Power and channel use cut by learning how people feel delays in body networks
Perception-Aware Joint Power and Sub-Band Allocation for 6G In-Body Subnetworks
Networking and Internet Architecture
Summary
In-body wireless networks around the human body often face interference that forces devices to use more power to keep up with virtual reality experiences. The paper’s authors created a method that learns how much delay people can actually notice and uses this information to better manage power and wireless channels. By doing this, their system can reduce the power needed while still making sure the user experience stays smooth. Simulations showed the new approach saves a lot of power, especially when many devices are close together.
in-body subnetwork6G wirelessextended realityquality of servicequality of experiencepower controlsub-band allocationGaussian mixture modelinglatencyradio resource management
Authors
Samira Abdelrahman, Hossam Farag
Abstract
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).