A Stochastic Geometry-Based Analysis of SWIPT-Assisted Underlaid Device-to-Device Energy Harvesting

Motivated by providing a model that fits real-world scenarios, we develop a tractable and realistic analytical framework by carrying out an analysis on quantifying the performance of SWIPT-assisted underlaid D2D networks. The analysis is conducted under two key constraints - imperfect channel state information (CSI) and tiered power allocation for cellular users - which are of critical importance in real-world communications and are frequently understudied in the literature. Specifically, we first derive the expected received energy at a D2D receiver (DRU) from its associated transmitter (DTU), assuming only statistical CSI availability at the receiver. Our stochastic geometry-based analysis reveals the impact of CSI uncertainty and, to our knowledge, constitutes the first such treatment for SWIPT-enabled D2D energy harvesting. Then, we extend the analysis to a stratified power model, where cellular users transmit at tier-specific power levels to capture device heterogeneity. Closed-form expressions for ergodic harvested energy are derived, signifying the impact of the tiered structure under imperfect CSI. Finally, we validate the analytical results through Monte Carlo simulations and discuss their practical implications for system design, including tier planning and user association strategies. The joint consideration of imperfect CSI with stratified power allocation enhances the tractability, usability, and accuracy of the model with respect to real-world SWIPT-enabled D2D deployments.