ABSTRACT Cyclostationary feature detection is one of the most powerful spectrum sensing techniques used for cognitive radio (CR) systems. This is because of its robustness against noise uncertainties. However, this technique needs high sampling rates, which is limited by the state-of the-art analog to digital converters (ADCs), especially in wideband regime. Comressive sensing (CS) was used by many researchers for solving this problem via sub-Nyquist sampling rates. However CS solves the high sampling rate problem, but it does ...

ABSTRACT Cyclostationary feature detection is one of the most powerful spectrum sensing techniques used for cognitive radio (CR) systems. This is because of its robustness against noise uncertainties. However, this technique needs high sampling rates, which is limited by the state-of the-art analog to digital converters (ADCs), especially in wideband regime. Comressive sensing (CS) was used by many researchers for solving this problem via sub-Nyquist sampling rates. However CS solves the high sampling rate problem, but it does ...

ABSTRACT Cyclostationary feature detection is one of the most powerful spectrum sensing techniques used for cognitive radio (CR) systems. This is because of its robustness against noise uncertainties. However, this technique needs high sampling rates, which is limited by the state-of the-art analog to digital converters (ADCs), especially in wideband regime. Comressive sensing (CS) was used by many researchers for solving this problem via sub-Nyquist sampling rates. However CS solves the high sampling rate problem, but it does ...

Abstract—In this paper, cooperative compressive spectrum sens-ing is considered to enable accurate sensing of the wide-band spectrum. The proposed algorithm is based on compressive sensing theory and aims to reduce the hardware complexity of the cognitive radio receiver by distributing the sensing work among groups of sensing nodes. The proposed algorithm classifies the cooperated sensing nodes into different sensing groups depending on the quality of the reporting channel between the sensing node and the ...

Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, we provide an energy model for WSNs considering the physical layer and MAC layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel. We show how the transmission power must be chosen in order to achieve energy-efficient communications over AWGN channel. We also find that, for each modulation scheme, there are optimal transmission power at which the energy consumption is minimized. Moreover, we investigated the energy saving gained from optimizing the constellation size.

Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, we provide an energy model for WSNs considering the physical layer and MAC layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel. We show how the transmission power must be chosen in order to achieve energy-efficient communications over AWGN channel. We also find that, for each modulation scheme, there are optimal transmission power at which the energy consumption is minimized. Moreover, we investigated the energy saving gained from optimizing the constellation size.

Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, we provide an energy model for WSNs considering the physical layer and MAC layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel. We show how the transmission power must be chosen in order to achieve energy-efficient communications over AWGN channel. We also find that, for each modulation scheme, there are optimal transmission power at which the energy consumption is minimized. Moreover, we investigated the energy saving gained from optimizing the constellation size.

Abstract—This work proposes a reduced complexity secure cooperative cognitive radio spectrum sensing algorithm for wideband systems. The main goal of the proposed algorithm is to reduce the cognitive radio receiver hardware complexity by distributing the sensing work among the trustful nodes only. Each cognitive radio node uses its local sensing matrix, which is a part of a global sensing matrix at the fusion center, to sense the wideband analog signal. The fusion center selects and rearranges the compressed data from the trustful ...

Abstract: Cooperative wide-band spectrum sensing has been considered to enable cognitive radio operation of wireless regional area networks (WRAN) in the UHF and VHF TV broadcasting bands. In this paper, cooperative compressed spectrum sensing is considered to enable fast sensing of the wide-band spectrum. The speed and accuracy of spectrum sensing are improved by further optimization of the compressed sensing receiver, which is done blindly without any prior knowledge of the sensed signal. Enhanced ...

Abstract—This paper proposes a spectrum sensing algorithm for cognitive radio transceivers to save in sampling rates and receiver hardware complexity. The proposed receiver blindly optimizes its compressed sensing sparsifying bases and has improved receiver-operating characteristics. Keywords: Cognitive radio; Compressive sensing; Wavelet Packet; Blind Optimization, Entropy, sub-Nyquist sampling