EBGT: Epistemology-aided Bayesian Game Theory for Uplink Power Control in Stochastically Distributed IoT Tiers

2026-08-07

AI Summary

This work addresses the challenges of power control in dense heterogeneous IoT uplink scenarios, where conventional methods struggle due to incomplete channel state information, inter-device interference, and stringent size, weight, and power (SWaP) constraints. To overcome these limitations, the authors propose a novel epistemic Bayesian game-theoretic framework that, for the first time, incorporates cognitive hierarchy modeling into power control. The approach employs a two-layer belief structure to capture users’ reasoning about opponents’ strategies and their own strategic self-assessment. Spatial randomness is modeled using Poisson point processes and stochastic geometry, while decentralized power optimization is achieved without feedback by leveraging Jensen–Shannon divergence analysis. Theoretical derivations yield a coverage probability-based utility function, and simulations demonstrate that, under high network density and stringent SINR requirements, the proposed method significantly reduces transmit power compared to fractional power control (FPC) and signal-to-noise-and-channel-power control (SNCPC), while maintaining the target coverage probability.

Abstract

Uplink power control in dense, heterogeneous Internet-of-Things (IoT) tiers is fundamentally limited by incomplete channel-state information (CSI) and mutual interference, while low size, weight, and power (SWaP) devices cannot afford the feedback and computation of conventional distributed schemes. This paper proposes EBGT, an epistemology-aided Bayesian game-theoretic framework for decentralized uplink power minimization in stochastically distributed IoT networks. Interfering users are modeled as spatially random through a Poisson point process (PPP), and each device reasons about its rivals through a two-layer belief hierarchy of inter-epistemic beliefs about opponents and intra-epistemic self-assessment, so that the transmit-power equilibrium is reached without repeated inter-node feedback. We derive a closed-form coverage-probability payoff via stochastic geometry and quantify belief convergence toward equilibrium using the Jensen--Shannon divergence (JSD) of the resulting power distributions. Monte-Carlo simulations validate the analytical coverage expressions and show that EBGT sustains the target coverage probability while reducing transmit power relative to fixed power control (FPC) and stochastic non-cooperative power control (SNCPC) baselines, particularly under stringent SINR and high-density regimes.

Problem

• Uplink power control
• Channel-state information
• Mutual interference
• Low-SWaP devices
• Heterogeneous IoT networks

Innovation

• Epistemology-aided Bayesian game theory
• Stochastic geometry
• Poisson point process
• Belief hierarchy
• Decentralized power control