This paper presents a practical online active learning solution for adaptive channel estimation in fluid antenna systems. We model channel state information (CSI) as a spatiotemporal Gaussian process (GP) and approximate it using a deep dropout neural network (DDNN), transforming the challenge into a deep learning task. By leveraging the expressive power of neural networks, our GP model effectively captures the spatiotemporal characteristics of CSI, thereby reducing the number of ports required for accurate estimation. The DDNN, characterized by its low complexity and ease of training, employs a least-squares loss function based solely on port information and pilot measurements, eliminating the need for true CSI. This facilitates dynamic updates and allows for effective adaptation to environmental changes. Additionally, our solution incorporates a port selection method that maximizes mutual information, focusing on prediction uncertainty to optimize port usage. Simulation results demonstrate that our proposed online active learning method significantly outperforms state-of-the-art solutions in CSI estimation accuracy and exhibits robust adaptability in dynamic environments.

In this paper, we propose a Bayesian Reinforcement Learning (BRL)-based CSI feedback, beamforming, and IRS control scheme for IRS-assisted massive MIMO-OFDM systems. Firstly, the proposed approach utilizes the equivalent CSI for optimization, aligning with current channel estimation protocols without necessitating extensive modifications. Secondly, it employs a practical IRS control model that optimizes the effective capacitance of IRS control circuits rather than IRS reflection coefficients, accurately reflecting the IRS's frequencyresponsive behavior to enhance system performance. Additionally, we advocate bypassing the reconstruction of the CSI at the BS to eliminate information irrelevant to beamforming and IRS control, thereby boosting feedback efficiency. Simulation results demonstrate that the proposed IRS-CSI-BRL scheme significantly outperforms start-of-the-art solutions in feedback overhead reduction and system data rate enhancement.

In Intelligent Reflecting Surface (IRS)-assisted massive Multiple-Input Multiple-Output (MIMO) systems, the downlink channel state information (CSI) needs to be fed back to the base station (BS) and utilized to perform the beamforming and IRS control for high spectral efficiency performance. However, the intricate nature of these systems, characterized by a vast number of antennas, subcarriers, and IRS elements, exacerbates the CSI feedback overhead and complicates the optimization of beamforming and IRS parameters, potentially compromising spectral efficiency. Addressing these challenges, this paper introduces a Bayesian Reinforcement Learning (BRL)-based approach, named IRS-CSI-BRL, for efficient CSI feedback, beamforming, and IRS control. Firstly, the IRS-CSI-BRL approach utilizes the equivalent CSI for optimization, aligning with current channel estimation protocols without necessitating extensive modifications. Secondly, it employs a practical IRS control model that optimizes the effective capacitance of IRS control circuits rather than IRS reflection coefficients, accurately reflecting the IRS’s frequency-responsive behavior to enhance system performance. Additionally, we advocate bypassing the reconstruction of the CSI at the BS to eliminate information irrelevant to beamforming and IRS control, thereby boosting feedback efficiency. Another distinctive feature of the proposed scheme is that its output format is probability distributions, which enables the incorporation of model-assisted knowledge about the latent space and boosts the algorithm’s robustness. Simulation results demonstrate that the proposed IRS-CSI-BRL scheme significantly outperforms start-of-the-art solutions in feedback overhead reduction and system data rate enhancement while maintaining exceptional robustness. Furthermore, this approach maintains flexibility, allowing for the incorporation of an additional training loss function for full CSI reconstruction if needed.

In this paper, we propose a Variational Bayesian Autoencoder (VBA)-based channel state information (CSI) compression and feedback scheme for massive multiple-input multiple-output (MIMO) systems. The proposed scheme incorporates the model-assisted knowledge of low-dimensional feedback features and the sparsity of channel to achieve enhanced compression efficiency. We also design a CsiVBA architecture that outputs distributions of the feedback features and the channel at the encoder and decoder, respectively, which facilitates a Bayesian training formulation exploiting the underlying channel sparsity. In addition, we also propose a low-complexity training scheme for new networks of different bit rates, significantly reducing the retraining cost for new compression requirements. Simulation results show that the proposed scheme achieves better rate-distortion trade-offs than the state-of-the-art solutions.

In satellite networks, information can be transmitted either directly by inter-satellite links or the movement of satellites carrying. Consequently, how to quantify network capacity, considering both the carrying and transmission capability of satellites is crucial to the deployment of satellite networks. In this paper, we define the capacity of satellite networks consisting of both, and propose a strategy to exploit the mobile carrying of satellites under the constraint of service requirements. Then, we reveal the theoretical relationship between satellite carrying and the network capacity. The theoretical analysis and simulated results show that 1) satellite carrying can improve the network capacity when the service delay constraints could be released; 2) The capacity gain from satellite carrying is influenced by network parameters, such as orbital altitude, number of satellites, and storage capacity.

Recently, satellite networks have played an increasingly important role in both military and civilian fields. With the continual growth of the network size, the assessment of link criticality is of great significance to protect or attack satellite networks. With regard to the dynamic topologies and store-carry-forward transmission paradigm in satellite networks, detecting critical links should fully consider the relationship of consecutive snapshots and the key performance of the traffic, which raises great challenges. In this paper, we explore critical link sequence of satellite networks from the perspective of delay. We first formulate the problem based on the time-expanded graph model and discuss its convexity. Then, by exploring the space-time relationship between the criticality of different link at different slots, a heuristic critical link sequence detection algorithm (CLSD) is proposed. The simulation proves that deleting the critical link sequence given by the algorithm can effectively prolong the minimum transmission delay of the network and verifies the importance of network vulnerability assessment from the perspective of delay.