부산대학교 도서관

Beyond 5G mobile networks have to support a wide range of performance requirements and unprecedented levels of flexibility. To this end, massive MIMO is a critical technology to improve spectral efficiency and thus scale up network capacity, by increasing the number of antenna elements. This also increases the overhead of Channel State Information (CSI) estimation and obtaining accurate CSI is a fundamental problem in massive MIMO systems. In this paper, we focus on scheduling uplink Sounding Reference Signals (SRSs) that carry pilot symbols for CSI estimation. Under the large number of users and high load that are expected to characterize beyond 5G systems, the limited amount of resources available for SRSs makes the legacy 3GPP periodic allocation scheme largely inefficient. We design TRADER, an SRS resource allocation framework that minimizes the age of channel estimates by taking advantage of machine learning-based short-term traffic forecasts at the base station level. By anticipating traffic bursts, TRADER schedules SRS resources so as to obtain CSI for each user right before the corresponding traffic arrives. Experiments with extensive real-world mobile network traces show that our solution is efficient and robust in high load scenarios: with respect to a round robin schedule of aperiodic SRS, TRADER provides more often CSI within the coherence time (up to 5× for given scenarios), leading to channel gains of up to 2 dB.