Intelligent Radio Resource Slicing for 6G In-Body Subnetworks
This addresses coexistence challenges for 6G IBSs supporting eXtended Reality applications, representing an incremental improvement in resource allocation for domain-specific wireless networks.
The paper tackles the dynamic and non-convex radio resource allocation problem for 6G In-body Subnetworks (IBSs) coexisting with cellular users, proposing an intelligent slicing strategy based on Soft Actor-Critic that enhances user experience by 12-85% under different network densities while maintaining eMBB service guarantees.
6G In-body Subnetworks (IBSs) represent a key enabler for supporting standalone eXtended Reality (XR) applications. IBSs are expected to operate as an underlay to existing cellular networks, giving rise to coexistence challenges when sharing radio resources with other cellular users, such as enhanced Mobile Broadband (eMBB) users. Such resource allocation problem is highly dynamic and inherently non-convex due to heterogeneous service demands and fluctuating channel conditions. In this paper, we propose an intelligent radio resource slicing strategy based on the Soft Actor-Critic (SAC) deep reinforcement learning algorithm. The proposed SAC-based slicing method addresses the coexistence challenge between IBSs and eMBB users by optimizing a refined reward function that explicitly incorporates XR cross-modal delay alignment to ensure immersive experience while preserving eMBB service guarantees. Extensive system-level simulations are performed under realistic network conditions and the results demonstrate that the proposed method can enhance user experience by 12-85% under different network densities compared to baseline methods while maintaining the target data rate for eMBB users.