Semantic Multiplexing

2025-11-15

AI Summary

Current wireless edge systems are constrained by bit-level parallelism, limiting their capacity to support concurrent multi-task execution. This work proposes task-level semantic multiplexing—a novel paradigm that achieves semantic-layer representation fusion and joint decoding across heterogeneous tasks, thereby circumventing the fundamental limitation imposed by the number of physical channels. Crucially, it enhances concurrent task capacity without requiring additional antennas or bandwidth. We implement and evaluate the approach on a hardware testbed comprising a Jetson Orin Nano and a millimeter-wave software-defined radio (SDR), demonstrating scalability from 2 to 8 concurrent tasks over a 4×4 MIMO channel while incurring <4% accuracy degradation in image classification. Relative to baseline bit-level schemes, our method reduces end-to-end latency, energy consumption, and communication load by up to 8×, 25×, and 54×, respectively. This work introduces a new degree of freedom—task-level semantic orthogonality—into semantic communications, establishing a foundational framework for high-density edge intelligence.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Mobile devices increasingly require the parallel execution of several computing tasks offloaded at the wireless edge. Existing communication systems only support parallel transmissions at the bit level, which fundamentally limits the number of tasks that can be concurrently processed. To address this bottleneck, this paper introduces the new concept of Semantic Multiplexing. Our approach shifts stream multiplexing from bits to tasks by merging multiple task-related compressed representations into a single semantic representation. As such, Semantic Multiplexing can multiplex more tasks than the number of physical channels without adding antennas or widening bandwidth by extending the effective degrees of freedom at the semantic layer, without contradicting Shannon capacity rules. We have prototyped Semantic Multiplexing on an experimental testbed with Jetson Orin Nano and millimeter-wave software-defined radios and tested its performance on image classification and sentiment analysis while comparing to several existing baselines in semantic communications. Our experiments demonstrate that Semantic Multiplexing allows jointly processing multiple tasks at the semantic level while maintaining sufficient task accuracy. For example, image classification accuracy drops by less than 4% when increasing from 2 to 8 the number of tasks multiplexed over a 4$ imes$4 channel. Semantic Multiplexing reduces latency, energy consumption, and communication load respectively by up to 8$ imes$, 25$ imes$, and 54$ imes$ compared to the baselines while keeping comparable performance. We pledge to publicly share the complete software codebase and the collected datasets for reproducibility.

Problem

• Enabling parallel execution of multiple offloaded computing tasks
• Overcoming bit-level transmission limits in wireless communication systems
• Merging task representations to extend semantic layer degrees of freedom

Innovation

• Merges multiple task representations into semantic representation
• Extends effective degrees of freedom at semantic layer
• Enables joint processing of multiple tasks wirelessly