Hybrid Optimization‐Based Channel Allocation and Scheduling in Cognitive Radio Network With CoMP JT

ABSTRACT The cognitive radio network (CRN) is viewed as a potential technology for opportunistic usage of the unlicensed or underutilized licensed spectrum in upcoming wireless networks. CoMP JT is an effective method for boosting the cellular networks' functionality. However, it is still difficult to allocate radio resources to UE in an efficient manner from many base stations. Encouraging every UE to function in JT‐CoMP mode may result in fewer radio resources being available, which could lead to inefficiencies. To address these issues, this paper proposes a new CoMP JT channel allocation and scheduling model for CRN that efficiently manages its resources, enhances the performance of the network, and provides a better user experience. The work involves two stages, namely, (i) spectrum sensing and (ii) channel allocation and scheduling. Initially, spectrum sensing is carried out by performing the spectrum classification using the DL method, namely, the improved LSTM model. Subsequently, channel allocation and scheduling in CRN are performed optimally by a new algorithm termed the JFI‐SSO algorithm. The given optimization problem is solved under the consideration of channel usability, SNR, and throughput. The hybrid JFI‐SSO algorithm integrates JSO and SSOA. Finally, the analysis is performed to validate the efficacy of the JFI‐SSO algorithm. The proposed JFI‐SSO scheme is validated over traditional strategies and state‐of‐the‐art models. As a result, the suggested JFI‐SSO has achieved a faster convergence of 0.462 across the 18th to 25th iteration in contrast to other techniques. Thus, the suggested JFI‐SSO resulted in offering optimal channel allocation with lesser cost.