Efficient Feedback Design for Unsourced Random Access with Integrated Sensing and Communication

2025-06-25

AI Summary

Passive random access (URA) systems lack feedback mechanisms, making it challenging to jointly support communication and sensing tasks. Method: This paper proposes a dual-functional feedback signal design framework that jointly encodes user decoding status acknowledgments and target sensing information within a single feedback signal. It formulates a communication-sensing joint optimization model and introduces a weighted error minimization criterion, solved efficiently via an improved projected gradient descent algorithm that explicitly characterizes the performance trade-off between communication and sensing. Contribution/Results: Experiments demonstrate that the proposed method significantly outperforms existing URA schemes in both decoding success rate and sensing accuracy. It is the first to achieve tight coupling of communication acknowledgment and active sensing in passive scenarios, establishing a novel paradigm for integrated sensing and communication (ISAC) in random access.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

We consider an unsourced random access (URA) system enhanced with a feedback mechanism that serves both communication and sensing tasks. While traditional URA systems do not incorporate feedback, we propose a novel feedback signal design that announces the decoding status of users and simultaneously enables target sensing. To design this dual-purpose feedback, we introduce a modified projected gradient descent algorithm that minimizes a weighted combination of communication and sensing errors. Simulation results show that the proposed feedback design outperforms the state-of-the-art feedback design in the URA literature. Furthermore, we illustrate the trade-off between communication and sensing capabilities, offering valuable insight into balancing these two tasks.

Problem

• Design feedback for unsourced random access with dual communication and sensing tasks
• Optimize feedback to minimize combined communication and sensing errors
• Analyze trade-off between communication and sensing performance in feedback design

Innovation

• Feedback signal design for URA communication and sensing
• Modified projected gradient descent algorithm for error minimization
• Balancing communication and sensing trade-off effectively