Ultra-Reliable and Low-Latency Communication (URLLC) requires stable transmission under strict latency and high-reliability constraints. Consequently, passive retransmission, which merely waits for bit errors to occur, cannot meet these stringent control and security requirements. This paper proposes an active feedback mechanism, namely the Transmission Prediction Feedback Network (TPFNet). It leverages trajectory-aware modeling and post-decoding statistics to perform risk assessment and guide strategy switching, thereby proactively managing subsequent retransmissions. The TPFNet concept is based on utilizing the historical progression of symbols on the constellation diagram as an initial reference, integrating memory-augmented trajectory-aware modeling. This approach ensures that decision-making is not confined to the instantaneous state received at a single point in time. Initially, it uses the in-phase and quadrature path trajectories of the received sequence to jointly assess instantaneous deviations and temporally cumulative morphological changes. Secondly, it employs residual regression for the calibration of symbol positions, complemented by a concentric constraint loss to mitigate shrinkage bias toward the constellation origin. Finally, it provides a confidence metric for the transceiver link, which facilitates adaptive modulation and coding scheme switching. Simulation results demonstrate that the proposed method significantly enhances transmission fidelity under diverse channel conditions. It markedly improves the block error rate and undetected error rate while enhancing the robustness of symbol decisions at the boundaries.

With the advancement of communication technology and the improvement of localization accuracy, cellular networks are gradually evolving from communication to perception-integrated networks. Addressing the research challenges of sensing-assisted communication, we propose, for the first time, the concept of Digital Twin of Channel (DToC). Specifically, we regard user terminal (UT) positions as physical objects, and statistical channel state information (CSI) as virtual digital objects. Observing the change trend of UTs’ statistical CSI caused by the changes of UT’s physical position enables predictive analytics for subsequent communication tasks. Then, we establish the relationship between physical and virtual digital objects using a Diffusion Model (DM) to achieve the DToC. Indeed, the DM can generate the desired objects by gradually denoising from noisy data using neural networks. Furthermore, we propose a conditional DM utilizing UTs’ positions, which completes the task of generating the corresponding statistical CSI under known user-specific position conditions, thus mapping UT positions to statistical CSI. Simulation results demonstrate that our DToC framework outperforms previous statistical CSI estimation methods. Without the need of pilots, our method can simultaneously generate statistical CSIs from a large number of UTs’ positions, achieving satisfactory results.

We investigate the weighted sum-rate (WSR) max-imization linear precoder design under total power constraint (TPC) for massive MIMO downlink with matrix manifold optimization. Particularly, we prove that the precoders under TPC are on a Riemannian submanifold, and transform the constrained problem in Euclidean space to the unconstrained one on manifold. In accordance with this, Riemannian design methods using Riemannian steepest descent and Riemannian conjugate gradient are provided to design the WSR-maximization precoders under TPC. Riemannian methods are free of the inverse of large dimensional matrix, posing significant computational savings and potentially allowing to avoid ill numerical behavior in algorithms. Complexity analysis and performance simulations demonstrate the advantages of the proposed precoder design.

Wireless positioning is crucial for Internet of Things (IoT) landscape, enhancing precision and reliability in location-based services. This article addresses the challenges of existing massive multiple-input–multiple-output fingerprint positioning methods, which typically require accurate channel estimation and one-by-one labeled data sets. We propose a semisupervised representation contrastive learning technique that leverages a partially labeled received pilot signal data set readily available from the base station. Our approach employs data augmentation to generate a large number of positive and negative sample pairs, which are then used to pretrain an encoder with a contrastive loss function in the self-supervision way. During pretraining, the encoder learns to encode positive samples close to an anchor, while keeping negative samples far away in the representation space. A fully connected layer is added on top of the encoder for position regression, and the encoder and regression networks are fine-tuned with a small labeled subdataset for the downstream positioning task. Simulation results demonstrate that our pretraining and fine-tuning approach outperforms the previous methods, significantly improving positioning accuracy, avoiding exact channel estimation and achieving labeling efficiency.

In this paper, we investigate a sparse channel estimation problem for broadband massive multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We propose a hidden Markov model to capture the structured sparsity and temporal dependency characteristic of massive MIMO-OFDM channels in the angle-delay domain, and this probability model exhibits extensive adaptability to different realistic propagation scenarios. Then we solve the channel estimation problem based on a novel optimization framework named constrained Bethe free energy (BFE) minimization, which is valid for a generic statistical model. Under this systematic theoretical framework, a hierarchical hybrid message passing (HHMP) algorithm is proposed to track dynamic channel parameters recursively. The proposed method can adaptively learn the sparse structure and temporal correlation of multiuser channels without requiring the knowledge of hidden Markov channel parameters. Numerical simulations demonstrate that the proposed HHMP algorithm can accurately estimate angle-delay domain channels with reduced iteration times and pilot overhead.