发表机构
Aswan University; Aalborg University（阿斯旺大学; 奥尔堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出感知感知的联合功率与子带分配框架，结合学习感知模型与李雅普诺夫优化，在6G体内子网络中降低发射功率并保持体验质量。
AI 中文摘要
体内子网络（IBS）预计将成为第六代（6G）网络中沉浸式扩展现实（XR）服务的关键推动因素，通过在人体周围提供超短距离、低延迟的无线连接。然而，多个IBS的密集共存导致严重的同信道干扰，需要增加发射功率以满足XR应用对延迟的严格要求。现有的干扰管理方法仅根据应用级服务质量（QoS）要求分配无线电资源，忽视了人类用户的感知限制。本文提出了一种感知感知的联合功率控制和子带分配框架，将用户的延迟感知整合到面向XR的IBS的无线电资源分配中。首先，通过结合高斯混合模型（GMM）与监督学习，开发了一种基于学习的感知模型，以建立延迟感知阈值的统计模型。然后，将学习到的感知模型纳入随机无线电资源分配问题中，该问题通过李雅普诺夫漂移加惩罚重新表述，并通过低复杂度的每时隙资源分配过程求解。在现实的IBS内部和IBS间传播条件下的系统级模拟表明，所提出的方法显著提高了无线电资源效率，在严格的延迟要求下实现了高达26%的发射功率降低，在密集IBS部署中实现了约60%的功率节省，同时保持了所需的质量体验（QoE）。
英文摘要
In-body subnetworks (IBSs) are expected to become a key enabler of immersive eXtended Reality (XR) services in sixth-generation (6G) networks by providing ultra-short-range, low-latency wireless connectivity around the human body. However, the dense coexistence of multiple IBSs leads to severe co-channel interference, requiring increased transmit power to satisfy the stringent latency requirements of XR applications. Existing interference management approaches allocate radio resources solely according to application-level Quality-of-Service (QoS) requirements, overlooking the perceptual limitations of human users. This paper proposes perception-aware joint power control and sub-band allocation framework that integrates users' delay perception into radio resource allocation for XR-oriented IBSs. A learning-based perception model is first developed by combining Gaussian mixture modeling (GMM) with supervised learning to develop a statistical model of the delay perception threshold. The learned perception model is then incorporated into a stochastic radio resource allocation problem, which is reformulated using a Lyapunov drift-plus-penalty and solved through a low-complexity per-slot resource allocation procedure. System-level simulations under realistic intra- and inter-IBS propagation conditions demonstrate that the proposed approach substantially improves radio resource efficiency, achieving up to 26% transmit power reduction under stringent latency requirements and approximately 60% power savings in dense IBS deployments, while maintaining the required Quality of Experience (QoE).
