO. Simulation results show that the proposed MHPPO method achieves an approximately 28.9% reduction in AoI compared with the HPPO method and about a 23% reduction compared with the mask-assisted deep deterministic policy gradient (MDDPG).

As a promising candidate for next-generation reconfigurable antennas (NGRAs), the reconfigurable holographic surface (RHS) offers significant advantages in reducing hardware costs and power consumption. To enable green millimeter-wave (mmWave) Internet of Vehicles (IoV) operations in dynamic environments, this paper proposes an RHS-based robust hybrid cooperative beamforming (HCBF) transmission scheme, integrated with a machine learning (ML)-based optimization framework, to minimize the total transmit power at the base station (BS) while taking into account imperfect channel state information (CSI). First, by leveraging the unique advantages of RHS, the proposed HCBF fully exploits multi-antenna collaborative gain, thereby effectively reducing overall power consumption of the IoV system. To overcome the limitations of traditional ML-based optimization methods, which require retraining the neural network (NN) from scratch whenever the IoV network topology varies due to the dynamic entry or exit of mobile vehicles (MVs), a lifelong graph learning-based HCBF optimization method (LGL-HCBF) is developed. Specifically, in LGL-HCBF, an incremental graph convolutional network (IGCN) is employed to facilitate parameter sharing and local structural information exchange across different graph structures, thus providing stronger adaptability to dynamic variations in IoV network topology. To further address the potential stability-plasticity dilemma of LGL-HCBF, two complementary mechanisms, namely, knowledge replay and weight consolidation, are introduced to preserve the knowledge learned from previous scenarios. Simulation results demonstrate that the proposed RHS-based HCBF achieves much lower power consumption compared with traditional phased-array beamforming schemes under the same hardware-cost budget, deployment scales, and rate constraints. Moreover, the proposed LGL-HCBF exhibits faster adaptation, and superior approximation ratio, outperforming several baseline methods.