Multi-Provider Resource Scheduling in Massive MIMO Radio Access Networks

TL;DR

The paper tackles resource scheduling in massive MIMO RAN slicing under diverse SLA requirements. It introduces a channel-aware and SLA-aware framework that extends beyond time-frequency resources by leveraging beamforming to share or privately allocate RBs across slices. The authors propose RU-orthogonal and RB-sharing scheduling schemes, including GP, DRO, and DRS, plus a graph-based User Grouping and a parallelism strategy to meet sub-millisecond latency. Through trace-driven experiments on real-world channel data, the solutions achieve substantial RB savings (up to $60.9\%$) and scalable, fast scheduling while satisfying SLAs, demonstrating practical impact for 5G+ deployments.

Abstract

An important aspect of 5G networks is the development of Radio Access Network (RAN) slicing, a concept wherein the virtualized infrastructure of wireless networks is subdivided into slices (or enterprises), tailored to fulfill specific use-cases. A key focus in this context is the efficient radio resource allocation to meet various enterprises' service-level agreements (SLAs). In this work, we introduce a channel-aware and SLA-aware RAN slicing framework for massive multiple input multiple output (MIMO) networks where resource allocation extends to incorporate the spatial dimension available through beamforming. Essentially, the same time-frequency resource block (RB) can be shared across multiple users through multiple antennas. Notably, certain enterprises, particularly those operating critical infrastructure, necessitate dedicated RB allocation, denoted as private networks, to ensure security. Conversely, some enterprises would allow resource sharing with others in the public network to maintain network performance while minimizing capital expenditure. Building upon this understanding, the proposed scheduler comprises scheduling schemes under both scenarios: where different slices share the same set of RBs, and where they require exclusivity of allocated RBs. We validate the efficacy of our proposed schedulers through simulation by utilizing a channel data set collected from a real-world massive MIMO testbed. Our assessments demonstrate that resource sharing across slices using our approach can lead up to 60.9% reduction in RB usage compared to other approaches. Moreover, our proposed schedulers exhibit significantly enhanced operational efficiency, with significantly faster running time compared to exhaustive greedy approaches while meeting the stringent 5G sub-millisecond-level latency requirement.

Figures (8)

• Figure 1: RAN Slicing based scheduling architecture in massive MIMO networks.
• Figure 2: Overview of implemented scheduling algorithms.
• Figure 3: Illustrative Example Comparing Greedy, Greedy Plus, and Gurobi: Consider 3 slices and 5 RBs. The achieved data rates by intra-slice schedulers are shown in circles. Greedy - $RB5 \rightarrow S1$, $RB1\rightarrow S3$, $RB4\rightarrow S1$, after which $S1$ has exceeded its SLA. Then, $RB3\rightarrow S3$, $RB2\rightarrow S2$. GP - Sort RBs in descending order within each slice, then serve the slice with largest SLA, thus $RB5 \rightarrow S1$, remove RB5 from lists of other slices and update SLA deficit of S1. Next S3 has largest deficit, thus $RB1 \rightarrow S3$. Further, $RB4\rightarrow S1$, completing S1's SLA requirement. Further, $RB5 \rightarrow S1$, $RB3\rightarrow S2$, and $RB2 \rightarrow S3$. Gurobi-employs Branch and Bound method.
• Figure 4: (a) Topology of real-world dataset collection datasetweb and (b) Average inter-user channel correlation among clusters reproduced from dataset.
• Figure 5: Scheduler performance in small-size network (a) static high-correlated and loose SLA (b) static high-correlated and tight SLA (c)static low-correlated and loose SLA (d) static low-correlated and tight SLA.