Optimizing Resource Block Allocation for Multicast in Beyond 5G Networks

New radio (NR) and non-orthogonal multiple access (NOMA) offer scalable and efficient resource allocation in Beyond 5G (B5G) networks. NR implements mixed numerology with flexible frame structures for future compatibility, whereas NOMA allows users with different channel states to share an identical Physical Resource Block (PRB). Multi-connectivity enables a user to connect to multiple networks for reliability, and multicast conveys data to users simultaneously that request the same content. However, resource allocation in the NOMA-based mixed numerology system with multi-connectivity for multicast remains unexplored. The problem is challenging due to 1) the different shapes of PRBs in NR and 2) the shared locations of PRBs in a frame with NOMA. In this paper, we formulate a new optimization problem, named Multicast, Multi-connectivity, and Multi-Dimensional Resource Allocation Problem (M<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>DRAP), and prove its NP-hardness and inapproximability. We propose an approximation algorithm for general M<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>DRAP with the ideas of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multicast Inter-Numerology Relation</i>, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Layer Dissimilarity</i>, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Subgrouping Nonuniformity</i>, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Segmentation Preference</i>. To find the intrinsic properties of PRB allocation for multicast in NOMA-based networks, we consider a single B5G usage scenario (e.g., eMBB, URLLC, or mMTC) and propose another approximation algorithm. Simulations demonstrate our algorithms improve the weighted sum rate by over 50% and increase the user satisfaction ratio by 1.5x.