Intelligent Dynamic Resource Allocation and Puncturing for Next-Generation Wireless Networks

As we progress from fifth generation (5G) to emerging 6G wireless, the spectrum of cellular communication services is set to broaden significantly, encompassing real-time remote healthcare applications and sophisticated smart infrastructure solutions, among others. This expansion brings to the forefront a diverse set of service requirements, underscoring the challenges and complexities inherent in next-generation networks. In the realm of 5G, enhanced mobile broadband (eMBB) and ultrareliable low-latency communications (URLLCs) have been pivotal service categories. As we venture into the 6G era, these foundational use cases will evolve and embody additional performance criteria, further diversifying the network service portfolio. This evolution amplifies the necessity for dynamic and efficient resource allocation strategies capable of balancing the diverse service demands. In response to this need, we introduce the intelligent dynamic resource allocation and puncturing (IDRAP) framework. leveraging deep reinforcement learning (DRL), IDRAP is designed to balance between the bandwidth-intensive requirements of eMBB services and the latency and reliability needs of URLLC users. The performance of IDRAP is evaluated and compared against other resource management solutions, including intelligent dynamic resource slicing (IDRS), policy gradient actor-critic learning (PGACL), system-wide tradeoff scheduling (SWTS), sum-log, and sum-rate. The results show an improved service satisfaction level (SSL) for eMBB users while maintaining the essential SSL threshold for URLLC services.