Sesham Srinu

FPGA implementation and performance study of spectrum sensing based on entropy estimation using cyclic features

This work presents a spectrum sensing technique based on the entropy of frequency domain autocorrelation of receiving signal at different cyclic frequencies. The performance of the proposed sensing technique is compared with other sensing techniques such as energy detection using Bayesian and Neyman-Pearson criteria, entropy estimation under frequency domain, cyclostationary feature detection. The performance of sensing algorithms is also analyzed for single node and multinode/cooperative environment under most probable channel effects such as fading, shadowing, receivers uncertainty and free space path loss using Monte-Carlo methods. Simulation results reveal that the proposed sensing technique is able to detect signals of signal-to-noise ratio up to -24dB with five nodes in cooperation while maintaining a false alarm probability of 0.1 and a detection probability of 0.9. The proposed sensing algorithm is also implemented in Virtex-4 Field Programmable Gate Arrays.

FPGA implementation of cooperative spectrum sensing for Cognitive Radio networks

Spectrum sensing is a critical and major component in Cognitive Radio (CR). Due to low signal to noise ratio (SNR), fading and shadowing effects a single CR node is not reliable for accurate detection. This motivates the study of cooperative spectrum sensing. In this paper, each CR uses energy detection method based on Neyman-Pearson criteria for local spectrum sensing. The individual decisions of each CR are integrated in the fusion center using different fusion rules. In this paper more specifically first the authors analyses and compares the performance of different fusion rules such as logical AND, OR and Equal Gain Combining (EGC) integrated either soft or hard decision rules. Second, the cooperative sensing technique is implemented in the Xilinx Virtex-2pro XC2VP30 (FFG896-7C) Field Programmable Gate Array (FPGA). Hardware in loop (HIL) simulation technique is used for verification of the algorithm in FPGA. HIL simulation result shows that EGC fusion rule is more appropriate for sensing the considered DVB-T signal upto −16dB SNR with probability of detection (Pd) ≥ 0.9 and probability of false alarm (Pf) ≤0.1. The implementation results reveal that the algorithm works with operating frequency of 149.5MHz.

Spectrum sensing based on entropy estimation using cyclostationary features for Cognitive radio

This work presents a new spectrum sensing technique based on entropy estimation of autocorrelation estimates of received signal at different cyclic frequencies. The performance of the proposed entropy detection is compared with cyclostationary detection based on spectral coherence function (SCF) and energy detection methods. Both the algorithms are verified under single node and multinode/cooperative environment. Sensing performance of both the algorithms are analyzed using Monte-Carlo methods. Simulation result discloses that, entropy detection algorithm detect signals of signal-to-noise ratio (SNR) upto -26dB whereas SCF method detects signal upto -23dB using seven nodes in cooperation for detection probability Pd≥0.9 and false alarm probability Pfa≤0.1. The proposed sensing algorithm is also implemented in Virtex-4 (XC4VSX35-10FF668) Field Programmable Gate Arrays.

Effective cooperative wideband sensing using energy detection under suspicious Cognitive Radio Network

Abstract Spectrum sensing is an essential concept in Cognitive Radio (CR) systems. To overcome the single node sensing issue that arises due to channel impediments, cooperative/multinode sensing is being used. Although cooperation among multiple cognitive users enhances the sensing performance, presence of few suspicious CR (SCR) users may severely degrade the sensing efficiency of the system. The SCR is a communication system, which intentionally sends the false report to other cognitive users in the network for bias use of the vacant bands. In this paper, cooperative wideband spectrum sensing (CWSS) with multiple SCR user elimination is proposed to increase the sensing performance. The performance of the algorithm is evaluated under probable channel impediments. The simulation results reveal that there is a significant improvement in cooperative sensing performance by elimination of multiple SCR users in the CR network. The wideband spectrum sensing algorithm is also implemented in Xilinx Virtex-4 Field Programmable Gate Array.

Optimal Multinode Sensing in a Malicious Cognitive Radio Network

Spectrum sensing is an essential function in cognitive radio systems for dynamic spectrum access. Multinode sensing is a technique being used in cognitive radio networks to enhance the sensing performance using space diversity concept. The challenges in multinode spectrum sensing are the prediction of signal status in multiple frequency bands in a low signal-to-noise ratio (SNR) regime and sensing reliability. The weighted gain combining (WGC) and the equal gain combining are the two soft decision cooperative sensing techniques being used frequently in literature. In this paper, we introduce weighted gain cooperative sensing using differential evolution (DE) and adjusted box-plot methods to exalt the sensing reliability together with the sensing performance. The main advantage of the WGC method using DE is that it can generate optimal weights independent of received signal characteristics, which is an indispensable condition to realize the system in real time. The proposed optimal cooperative sensing method with entropy and cyclic features enhances the sensing performance, and it is less severe to noise uncertainties compared with the traditional sensing methods. It can detect the low SNR signals up to -24 dB at desired sensing performance (Pf = 0.1 and Pd = 0.9) with a frame size of 256 and using five nodes in cooperation. It is a significant improvement for IEEE 802.22 WRAN systems, which work under low SNR regime.

