Hybrid Optimization Approach for Handoff Strategy–Based Spectrum Allocation in Cognitive Radio Network

Abstract

Device-to-device (D2D) transmission is essential for enhancing the functionality of fifth-generation (5G) networks. This paper addresses the need for effective power allocation and resource management for D2D users, who operate as secondary users (SUs) alongside primary users (PUs). Ensuring that D2D operations do not disrupt PU interactions is crucial. Traditional bandwidth distribution approaches rely on complete channel state information (CSI) from the base station (BS), leading to uncertainty fin resource allocation. To get rid of these challenges, this paper proposes a novel handoff strategy based on spectrum allocation in cognitive radio networks (CRNs). The data priority–based channel allocation is carried out using proposed hybrid optimization cuttlefish updated dwarf mongoose optimization (CUDMO). It is the combination of both cuttle fish algorithm (CFA) and dwarf mongoose optimization (DMO) algorithms. This optimization considers constraints such as coverage, signal strength, distance, bandwidth and improved strategy like signal-to-noise ratio–channel usability (SNR-CU). Furthermore, an improved fuzzy logic–based proactive handoff mechanism, the fuzzy-induced modified rules for channel selection (FIMRCS), is introduced. This scheme optimally selects channels, minimizing service interruption during handoff. In the dynamic multichannel selection (DMCS) scheme, parameters like channel rank, channel transmission, and channel usability are considered as the constraints while selecting the channel. They are evaluated against a set of 27 defined rules, ensuring efficient data transmission through the chosen channels. Finally, the performance of proposed CUDMO algorithm is contrasted over state-of-the-art models in terms of various constraints. The CUDMO for Device 450 generated a bandwidth of 1.175 bps, surpassing the lower bandwidth achieved by conventional strategies.