EBGT：用于随机分布物联网层级上行链路功率控制的认识论辅助贝叶斯博弈理论
EBGT: Epistemology-aided Bayesian Game Theory for Uplink Power Control in Stochastically Distributed IoT Tiers
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中文总结 AI 辅助
针对随机分布物联网上行链路功率控制的CSI缺失与设备算力受限问题，提出EBGT框架，通过双层信念层级与随机几何推导，在维持覆盖概率的同时降低发射功率，性能优于FPC与SNCPC基线。
中文摘要 AI 辅助
密集异构物联网（IoT）层级中的上行链路功率控制受限于不完整的信道状态信息（CSI）和相互干扰，而小尺寸、低功耗（SWaP）设备无法承担传统分布式方案所需的反馈与计算开销。本文提出EBGT，一种用于随机分布物联网网络中分散式上行链路功率最小化的认识论辅助贝叶斯博弈理论框架。干扰用户通过泊松点过程（PPP）建模为空间随机节点，每个设备通过包含对手间的认识论信念和自身内省评估的双层信念层级来推理对手，从而无需节点间反复反馈即可达到发射功率均衡。我们通过随机几何推导了闭式覆盖概率收益，并使用所得功率分布的Jensen-Shannon散度（JSD）量化信念向均衡的收敛情况。蒙特卡洛仿真验证了分析性覆盖表达式，结果显示EBGT在维持目标覆盖概率的同时，相较于固定功率控制（FPC）和随机非合作功率控制（SNCPC）基线降低了发射功率，尤其在严格的SINR和高密度场景下表现突出。
英文摘要
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.
