Stochastic-Geometry-Based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA
Takeshi Hirai, Yuta Ueda, Naoki Wakamiya · IEEE Internet of Things Journal · 2024
This article proposes a stochastic geometry-based analytical framework for the throughput of the grant-free power-domain nonorthogonal multiple access (GF-NOMA) with user-specific constraints of selectable power levels and analyzes the achievable throughput. Our analytical framework uses stochastic geometry to reflect selectable power levels constrained by the maximum transmission power and channel of each user to an inhomogeneous offered load per level. This key idea enables our framework to analyze the throughput bounded by the geographical user distribution and derive a suitable selection strategy of power levels under the constraint more accurately than the existing models. Our analytical results showed that our framework analyzed the throughput with only an analysis error of 0.1% compared with the Monte Carlo simulations, although the existing model overestimated 58% higher throughput. By using the proposed analytical model, our results presented decreasing the achievable throughput with increasing the coverage range. This article also proposes a heuristic method based on our proposed analytical model to derive a suitable selection strategy of power levels. Our results highlight that the derived selection strategy on our analytical framework achieved 20% higher throughput than the baseline strategy, where each user randomly selects a power level under the power-level constraint.