To achieve an efficient compression and reconstruction of vibrotactile signals in the Tactile Internet, an end-to-end neural vibrotactile codec, PC-NSVC, is proposed. By integrating a residual product quantizer (RPQ) within a deep autoencoder, PC-NSVC effectively reduces coding latency through joint training and inference of the entire framework, while simultaneously enhancing the quality of the reconstructed signals. The RPQ allows for control over transmission bitrates by adjusting quantizer parameters, enabling scalable codec across various network environments and bandwidths. Additionally, PC-NSVC incorporates psychohaptic model to account for the influence of human perception, further improving the perceptual fidelity of the reconstructed signals. A remote vibrotactile sharing prototype, TouchShare, was developed to conduct transmission and material classification tests. Simulation and transmission results demonstrate that the PC-NSVC scheme significantly improves the quality of reconstructed signals at different compression ratios and supports accurate material classification, outperforming existing schemes.

Utilizing device mobility to form virtual large-scale antenna arrays can provide accurate angle-of-arrival (AoA) information for robots. However, existing wireless localization systems that exploit device mobility are designed based on far-field channel assumptions and cannot directly provide range estimates. To address this problem, in this paper, we develop a novel near-field localization architecture for mobile robots by fusing the robot’s motion trajectory and channel state information (CSI) of a single antenna. Specifically, we first utilize channel reciprocity to multiply the uplink CSI and downlink CSI to eliminate the phase offset. Second, we further propose a two-stage localization algorithm that separates the line-of-sight (LoS) path from the multipath, and a multi-scale iterative scheme is employed to refine the estimation of AoA and distance of the LoS path. In addition, the range and AoA profiles for different trajectory shapes and the Cramer-Rao bounds for localization accuracy under squared channels are derived. Finally, the effectiveness of the proposed system is verified in a real environment. The simulation and experimental test results show that the proposed near-field localization system can operate in complex channel environments, and its localization accuracy outperforms the existing schemes.

This paper presents an edge-assisted massive mobile live streaming (MMLS) framework named LOCA, integrating chunk-level analysis and long-term optimization to design resource allocation, bitrate adaptation, and source selection strategies. The proposed method ensures sustained real-time video delivery while minimizing latency and communication costs. Firstly, a chunk-level analysis of the entire process of video streaming is introduced, aiming at modeling fetch queue waiting time and rebuffering duration in each time slot. By embedding this mathamatical model into consideration, a long-term optimization is formulated to minimize rebuffering and communication overhead while maintaining high video qualities for massive users. Leveraging Lyapunov optimization, we transform this problem into a computationally tractable form. Further simplification via linearization achieves near-optimal solutions by adopting the mixed-integer linear programming method with enhanced computational efficiency. Simulation results demonstrate superior stability and long-term performance compared to the state-of-theart and baseline methods, validating the framework's efficacy in MMLS scenarios

Filter bank multicarrier (FBMC) is emerging as a promising approach to combat the orthogonal frequency division multiplexing (OFDM) sensitivity to synchronization errors for machine-type communication (MTC). However, related designs in FBMC-MTC fail to meet the requirements of transmitting diverse data between machines as well as eliminating the effects of FBMC’s inherent imaginary interference. To address this issue, in this paper, we propose an FBMC-MTC system with deep learning (DL) assistance. Specifically, we first construct a hybrid packet transmission architecture to meet the delay and throughput requirements of different packets. Second, we further present a DL-based receiver consists two modules driven by communication domain knowledge to enhance data reliability. The dense and convolutional layers are used in two different modules since the two types of packets have different pilot structures and propagation properties. Finally, we deploy the proposed FBMC-MTC system via universal software radio peripheral (USRP) and mobile robots and test the DL-based receiver over the air (OTA). Both simulation and OTA test results show that the proposed FBMC-MTC system can operate in various channel environments, and its receiver is better than the previously advanced OFDM and FBMC receivers.