Refraction reverse learning based hybrid Namib Antenna Beetle Optimization for resource allocation in NB-IoT platform

The efficient allocation of resources is a critical challenge in the context of Narrowband IoT(NB-IoT) networks. This paper presents the Hybrid Namib Antenna Beetle optimization with Refraction Reverse Learning (HNAB-RRL) algorithm, which combines the Namib Antenna Beetle Optimization (NAB) and Beetle Antenna Search (BAS) Optimization algorithms with the Refraction Reverse Learning (RRL) algorithm to optimize resource allocation. The HNAB-RRL algorithm is designed to allocate available resources to multiple users efficiently and optimally. The algorithm takes into account factors such as signal-to-noise ratio, data rate, and available bandwidth to allocate resources to each user. The NAB and BAS algorithms are used to explore the search space and identify candidate solutions. These algorithms use a combination of local and global search strategies to find optimal solutions. The NAB algorithm focuses on finding the best combination of subcarriers and timeslots, while the BAS algorithm optimizes the allocation of power to the subcarriers. The RRL algorithm refines and optimizes the candidate solutions identified by the NAB and BAS algorithms. This machine learning technique learns from previous resource allocation decisions to improve future allocations. The algorithm takes into account factors such as user requirements, available resources, and previous allocation decisions to make optimal resource allocation decisions. The HNAB-RRL algorithm continuously updates the RRL algorithm with new resource allocation decisions to improve future allocations, leading to higher network throughput and better performance overall. The experimentation results revealed that the proposed HNAB-RRL model requires less time to run, provides better group fairness, and enhances performance while reducing high complexity. The achieved throughput is 105 Kbit/s, which is higher than existing methods such as E-CORA, fusion, and greedy algorithms. Overall, the HNAB-RRL algorithm provides a powerful and effective approach to resource allocation in NB-IoT networks, combining the strengths of multiple optimization techniques to find the best possible solutions.