Validation hybrid filter detection for Multi-User multiple input multiple output F-OFDM by Universal software radio Peripheral

Abstract

The present communication system is not enough efficient for the data rate requirement for the real-time applications like video conferencing and file sharing. To meet the requirement of the data rate, the concept of F-OFDM (Filter- Orthogonal Frequency Division Multiplexing) has been proposed. To further improve the efficiency of the system, Cognitive Radio is used. Cognitive Radio is capable of recognizing the spectrum utilization of the licensed users and accordingly switches the frequency bands. The aim of the paper is to detect the primary users in an efficient and novel way. Therefore, a hybrid filter detection technique is proposed for the Multi-User Multiple Input Multiple Output (MU-MIMO) F-OFDM system where multiple users are served with the same time and frequency resource and improve signal strength. The proposed technique uses the combination of cosine filtering, Bartlett segmenting, and Hamming windowing. The cosine filtering differentiated between the 5G traffic and the noise. Then, the filtered signals are segmented with the help of the Bartlett Segmenting for decreasing the noise variance then every segment is windowed using the Hamming windowing for maintaining the signal resolution. This paper proposes a Validation Hybrid Filter Detection for Multi-User Multiple Input Multiple Output (MIMO) Frequency-Division Multiplexing Orthogonal Frequency Division Multiplexing (F-OFDM) by Universal Software Radio Peripheral (USRP) based on Cognitive Radio (CR). Detection performance dropped to its least value for M−ary = 256 and 10 user SUs. Further, false alarm probability at −24 dB SNR was less than 0.01, which is excellent considering system characteristics of less than 0 dB SNR, 0.092% and > 98% global system error and detection likelihoods, respectively. The study also evaluated spectral efficiency to offer optimum detection at acceptable complexity levels for low SNR signals. The performance of the proposed technique is compared with the existing techniques and it is found that the proposed technique better and can effectively improve the detection accuracy of the signal with low SNR.