Intelligent multi-branch allocation of spectrum slices for inter-numerology interference minimization

Abstract

Network slicing and mixed-numerology access schemes cover a central role to enable the flexible multi-service connectivity that characterizes 5G radio access networks (RAN). However, the interference generated by the simultaneous multiplexing of radio slices having heterogeneous subcarrier spacing can hinder the isolation of the different slices sharing the RAN and their effectiveness in meeting the application requirements. To overcome these issues, we design a radio resource allocation scheme that accounts for the inter-numerology interference and maximizes the aggregate network throughput. To overcome the computationally complexity of the optimal formulation, we leverage deep reinforcement learning (DRL) to design an agent capable of approximating the optimal solution exploiting a model-free environment formulation. We propose a multi-branch agent architecture, based on Branching Dueling Q-networks (BDQ), which ensures the agent scalability as the number of spectrum resources and network slices increases. In addition, we augment the agent learning performance by including an action mapping procedure designed to enforce the selection of feasible actions. We compare the agent performance to several benchmarks schemes. Results show that the proposed solution provides a good approximation of the optimal allocation in most scenarios.

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

“…So, these methods are not practically feasible in real-time applications with regard to the new architectures of RAN. In some proposals such as [40], [120], [136], [151], [133], [141], [147], [143], [153], [144], [145], [146], [149], [150], [155], [142], [154], [118], [152], [157] computational and time complexity have been considered as an important issue, but in other reviewed proposals this issue has not been accounted for RAN slicing. Therefore, given the changes in the network and responding to users' requests at an acceptable time, this is a vital challenge that must be considered.…”

“…The simulation result shows that FL-CA performs better than AC and greedy power allocation methods in terms of spectrum efficiency. In [145], [146], two methods based on multi-branch dueling Q-network (MBDQN) are proposed for allocating radio resources to solve inter-numerology interference and maximize network throughput. First, the resource allocation problem is modeled as a non-convex integer problem and then solved by MBDQN.…”

“…The authors in [157] address mobility management and dynamic slice creation and management, along with the listed challenges in Section V-D1. In [157], a distributed method [141], [143], [144], [145], [146], [147], [142], [153], [120], [40], [155], [157] ✓ called UC-PA-RA is provided to allocate power and radio blocks to user devices within clusters in multiple F-APs into a single F-RAN. In this method, users' devices in a specific cluster of a F-AP can receive their information through similar RBs.…”

“…Also, the use of federated learning distributes the resource allocation algorithm between different entities in the RAN and reduces the computational and time complexity of the algorithm [144], [155]. In [40], [120], [143], [153], [145], [146], [142], [133], the speed of resource allocation to users of slices in RAN increases by distributing the resource allocation algorithm between different agents, also, the running of each agent on separate CPU thread can facilitate to create and manage the slices.…”