Integrated Massive Communication and Target Localization in 6G Cell-Free Networks

2025-10-16

AI Summary

In 6G cell-free networks, user activity detection, channel estimation, and target localization are strongly coupled, leading to computationally intractable posterior inference. Method: This paper proposes a joint processing framework integrating massive access communication and integrated sensing and communication (ISAC). Leveraging multipath signatures induced by target scattering of user pilot signals, we construct a user–target joint factor graph model and design a hybrid message-passing algorithm based on approximate inference, explicitly capturing the nonlinear dependencies between channels and target positions to enable low-complexity minimum mean-square-error (MMSE) estimation. Contribution/Results: To the best of our knowledge, this is the first work embedding high-accuracy sensing capability into massive-access scenarios under distributed base station architectures. Simulations demonstrate that the proposed method maintains communication performance while significantly improving activity detection accuracy, channel estimation fidelity, and target localization resolution—thereby validating the feasibility and effectiveness of 6G integrated sensing and communication systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Probabilistic Circuits and Graphical Models
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements

Abstract

This paper presents an initial investigation into the combination of integrated sensing and communication (ISAC) and massive communication, both of which are largely regarded as key scenarios in sixth-generation (6G) wireless networks. Specifically, we consider a cell-free network comprising a large number of users, multiple targets, and distributed base stations (BSs). In each time slot, a random subset of users becomes active, transmitting pilot signals that can be scattered by the targets before reaching the BSs. Unlike conventional massive random access schemes, where the primary objectives are device activity detection and channel estimation, our framework also enables target localization by leveraging the multipath propagation effects introduced by the targets. However, due to the intricate dependency between user channels and target locations, characterizing the posterior distribution required for minimum mean-square error (MMSE) estimation presents significant computational challenges. To handle this problem, we propose a hybrid message passing-based framework that incorporates multiple approximations to mitigate computational complexity. Numerical results demonstrate that the proposed approach achieves high-accuracy device activity detection, channel estimation, and target localization simultaneously, validating the feasibility of embedding localization functionality into massive communication systems for future 6G networks.

Problem

• Combining integrated sensing and communication with massive connectivity
• Enabling target localization through scattered pilot signals in cell-free networks
• Addressing computational complexity in joint channel estimation and localization

Innovation

• Combines ISAC with massive communication in cell-free networks
• Uses hybrid message passing to reduce computational complexity
• Simultaneously achieves detection, estimation, and localization