Spectrum Sensing and Resource Allocation for Proficient Transmission in Cognitive Radio with 5G

Abstract

In orthogonal frequency division multiplexing (OFDM)-based cognitive radio network–cooperative spectrum sensing (CRN-CSS), resource allocation is critical due to the introduction of interference during spectrum sensing and transmission. This paper resolves this problem of interference through proficient spectrum sensing and resource allocation to improve data transmission. CRN-CSS network is integrated with 5G supported by multiple-input–multiple-output equipped fusion center (FC) to handle massive number of users without loss in connectivity. OFDM-based data transmission is adapted in CRN-CSS to achieve a better transmission rate. In the integrated network, grouping process is initiated by a balanced K-means clustering algorithm to preserve cooperation among secondary users (SUs). Dynamic slot allocation scheme, two-stage multi-slot channel assignment method, is proposed to avoid interference during spectrum sensing. Sensing errors are minimized with the assistance of the energy spectral density-based energy detection spectrum sensing method. For global decision-making, the channel state weighted graph scheme is introduced in which spectrum agent plays a vital role. Finally, resource allocation is carried out by the FC by utilizing efficient Karush–Kuhn–Tucker (EKKT) conditions. Through EKKT method spectrum, transmission powers are allocated to SUs together in such a way that interference with primary user is avoided. Meanwhile, OFDM-based transmission with quadrature phase shift keying modulation scheme reduces peak-to-average-power ratio that leads to high transmission rate. The proposed network is extensively simulated in the NS-3.26 simulation tool and the performance is evaluated from the following performance metrics: throughput, capacity, network utility, transmission power, and transmission rate.