The growth of frequency bandwidths in the new generation of wireless networks gives rise to the multitude of wireless communication scenarios and highlights the challenges of generalized capability of the wireless communication system in different scenarios, especially the channel estimation module. In this paper, we propose a large model dubbed Conditional Latent Diffusion Channel Generation Model (C-LCGM) to learn the distributions of channel state information (CSI) in different wireless communication scenarios to form Bayesian Modeling based Channel Estimation Scheme (BMCE) for universal channel estimation. BMCE conducts universal channel estimation by generating reference CSIs from C-LCGM and mapping the reference CSIs to the optimal channel estimation neural network for each scenario. Specifically in C-LCGM, we propose to compress the CSIs into latent codes and design a conditional diffusion model to model the distribution of the latent codes given the large-scale parameters (LSP) of CSIs as the condition. Further in BMCE, we propose to deploy C-LCGM on the server center and design a hyper-network dubbed Parameters Generating Module (PGM) to map the generated CSIs of C-LCGM to the channel estimation networks for the base stations (BS) according to the reported LSPs. The design rationale and training loss of C-LCGM and BMCE are derived theoretically in this paper. We also conduct extensive simulations to verify the performance of C-LCGM and BMCE. The simulation results show that BMCE can achieve optimal channel estimation performance in different and novel scenarios with C-LCGM generating high-quality CSIs approximating the real CSIs in each scenario. Complexity analysis shows BMCE can fit the delay requirement of wireless communication systems.

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.

The growth of frequency bandwidths and applications with the forthcoming generations of wireless networks will give rise to a multitude of wireless transmission scenarios, topologies and channel structures. In this work, we go beyond existing learning-based channel estimation methods tailored for specific scenarios, to develop an adaptive learning-based channel state information (CSI) estimation approach. We offer the adaptivity in the learning approach through extracting the scenario embeddings of CSI and adjusting the channel estimation method with the extracted information automatically in each scenario. Specifically, Learning-Based Scenario-Adaptive Channel Estimation Algorithm (LACE) is designed. LACE is based on a Scenario-Aware Hyper-Network (SAH-Net) that incorporates the embedding loss to make the Convolutional Neural Network (CNN) based encoder learn to extract the effective scenario embeddings from the time-space two dimensional features of the CSI. The extracted embeddings are utilized by a Multi-Layer Perceptron (MLP) based tuning module to tune the parameters of the channel estimation method. Our learning design is complemented with analysis to verify that the theoretical performance of LACE is strictly superior to that of the mix-training method, which involves conventionally training the deep network-based channel estimation method using samples from all scenarios. Our results show that the performance of LACE trained in finite scenarios is comparable to that of the deep network-based channel estimation method trained in each scenario, while having lower complexity. Further more, the performance of LACE trained in infinite scenarios is demonstrated to be superior to that of the mix-training method in all test scenarios.

This paper works on the detection of physical random access channel (NPRACH) in Narrowband Internet of Things (NB-IoT) system. The frequency hopping preamble design and increasing number of IoT terminals lead to inter-cell interference among different cells, resulting in inevitable increase of false alarm rate. Due to the ambiguity between preamble and interference, it is a great challenge for NPRACH detection methods to achieve low false alarm rate when having strong interference. In this paper, we analyze the difference between preamble and interference in the propagation environments of NPRACH signals in the 2-dimensional Fast Fourier Transformation (2-D FFT) domain. Then we propose a deep learning-based NPRACH detection method, dubbed Mask Assisted Anti-Interference Universal Detection Scheme (MIUS), in the 2-D FFT domain for preamble detection with inter-cell interference in different repetition cases. In the proposed MIUS, the Mask-ResNet Block is designed as a building block to extract features distinguishing the preamble and interference based on masking operations. Our proposed MIUS utilizes the Mask-ResNet Block in a separate manner to detect the preambles in sequential repetitions across different repetition cases. Simulation results show that MIUS can simultaneously maintain the low false alarm rate and achieve high detection accuracy in low Signal to Interference and Noise Ratio (SINR) regime in all repetition cases.

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.