Joint Q-learning Based Resource Allocation and Multi-Numerology B5G Network Slicing Exploiting LWA Technology

• Published in: IEEE access 2024-01, Vol.12, p.1-1
• Main Authors: Elmosilhy, Noha A., Elmesalawy, Mahmoud M., Ibrahim, Ibrahim I., El-Haleem, Ahmed M. Abd
• Format: Article
• Language: English
• Subjects: 5G mobile communication; Algorithms; HetNet; Long Term Evolution; LWA; Machine learning; Multi-RAT; Network slicing; Numerical analysis; Numerology; Optimization; Performance evaluation; Quality of service; Quality of service architectures; Regret Matching; Resource allocation; Resource management; Traffic congestion; Ultra reliable low latency communication; User satisfaction; Wireless communications; Wireless fidelity; Wireless LAN; WLAN
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3363162

The emergence of the sixth generation (6G) era has highlighted the importance of Network Slicing (NS) technology as a promising solution for catering the diverse service requests of users. With the presence of a large number of devices with different service requests and since each service has different goal and requirements; efficiently allocating Resource Blocks (RBs) to each network slice is a challenging task in order to meet the desired Quality of Service (QoS) standards. However, it is worth noting that the majority of research efforts have primarily concentrated on cellular technologies, leaving behind the potential benefits of utilizing unlicensed bands to alleviate traffic congestion and enhance the capacity of existing LTE networks. In this paper we propose a novel idea by exploiting LTE-WLAN Aggregation technology (LWA) in Multi-Radio Access Technology (RAT) Heterogeneous Networks (HetNet), aiming to solve radio resource allocation problem based on the Radio Access Network (RAN) slicing and 5G New Radio (NR) scalable numerology technique. A joint optimization problem is proposed by jointly finding an efficient resource allocation ratio for each slice in each Base Station (BS) and by finding the optimum value of scalable numerology with the objective of maximizing users' satisfaction. In order to solve this problem, a novel three-stage framework is proposed which is based on channel state information as a pre-association stage, Reinforcement Learning (RL) algorithm as finding the optimum value of slice resource ratio and scalable numerology, and finally Regret Learning Algorithm (RLA) as users' re-association phase. Furthermore, a comprehensive performance evaluation is conducted against different base-line approaches. The simulation results show that our proposed model balance and achieve improvement in users' satisfaction by deploying the proposed Multi-RAT Het-Net architecture that leverage LWA technology.