1,619 research outputs found
Compressive Identification of Active OFDM Subcarriers in Presence of Timing Offset
In this paper we study the problem of identifying active subcarriers in an
OFDM signal from compressive measurements sampled at sub-Nyquist rate. The
problem is of importance in Cognitive Radio systems when secondary users (SUs)
are looking for available spectrum opportunities to communicate over them while
sensing at Nyquist rate sampling can be costly or even impractical in case of
very wide bandwidth. We first study the effect of timing offset and derive the
necessary and sufficient conditions for signal recovery in the oracle-assisted
case when the true active sub-carriers are assumed known. Then we propose an
Orthogonal Matching Pursuit (OMP)-based joint sparse recovery method for
identifying active subcarriers when the timing offset is known. Finally we
extend the problem to the case of unknown timing offset and develop a joint
dictionary learning and sparse approximation algorithm, where in the dictionary
learning phase the timing offset is estimated and in the sparse approximation
phase active subcarriers are identified. The obtained results demonstrate that
active subcarrier identification can be carried out reliably, by using the
developed framework.Comment: To appear in the proceedings of the IEEE Global Communications
Conference (GLOBECOM) 201
Spectrum Sensing of DVB-T2 Signals in Multipath Channels for Cognitive Radio Networks
© 2018 VDE VERLAG GMBHIn this paper, spectrum sensing of digital video broadcasting-second generation terrestrial (DVB-T2) signals in different fading environments with energy detection (ED) is considered. ED is known to achieve an increased performance among low computational complexity detectors, but it is susceptible to noise uncertainty. By taking into consideration the edge pilot and scattered pilot periodicity in DVB-T2 signals, a low computational complex noise power estimator is proposed. It is shown analytically that the choice of detector depends on the environment, the detector requirements, the available prior knowledge and with the noise power estimator. Simulation confirm that with the noise power estimator, ED significantly outperforms the pilot correlation-based detectors. Simulation also show that the proposed scheme enables ED to obtain increased detection performance in fading channels
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