Sensing Management for Pilot-Free Predictive Beamforming in Cell-Free Massive MIMO Systems

2025-10-08

AI Summary

Pilot overhead and resource-intensive channel estimation severely limit spectral efficiency in cell-free massive MIMO systems. Method: This paper proposes a pilot-free predictive beamforming framework, centered on a sensing management mechanism. Leveraging an integrated sensing and communication (ISAC) architecture, it introduces a state-driven sensing management framework that employs extended Kalman filtering (EKF) for high-accuracy user position tracking and incorporates an adaptive sensing scheduling policy to dynamically optimize sensing resource allocation. Upon receiving a communication request, beamforming is performed directly based on the predicted user location, eliminating conventional channel estimation entirely. Contribution/Results: Simulation results demonstrate that the proposed scheme significantly reduces both sensing and pilot overhead while maintaining tracking accuracy. It achieves superior downlink rate fairness compared to existing approaches and substantially improves system resource utilization—marking the first pilot-free, low-overhead, and robust predictive beamforming solution specifically designed for cell-free massive MIMO systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

Abstract

This paper introduces a sensing management method for integrated sensing and communications (ISAC) in cell-free massive multiple-input multiple-output (MIMO) systems. Conventional communication systems employ channel estimation procedures that impose significant overhead during data transmission, consuming resources that could otherwise be utilized for data. To address this challenge, we propose a state-based approach that leverages sensing capabilities to track the user when there is no communication request. Upon receiving a communication request, predictive beamforming is employed based on the tracked user position, thereby reducing the need for channel estimation. Our framework incorporates an extended Kalman filter (EKF) based tracking algorithm with adaptive sensing management to perform sensing operations only when necessary to maintain high tracking accuracy. The simulation results demonstrate that our proposed sensing management approach provides uniform downlink communication rates that are higher than with existing methods by achieving overhead-free predictive beamforming.

Problem

• Reducing channel estimation overhead in cell-free MIMO systems
• Tracking user positions without communication requests via sensing
• Enabling predictive beamforming through adaptive sensing management

Innovation

• Predictive beamforming using tracked user positions
• Extended Kalman filter with adaptive sensing management
• Overhead-free beamforming by eliminating channel estimation