The architectural design of the air interface plays a critical role in wireless communications, embedding crucial functionality to guarantee both efficiency and robustness. Physical layer algorithms often face performance challenges in real-world scenarios owing to the basic assumptions of Gaussian noise, channel model linearity, and functional separation. This paper explores intelligent air interface (IAI) algorithms for multiple-input multiple-output (MIMO) systems to overcome the limitations of these assumptions. The physical layer link is restructured as a composite of various functions and framed as a mathematical optimization problem aimed at maximizing transmission rates, solved through optimization sub-problems for each function using specialized neural networks. Additionally, this paper presents the intelligent modulation and demodulation network (IMD-Net) with an adaptive adjustment sub-network, joint channel feedback and prediction network (CFP-Net), and GEM-Net for joint channel estimation and signal detection, using an unfolded generalized expectation maximization algorithm. Simulation results indicate that the proposed algorithms surpass traditional linear methods and the independent deep learning (DL) based methods in various scenarios and configurations.

On the road to 6G, the growing number of antennas and mobility-related applications emphasize the urgency of addressing the channel aging issue. Traditional channel prediction methods are no longer valid due to simplified assumptions. Deep learning (DL)-based predictors, even with attractive performance improvements, generally lack the utilization of extra correlations and flexible inference with accuracy-efficiency trade-off. In this paper, by representing the topology structure of nearby users (UEs) as a graph, a multi-user channel prediction algorithm is proposed for learnable UE correlation extraction, called LUCE, to improve prediction performance. Instead of simple summation-based fusion, LUCE introduces a predefined geographic embedding and a learnable embedding to offset the position information of the cross-attention (CA) to learn and interact UE features on the graph automatically. Furthermore, a lightweight LUCE is proposed to leverage the homogeneity of CSIs by sharing sub-networks. In addition, a variable-depth learning architecture is proposed, called adaptive LUCE (AdaLUCE), and its number of blocks can be dynamically adjusted for each input by tuning a desired threshold. In particular, AdaLUCE utilizes a hierarchical triple residual architecture to promote its training efficacy and produce predictions at its internal blocks without increasing complexity, and adopts a fitting inspired criterion (FIC) to make real-time decisions for adaptive inference. In total, AdaLUCE is optimized by the weighted sum of multiple loss functions, and a multi-step training scheme is presented. We show that AdaLUCE preserves and releases computing resources for various speeds in sample-wise, improving both prediction accuracy and inference efficiency.

In the era of upcoming sixth-generation (6G) wireless systems, the intelligent integrated sensing and communication (ISAC) paradigm has emerged as a pivotal research domain, catalyzing advancement across a wide range of applications. In this paper, we investigate an ISAC-assisted anti-eavesdropping communication system, where an ISAC ground base station exploits its radar function to track potential aerial eavesdroppers and implements predictive beamforming to ensure secure communications with multiple ground users. We harness the powerful capability of the Transformer for time series prediction to establish a novel deep neural network, termed the ISACformer, for constructing predictive beamformers via exploiting previously estimated channel state information in an unsupervised manner. By eliminating the need for explicit channel prediction, our proposed framework effectively reduces signaling overhead and complexity. In addition, by formulating a weighted objective function, our design meticulously balances the trade-off between the ergodic achievable worst-case secrecy rate for ground users and the ergodic Cramér-Rao lower bound for the kinematic parameters of potential aerial eavesdroppers. Simulation results demonstrate that the proposed ISACformer can deliver the desired predictive beamforming for harmonizing radar and communication functionalities effectively. Moreover, our method achieves performance approaching the theoretical upper bound obtained by ignoring multi-user interference, thereby highlighting the robustness of the proposed approach.

Channel state information (CSI) plays a critical role in achieving the potential benefits of massive multiple input multiple output (MIMO) systems. In frequency division duplex (FDD) massive MIMO systems, the base station (BS) relies on sustained and accurate CSI feedback from users. However, due to the large number of antennas and users being served in massive MIMO systems, feedback overhead can become a bottleneck. In this paper, we propose a model-driven deep learning method for CSI feedback, called learnable optimization and regularization algorithm (LORA). Instead of using$l_{1}$-norm as the regularization term, LORA introduces a learnable regularization module that adapts to characteristics of CSI automatically. The conventional Iterative Shrinkage-Thresholding Algorithm (ISTA) is unfolded into a neural network, which can learn both the optimization process and the regularization term by end-to-end training. We show that LORA improves the CSI feedback accuracy and speed. Besides, a novel learnable quantization method and the corresponding training scheme are proposed, and it is shown that LORA can operate successfully at different bit rates, providing flexibility in terms of the CSI feedback overhead. Various realistic scenarios are considered to demonstrate the effectiveness and robustness of LORA through numerical simulations.

This paper proposes a unified mathematical frame-work for intelligent transceiver design. It mainly includes three most important modules in the communication system, namely, beamforming, channel estimation and Multiple-Input Multiple-Output (MIMO) detection. Firstly, the mathematical correlation behind different algorithms of a single communication module is analyzed, the purpose is to realize the unification between different algorithms of a specific communication module. Next, a cross-module unified mathematical framework is proposed. Finally, an AI architecture for the unified mathematical framework is designed, which shows that the intelligent transceiver based on the mathematical framework has higher performance.

In massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, a challenging problem is how to predict channel state information (CSI) (i.e., channel prediction) accurately in mobility scenarios. However, a practical obstacle is caused by CSI non-stationary and nonlinear dynamics in temporal domain. In this paper, we propose a spatio-temporal neural network (STNN) to achieve better performance by carefully taking into account the spatio-temporal characteristics of CSI. Specifically, STNN uses its encoder and decoder modules to capture the spatial correlation and temporal dependence of CSI. Further, the differencing-attention module is designed to deal with the non-stationary and nonlinear temporal dynamics and realize adaptive feature refinement for more accurate multi-step prediction. Additionally, an advanced training scheme is adopted to reduce the discrepancy between STNN training and testing. Evaluated on a realistic channel model with enhanced mobility and spherical waves, experimental results show that STNN can effectively improve the accuracy of prediction and perform well with respect to different signal to noise ratios (SNRs). Visualization and testing for unit root illustrate STNN is able to learn CSI time-varying patterns by alleviating series non-stationarity.