In this paper, we discuss general data-framing and optimization, parallel frequency-phase estimation, and powerful frequency estimator for satellite communications. Based on the standard pilot-symbol-assisted-modulation (S-PSAM) frame, we design a general PSAM (G-PSAM) frame, whose corresponding data-aided & non-data-aided Cramér-Rao bound (DA&NDA CRB) is derived with respect to carrier frequency offset, time delay and phase offset, followed by its approximate form. Also, we provide a simple solution to the G-PSAM frame optimization by means of the approximate DA&NDA CRB and the control-variate method. With an unbiased auto-correlation (AC) value, we then propose a frequency-phase decoupler and develop a parallel frequency-phase estimation (PFPE) mode in terms of the serial frequency-phase estimation (SFPE) mode, where a serial-to-parallel estimation conversion can be implemented. Furthermore, we extend the AC frequency estimator in the PFPE mode as a powerful stepwise correlation-based one aiming at the target performance. Theoretical analysis and numerical results are provided to show the superiority of the optimized frames, the robustness of the PFPE mode, and the adaptability of the proposed frequency estimator with the optimized frames under different transmission systems.

This article introduces a distributed edge intelligence (DEI) framework for secure and efficient data sharing in 6G Internet of Vehicles (6G-IoV) environments. The approach integrates a hierarchical blockchain architecture with an innovative asynchronous federated learning (FL) algorithm to address the challenges of privacy preservation, communication efficiency, and scalability in 6G-IoV scenarios. The proposed framework employs a multilayer structure comprising vehicles, roadside units (RSUs), and cloud servers, each playing a distinct role in the distributed learning process. We present a comprehensive latency model that accounts for computation and communication aspects, considering the unique characteristics of vehicular networks. The asynchronous FL algorithm incorporates genetic algorithm-based resource optimization to adaptively allocate communication resources, significantly enhancing efficiency in heterogeneous 6G-IoV environments. Furthermore, we introduce the hierarchical edge intelligence consensus (HEIC) mechanism, a lightweight consensus protocol tailored for edge intelligence scenarios, which accelerates blockchain consensus while maintaining security. Simulations using MNIST and SVHN datasets demonstrate that the proposed framework outperforms state-of-the-art methods regarding model accuracy, communication efficiency, and privacy preservation.

The rapid growth of social media platforms has led to an unprecedented increase in user-generated content and social interactions, posing significant challenges for recommendation systems. This article addresses the challenges of recommendation in large-scale dynamic social environments, where user interactions and preferences evolve rapidly across vast networks. In large-scale dynamic social networks, knowledge discovery requires methods to efficiently process vast amounts of data while capturing the evolving nature of user interactions and preferences. Multigraph neural networks offer a promising approach for this task, as they can model complex relationships and temporal dynamics in these environments. This article proposes a novel social-aware multigraph neural network for the session-based recommendation (SAMGNN-SR) model that leverages dynamic social information and multigraph neural networks to enhance recommendation accuracy and knowledge discovery in complex social computing environments. The model constructs a global social-aware interaction graph from all user session sequences and employs an adaptive subgraph sampling strategy to extract relevant collaborative signals efficiently. A dynamic interest extraction module utilizing dual-direction information propagation captures users’ evolving preferences, while a social information fusion network based on graph attention mechanisms models the dynamic nature of social influences. Experiments on three real-world datasets (Douban, Delicious, and Yelp) demonstrate the superiority of SAMGNN-SR over nine state-of-the-art baselines, with improvements of up to 6.79% in NDCG@20 and 6.19% in Hit@20. Ablation studies validate the effectiveness of each model component in capturing complex social dynamics and session-based user behaviors.

Considering limited pilot and storage resources in satellite communications, we propose an efficient correlation-based reception scheme consisting of a “parallel” frequency-phase estimation (PFPE) mode and a data blockwise compensation (DBC) scheme. In specific, the PFPE mode provides an autocorrelation (AC) frequency offset estimator and a maximum-likelihood (ML) phase offset estimator using a special but heuristic AC value; the DBC scheme uses a small amount of cross-correlation (CC) based compensation values to eliminate the impact of residual offsets on each data block. Simulation results show that with a small pilot overhead the proposed reception scheme can weaken “frequency-to-phase coupling” and achieve good error performance with a small demand for storage.

In space communications, there exist two prominent issues: large Doppler spread and limited pilot resources, which will result in poor performance for a pilot-aided (PA) traditional carrier synchronization mode (TCSM) with serial processing. In view of this, we propose a frequency-phase decoupling method (FPDM) based on an unbiased auto-correlation (AC) operator with a variable correlation delay length. By using this method, a new carrier synchronization mode (NCSM) is developed with parallel processing. Taking the classical maximum likelihood (ML) estimation as an example, both theoretical analysis and simulation results show that considering a modified generalized frame format, I) the resulting NCSM can implement decoupling of the frequency offset estimator (FOE) and the phase offset estimator (POE), and II) the POE of the NCSM achieves stronger ability of anti-Doppler spread and requires less complex multiplication operations compared to that of the TCSM.

In order to support low power wide area networks (LPWANs) with a scarce pilot resource, we discuss the data framing and its corresponding optimization problem. In specific, we design a general pilot-symbol-assisted-modulation (G-PSAM) frame based on the standard PSAM (S-PSAM) frame and derive joint data-aided & non-data-aided Cramér-Rao bounds (DA&NDA CRBs) for carrier frequency offset (CFO), phase offset (PO), and time delay estimation, followed by an approximate DA&NDA CRB for the CFO. With the approximate DA&NDA CRB and the well-known control-variate method (CVM), we then provide an efficient G-PSAM frame optimization scheme, which is asymptotically optimal in terms of the mean-squared-error (MSE) performance. Simulation results show the superiority of the optimized G-PSAM frame over the S-PSAM frame for both the short-packet and long-packet transmissions. Meanwhile, we also present an example verification on the joint DA&NDA aided carrier synchronization (i.e., Turbo synchronization).