FPGA implementation of spectrum sensing based on energy detection for Cognitive Radio

Spectrum sensing is a critical component of the Cognitive Radio that detects the presence of primary user signal in a channel. In this paper energy detection technique based on Neyman-pearson criterion is used to detect the presence of deterministic primary user (PU) signals in the channel. We have considered three different kinds of modulated signal such as BPSK, QPSK, DVB-T (2K mode) under additive white Gaussian noise (AWGN) and Rayleigh fading channel environment as specified in IEEE 802.22 standard for validating the algorithm. The simulation result shows that the energy detector achieves the desired probability of detection (≥0.9) with probability of false alarm (≤0.1) at low signal to noise ratio (SNR) up to −8dB for QPSK and DVB-T modulated signal with sample size of 64. The algorithm is also implemented in Xilinx Virtex2pro XC2VP30 (FFG896-7) Field Programmable Gate Array (FPGA). Hardware in loop (HIL) technique is used for verifying the algorithm in FPGA. The implementation result reveals that the algorithm fits into the Virtex2pro FPGA and can execute with operating frequency between 110 to 138 MHz for different sample size of primary user signals.

Wideband spectrum sensing based on energy detection for Cognitive Radio network

Spectrum sensing is an essential concept in Cognitive Radio (CR) systems. It exploits the inefficient utilization of radio frequency spectrum without causing destructive interference to the licensed/primary user communication. In recent past, most of the studies on spectrum sensing are focused on cooperative/multinode detection approaches. However, they are confined to the detection of signals in a single frequency band or narrow band. In order to improve the opportunistic throughput, the CR must sense the signals in multiple bands or wideband. This paper presents a wideband spectrum sensing technique based on energy measurement in each sub band. The simulation result shows that the wideband sensing algorithm detects primary user (PU) signal upto −8dB average SNR with sample size 256, for 8 nodes in cooperation. It is able to detect only upto 0dB average SNR for singe node detection with sample size 64 with detection probabilty Pd ≥ 0.9, and false alarm probability Pf ≤ 0.01. We have also implemented the wideband spectrum sensing in Xilinx Virtex-4 (XC4VSX35-FFG668-10) Field programmable Gate Arrays.

FPGA realization of spectrum sensing techniques for cognitive radio network

Spectrum sensing is one of the most important and challenging task in cognitive radio network. Its main functionality is to detect the presence of primary user signal and to be cognizant about its surrounding radio spectrum. In this paper, perfomrance of single node spectrum sensing based on energy and cyclostationary feature detection algorithms are compared for different signal to noise ratio (SNR). We have also extended single node sensing to cooperative sensing using AND, OR and equal gain combining (EGC) fusion rule with energy detection technique in each node. We also implemented the cooperative sensing technique in Xilinx Vertex-2pro XC2VP30 (FFG896–7C) Field Programmable Gate Array (FPGA). The experimental results show that cooperative sensing technique using EGC fusion rule can sense the primary user signal upto −16dB SNR with 60% of resource utilization, 140 MHz operating frequency. As a case study, DVB-T signal is considered as primary user signal in this paper.

Cooperative wideband sensing based on entropy and cyclic features under noise uncertainty

Spectrum sensing is a key component to realise the cognitive radio. The main requirements of spectrum sensing are the prediction of signal status in multiple frequency bands in a low signal-to-noise ratio (SNR) and decision reliability. This study proposes a novel multinode wideband sensing technique to predict the status of multiple frequency bands based on the integration of entropy and cyclic properties of received signals. It uses the uncertainty and auto-correlation properties of the deterministic signal and noise in the frequency domain for signal detection. To increase the decision reliability, cooperative sensing techniques are being used for spectrum sensing. Although cooperation among multiple cognitive users enhances the sensing performance, presence of few suspicious/malicious cognitive users severely degrade the decision reliability of the system. Hence, in this work, generalised extreme studentised deviate and adjusted box-plot methods are introduced to eliminate multiple malicious users in the cooperation. The proposed sensing method shows the best performance and is less severe to noise uncertainties compared to the traditional sensing methods in the literature. It enhances the sensing performance by 2.5 dB using five nodes in cooperation for same sensing parameters compared to other detection methods. It is a significant improvement for IEEE 802.22 systems that work under low SNR environment.

Cooperative spectrum sensing under noisy control channel for Cognitive Radio Network

Spectrum sensing is a vital phase in Cognitive Radio (CR) to identify the unutilized spectrum for improving the spectrum utilization. Cooperative sensing is being used for spectrum sensing to mitigate the effect of shadowing and fading in the channel. In cooperative sensing, the channels to be sensed by cognitive users are assumed to be noisy. Moreover, channel noise is also presents in between CR users and fusion center which reduces the cooperative sensing detection accuracy. In this paper, we studied the effect of noise in the control channel on detection probability and used forward error correction technique with convolutional encoder to mitigate the effect of control channel noise. Energy detection based on Neyman-Pearson criteria is used in each CR and sensing performance is analyzed using Monte-Carlo methods. The simulations are carried out with different signal-to-noise ratio (SNR) in the control channel with and without convolutional coding. The results reveal that the detection probability of the algorithm improves significantly with convolutional coding.