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This paper considers a cooperative spectrum sensing scheme for cognitive radio network that uses a power splitting mode for simultaneous sensing and harvesting of energy. The objective is to maximize the residual harvested energy over the total energy consumption in the sensing process. An optimization problem is formulated in terms of the number of secondary user (sensing) nodes, the number of samples...
Energy efficient and reliable spectrum sensing become essential in cooperative cognitive radio networks. However, cooperation in spectrum sensing (SS) is often vulnerable to several security threats; one such operation is primary user emulation attack (PUEA). To address the issues, an optimization framework is developed in this work to minimize the energy consumption in cooperative spectrum sensing...
The demand for high throughput at low power consumption requires spectrum efficient as well as energy efficient design in next generation wireless networks. However, spectrum efficiency (SE) and energy efficiency (EE) can not be improved simultaneously. To this aim, in this work, the EE-SE trade-off issue is investigated in a cooperative cognitive radio network (CCRN). The secondary users (SUs) either...
Compressed Sensing (CS) can be used to reconstruct signals by using lesser number of samples than what is required by the Nyquist sampling rate. However, the reconstruction requires a large number of calculations. Block Compressed Sensing (BCS) reduces the computational complexity by reconstructing the signal in blocks, using the same measurement matrix for each block. BCS requires lesser memory to...
A cooperative cognitive radio (CR) network that accomplishes both spectrum sensing (SS) and sharing is considered in this work. System model is assumed to be a cognitive ad hoc network that consists of multiple secondary transmitter (ST) and receiver (SR) pairs sharing transmission over a spectrum with a primary network of single transmitter-receiver pair. Based on SS outcome, secondary users (SUs)...
Compressed sensing (CS) in imaging is essential in many practical situations and does need suitable algorithm for reconstruction from this highly incomplete observations/measurements. Measurements are also noisy in majority of the cases. This paper addresses CS-reconstruction of images from multiple channel data acquisition with under sampling measurements. An adaptive filtering based stochastic approximation...
Compressed sensing (CS) or compressive sampling deals with reconstruction of signals from limited observations/ measurements far below the Nyquist rate requirement. This is essential in many practical imaging system as sampling at Nyquist rate may not always be possible due to limited storage facility, slow sampling rate or the measurements are extremely expensive e.g. magnetic resonance imaging (MRI)...
In this paper, the problem of joint spectrum sensing and data transmission in a two-user cooperative cognitive radio network is studied. Tractable solution for optimal power allocation is proposed for cognitive sources for both sensing and data transmission slots with an aim to maximize sum throughput of the system while meeting the constraints of probability of detection of PU's signal and total...
This paper proposes a joint spectral sensing and secondary data transmission scheme in decode and forward relay assisted cognitive radio networks (CRNs). Sensing information of cognitive source (CS) and cognitive relay (CR) are cooperatively combined at CS to determine the possible presence of primary user (PU). An optimization framework is developed to determine the sensing time, data transmission...
An energy-efficient cognitive radio system design is suggested that simultaneously meets spectrum sensing reliability and secondary data transmission rate constraints. System model involves co-located multiple amplify-and-forward relays in single cognitive radio source-destination (S-D) environment with a primary focus on optimal relay power allocation strategy. The problem is mathematically formulated...
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