Optimal Resource allocation in NOMA-based M2M communication using Hybrid Rider Optimization with FireFly: Power Saving Strategy of IoT

Nowadays, the Orthogonal Multiple Access (OMA) principle has utilized for allocating proper radio resources in wireless networks. However, as the count of users rises, OMA-based approaches may not satisfy the stringent emerging requirements including very low latency, very high spectral efficiency, and massive device connectivity. Moreover, there are significant challenges in cellular-enabled Machine-to-Machine (M2M) communications due to the unique features of M2M-based applications. In order to overwhelm these challenges, non-orthogonal multiple access (NOMA) principles emerge as a solution to enhance the spectral efficiency while allowing some degree of multiple access interference at receivers. Hence, this paper intends to develop an optimal resource allocation mechanism for M2M communication. Here, the nonlinear energy harvesting performed with the aid of an accessing technology termed as NOMA. The key objective of the proposed resource allocation model is the minimization of the total energy consumption of the network. For attaining the minimized power consumption, the time allocation, and transmission power of NOMA is optimally tuned by a hybrid optimization algorithm. The proposed hybrid algorithm merges the beneficial concepts of Rider Optimization Algorithm (ROA) and FireFly (FF) algorithm and implements a new algorithm termed as FireFly Modified Bypass-based Rider Optimization Algorithm (FMB-ROA). Finally, the analysis of total energy concerning various constraints validates the performance of the proposed model over conventional models.