Spectrum and RAN Sharing: How to Avoid Cross-Subsidization While Taking Full Advantage of Massive MU-MIMO?

TL;DR

This work tackles fine-grained spectrum sharing (FGSS) in a single-cell neutral-host downlink with multi-user massive MIMO, enforcing isolation and cross-subsidization avoidance while maximizing gains over a static benchmark. The authors formulate FGSS as a mixed-integer problem and derive a tractable online solution built on per-PRB weighted sum-rate optimization with Lagrangian weights, augmented by a Graph Neural Network (GNN) that predicts benchmark performance and a low-complexity Select$(K)$ heuristic for user selection. Offline analysis shows substantial gains (up to 71% for 2 slices and 193% for 4 slices) with FGSS, and online results demonstrate that the combination of GNN predictions and Select$(K)$ approaches offline performance, often within single-digit percentages, across various deployment scenarios. The approach provides a practical, scalable pathway to safe, fair, and efficient RAN sharing in next-generation networks, with significant implications for spectrum policy and operator collaboration.

Abstract

Motivated by the need to use spectrum more efficiently, this paper investigates fine grained spectrum sharing (FGSS) in Multi-User massive MIMO (MU-mMIMO) systems where a neutral host enables users from different operators to share the same resource blocks. To be accepted by operators, FGSS must i) guarantee isolation so that the load of one operator does not impact the performance of another, and ii) avoid cross-subsidization whereby one operator gains more from sharing than another. We first formulate and solve an offline problem to assess the potential performance gains of FGSS with respect to the static spectrum sharing case, where operators have fixed separate sub-bands, and find that the gains can be significant, motivating the development for online solutions for FGSS. Transitioning from an offline to an online study presents unique challenges, including the lack of apriori knowledge regarding the performance of the fixed sharing case that is required to ensure isolation and cross-subsidization avoidance. We overcome these challenges and propose an online algorithm that is fast and significantly outperforms the static case. The main finding is that FGSS for a MU-mMIMO downlink system is doable in a way that is ``safe" to operators and brings large gains in spectrum efficiency (e.g., for 4 operators, a gain above 60\% is seen in many cases).

Figures (6)

• Figure 1: Sample paths of the normalized performance achieved with Algorithm \ref{['algo:lagrangian_solution']} for configurations (a), (b), (c) and (d) with $M=100$ antennas over $N = 2000$ PRBs for the UMa scenario.
• Figure 2: Cumulative distribution function of the normalized performance per slice at the end of the frame $\xi_q(N)$ for $Q=4$ slices, assuming $\alpha_q = 0.25 ~\forall q$, for different values of numbers of users per slice $U_q$ and different deployment scenarios.
• Figure 3: MAPE testing score vs. slice resource share ($\alpha$) for different deployment scenarios when $U=20$ and $M=100$.
• Figure 4: MAPE testing score vs. number of users ($U$) for different deployment scenarios when $\alpha=0.20$ and $M=100$.
• Figure 5: Average normalized performance $\xi_q(N)$ of online ${\sf Select}(K)$ and offline GDAW-s-FD scheme of hussein2023Operating vs. the number of selected users $K$ for the case of $Q=3$ identical slices with $\alpha_q = 1/3$, $U_q = 30$ and $M=100$ antennas for an urban macro cell deployment.