Power and frequency use improved for body wireless XR in 6G networks
Perception-Aware Joint Power and Sub-Band Allocation for 6G In-Body Subnetworks
Networking and Internet Architecture
Summary
Connecting devices inside or around the human body for detailed virtual reality needs very fast and reliable wireless links, but many such devices together can cause signals to interfere and slow things down. The authors designed a new way to manage wireless power and frequencies by taking into account how much delay people can actually notice, not just technical demands. They created a model that learns user delay sensitivity and used it to better assign wireless resources, which saved a lot of power while keeping the experience smooth for users. This approach works especially well when many devices are close together and need quick responses.
What this means in practice
- •For wireless network engineers: Optimize power use and frequency assignments for ultra-short-range body area networks supporting virtual reality gear with strict delay needs.
- •For medical device developers: Design energy-efficient radio communication methods for wearable and implantable devices that support immersive user experiences without exceeding delay thresholds that users can perceive.
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).