Gaussian Process Regression for Feedback Reduction in Wireless Multiuser Networks

摘要

Periodic Channel Quality Indicator (CQI) feedback consumes much of the uplink resources. In scenarios such as urban festivals, sport events, and Olympics football World Cups, which pose additional challenges to the wireless networks due to the heavy traffic load, channel estimation strategies have to be implemented to overcome the problem of signalling overhead while satisfying certain performance bounds. To reduce the CQI feedback overhead, we propose a limited feedback selection scheme. The proposed scheme permits the Base Station (BS) to obtain CQI from a subset of users under substantially reduced feedback overhead and estimate the channel for the remaining users. We cast the problem of CQI estimation by exploiting the theory of Gaussian Process Regression (GPR) which benefits from the correlation property of the macroscopic shadow fading. I.e., users that are close to each other have a correlated channel. The results show that with the proposed approach, a significant reduction in feedback is achieved while keeping the BLock Error Ratio (BLER) below $10 %$ threshold.