The AI-enabled autoencoder has demonstrated great potential in channel state information (CSI) feedback in frequency division duplex (FDD) multiple input multiple output (MIMO) systems. However, this method completely changes the existing feedback strategies, making it impractical to deploy in recent years. To address this issue, this paper proposes a channel modeling aided data augmentation method based on a limited number of field channel data. Specifically, the user equipment (UE) extracts the primary stochastic parameters of the field channel data and transmits them to the base station (BS). The BS then updates the typical TR 38.901 model parameters with the extracted parameters. In this way, the updated channel model is used to generate the dataset. This strategy comprehensively considers the dataset collection, model generalization, model monitoring, and so on. Simulations verify that our proposed strategy can significantly improve performance compared to the benchmarks.

Integrated sensing and communication (ISAC) technology is essential for enabling the vehicular networks. However, the communication channel in this scenario exhibits time-varying characteristics, and the potential targets may move rapidly, creating a doubly-dynamic phenomenon. This nature poses a challenge for real-time precoder design. While optimization-based solutions are widely researched, they are complex and heavily rely on perfect prior information, which is impractical in double dynamics. To address this challenge, we propose using constrained deep reinforcement learning (CDRL) to facilitate dynamic updates to the ISAC precoder design. Additionally, the primal dual-deep deterministic policy gradient (PD-DDPG) and Wolpertinger architecture are tailored to efficiently train the algorithm under complex constraints and variable numbers of users. The proposed scheme not only adapts to the dynamics based on observations but also leverages environmental information to enhance performance and reduce complexity. Its superiority over existing candidates has been validated through experiments.

Meta-learning owns unique effectiveness and swiftness in tackling emerging tasks with limited data. Its broad applicability is revealed by viewing it as a bi-level optimization problem. The resultant algorithmic viewpoint however, faces scalability issues when the inner-level optimization relies on gradient-based iterations. Implicit differentiation has been considered to alleviate this challenge, but it is restricted to an isotropic Gaussian prior, and only favors deterministic meta-learning approaches. This work markedly mitigates the scalability bottleneck by cross-fertilizing the benefits of implicit differentiation to probabilistic Bayesian meta-learning. The novel implicit Bayesian meta-learning (iBaML) method not only broadens the scope of learnable priors, but also quantifies the associated uncertainty. Furthermore, the ultimate complexity is well controlled regardless of the inner-level optimization trajectory. Analytical error bounds are established to demonstrate the precision and efficiency of the generalized implicit gradient over the explicit one. Extensive numerical tests are also carried out to empirically validate the performance of the proposed method.