The recent developments in the field of wireless communication and satellite technology highlight a significant push towards optimizing bandwidth usage, enhancing channel modeling, and improving the efficiency of data transmission and processing in constrained environments. Innovations are particularly focused on integrated sensing and communication (ISAC) systems, where novel methods are being developed to achieve high performance with limited bandwidth. Additionally, there's a growing emphasis on the upper midband (FR3) for next-generation wireless networks, with comprehensive channel measurement campaigns providing critical insights for future system designs. In the realm of satellite communications, advancements are being made in handling large differential delays and Doppler shifts for IoT devices, alongside the development of semi-supervised split learning frameworks to overcome the challenges posed by intermittent connectivity and data heterogeneity. Furthermore, the introduction of multi-task supervised compression models for split computing represents a leap forward in reducing latency and energy consumption for resource-constrained devices.

Noteworthy Papers

• Achieving Full-Bandwidth Sensing Performance with Partial Bandwidth Allocation for ISAC: Introduces a novel two-stage delay estimation method that achieves full-bandwidth performance using only a fraction of the bandwidth, significantly advancing ISAC systems.
• Ultra-Wideband Double-Directional Channel Measurements and Statistical Modeling in Urban Microcellular Environments for the Upper-Midband/FR3: Presents the first UWB double-directional measurement campaign in the FR3 range, offering essential insights for future wireless network designs.
• LEO Satellite-Enabled Random Access with Large Differential Delay and Doppler Shift: Proposes a comprehensive solution for LEO satellite-enabled grant-free random access systems, enhancing device identification, channel estimation, and symbol detection.
• LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks: Develops a semi-supervised split learning framework tailored for satellite networks, addressing data scarcity and heterogeneity challenges.
• A Multi-task Supervised Compression Model for Split Computing: Introduces Ladon, a multi-task supervised compression model that significantly reduces latency and energy consumption in split computing scenarios.