Rényi Entropy-Based Spectrum Sensing in Mobile Cognitive Radio Networks Using Software Defined Radio

Abstract

A very important task in Mobile Cognitive Radio Networks (MCRN) is to ensure that the system releases a given frequency when a Primary User (PU) is present, by maintaining the principle to not interfere with its activity within a cognitive radio system. Afterwards, a cognitive protocol must be set in order to change to another frequency channel that is available or shut down the service if there are no free channels to be found. The system must sense the frequency spectrum constantly through the energy detection method which is the most commonly used. However, this analysis takes place in the time domain and signals cannot be easily identified due to changes in modulation, power and distance from mobile users. The proposed system works with Gaussian Minimum Shift Keying (GMSK) and Orthogonal Frequency Division Multiplexing (OFDM) for systems from Global System for Mobile Communication (GSM) to 5G systems, the signals are analyzed in the frequency domain and the Rényi-Entropy method is used as a tool to distinguish the noise and the PU signal without prior knowledge of its features. The main contribution of this research is that uses a Software Defined Radio (SDR) system to implement a MCRN in order to measure the behavior of Primary and Secondary signals in both time and frequency using GNURadio and OpenBTS as software tools to allow a phone call service between two Secondary Users (SU). This allows to extract experimental results that are compared with simulations and theory using Rényi-entropy to detect signals from SU in GMSK and OFDM systems. It is concluded that the Rényi-Entropy detector has a higher performance than the conventional energy detector in the Additive White Gaussian Noise (AWGN) and Rayleigh channels. The system increases the detection probability (PD) to over 96% with a Signal to Noise Ratio (SNR) of 10dB and starting 5 dB below energy sensing levels.

Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

“…The classification metrics adopted on this paper are adopted on the basis of two considerations. The first consideration is that they are commonly used in literature to evaluate the performance of the spectrum sensing algorithm [ 4 , 6 , 15 ]. The second consideration is they are derived from the binary hypothesis test, which is used to find out the presence of the PU or incumbent signal: is the noise in the absence of a PU/incumbent signal and indicates the presence of a PU/incumbent signal as defined in the following equations: where is the PU/incumbent signal, is the noise and i = 0, 1, 2, … , N , is the sample size under analysis.…”

“…Some studies have also used other entropy measures. For example, the authors in [ 6 ] have used Renyi entropy (a generalization of Shannon entropy) in the frequency domain, and they have compared it with the ED showing the superior performance of the Renyi entropy based approach. On the other side, the authors of [ 6 ] do not use a ML approach as in this study.…”

“…For example, the authors in [ 6 ] have used Renyi entropy (a generalization of Shannon entropy) in the frequency domain, and they have compared it with the ED showing the superior performance of the Renyi entropy based approach. On the other side, the authors of [ 6 ] do not use a ML approach as in this study. Another paper, which adopted Renyi and Shannon entropy in combination with the Parzen window, is [ 5 ], where the performance of the proposed approach was applied to simulated data.…”

“…A survey on the application of ML and its extension to Deep Learning is provided in Section 2 of this paper, but it can be anticipated that this study focuses on a specific implementation of ML for spectrum sensing based on the application of entropy measures. A number of papers have applied entropy measures [ 4 , 5 , 6 ] to spectrum sensing because of its robustness to the presence of noise and because it reduces the dimensionality of the channel status evaluation problem. Spectrum sensing may involve the processing of a large number of samples (e.g., the digital representation of the signal in space), which can be reduced by applying feature extraction including the application of entropy measures.…”

Artificial Neural Network Controller for a Modular Robot Using a Software Defined Radio Communication System

“…Furthermore, the evolution of communication systems throughout the years has led to their application in robots to enhance their performance [15]. One of the more novel approaches in communication systems corresponds to the development of software defined radio (SDR) technology, which is a radio system where the components are implemented using software to interact with hardware [16,17]. In this project, the Raspberry Pi 3 device was used to implement the SDR since it allows for the development of wireless applications with a robust low-cost embedded system, which has been used as a communication system for robot control in the monitoring and storage of data in real time [18].…”