FairShare: Auditable Geographic Fairness for Multi-Operator LEO Spectrum Sharing

TL;DR

Geographic fairness in multi-operator LEO spectrum sharing is underserved by current DSS policies, which tend to favor urban users due to elevation- and beam-gain geometry. The authors propose FairShare, a quota-based, partition-then-optimize allocation that enforces geographic quotas $W_\ell = q_\ell W$ and then assigns resources within each region by descending $\gamma_u$, aiming to maximize sum-rate while guaranteeing minimum regional access. Using 3GPP TR 38.811 channel models in a large-scale NTN simulator, they show that conventional SNR-priority policies produce urban-rural disparities with $\Delta_{geo}$ as high as $1.65$, while FairShare achieves an affirmative $\Delta_{geo} = 0.72$ and reduces runtime by $3.3\%$, demonstrating that fairness can be attained without sacrificing efficiency. The work offers regulators auditable metrics ($\rho_\ell$, $\Delta_{geo}$) and a practical, enforceable mechanism for equitable spectrum governance in next-generation satellite networks, with potential extensions to dynamic quotas and uplink scenarios.

Abstract

Dynamic spectrum sharing (DSS) among multi-operator low Earth orbit (LEO) mega-constellations is essential for coexistence, yet prevailing policies focus almost exclusively on interference mitigation, leaving geographic equity largely unaddressed. This work investigates whether conventional DSS approaches inadvertently exacerbate the rural digital divide. Through large-scale, 3GPP-compliant non-terrestrial network (NTN) simulations with geographically distributed users, we systematically evaluate standard allocation policies. The results uncover a stark and persistent structural bias: SNR-priority scheduling induces a 1.65x urban-rural access disparity, privileging users with favorable satellite geometry. Counter-intuitively, increasing system bandwidth amplifies rather than alleviates this gap, with disparity rising from 1.0x to 1.65x as resources expand. To remedy this, we propose FairShare, a lightweight, quota-based framework that enforces geographic fairness. FairShare not only reverses the bias, achieving an affirmative disparity ratio of Delta_geo = 0.72x, but also reduces scheduler runtime by 3.3%. This demonstrates that algorithmic fairness can be achieved without trading off efficiency or complexity. Our work provides regulators with both a diagnostic metric for auditing fairness and a practical, enforceable mechanism for equitable spectrum governance in next-generation satellite networks.

Figures (4)

• Figure 1: Geographic disparity (a) and the FairShare framework (b). The Physical Problem: Rural users face a structural disadvantage through two compounding mechanisms: beam gain roll-off (as beams are centered on population-dense urban areas) and elevation-dependent path loss. The figure illustrates the latter, where rural users experience lower elevation angles ($\theta_r < \theta_u$) and longer slant ranges, contributing to the overall SNR penalty (a). The FairShare Solution: The proposed framework replaces purely competition-based allocation with geographic partitioning, enforcing specific bandwidth quotas (b).
• Figure 2: Geographic distribution of 1,000 simulated users. Urban users (50%) cluster near the metropolitan center, while rural users (30%) occupy the outer ring with systematically lower elevation angles.
• Figure 3: SNR distribution by geographic category. Urban users experience a systematic 5--8 dB advantage due to favorable elevation geometry.
• Figure 4: Efficiency-fairness tradeoff: FairShare achieves near-optimal throughput while ensuring $\Delta_{\text{geo}} < 1$.

Theorems & Definitions (2)

• Proposition 1: Pareto Optimality
• proof