Dynamic compressive spectrum sensing for cognitive radio networks

Abstract

Abstract-In the recently proposed collaborative compressive sensing, the cognitive radios (CRs) sense the occupied spectrum channels by measuring linear combinations of channel powers, instead of sweeping a set of channels sequentially. The measurements are reported to the fusion center, where the occupied channels are recovered by compressive sensing algorithms. In this paper, we study a method of dynamic compressive sensing, which continuously measures channel powers and recovers the occupied channels in a dynamic environment. While standard compressive sensing algorithms must recover multiple occupied channels, a dynamic algorithm only needs to recover the recent change, which is either a newly occupied channel or a released one. On the other hand, the dynamic algorithm must recover the change just in time. Therefore, we propose a least-squared based algorithm, which is equivalent to 0 minimization. We demonstrate its fast speed and robustness to noise. Simulation results demonstrate effectiveness of the proposed scheme.

A performance comparison of measurement matrices in compressive sensing

“…Sensing a wideband spectrum using traditional approaches requires a great deal of time and resources. Fortunately, a few channels are occupied in the radio spectrum, which enables the application of compressive sensing in this area . The design of a measurement matrix that selects the occupied channels to recover the wideband signal with a small recovery error is challenging since the number of measurement is dependent on the sparsity level of the signal, which is not always available.…”

“…Fortunately, a few channels are occupied in the radio spectrum, which enables the application of compressive sensing in this area. 6,[56][57][58][59][60][61][62][63][64][65][66] The design of a measurement matrix that selects the occupied channels to recover the wideband signal with a small recovery error is challenging since the number of measurement is dependent on the sparsity level of the signal, which is not always available. To avoid an estimation of this parameter, the design of the measurement matrix has to be independent of the wideband signal sparsity.…”

Trends in survivable/secure cognitive networks

“…UWB applications include non-cooperative radar imaging, sensor data collection, precision locating, and target tracking. UWB has unique hardware CR issue of antenna design, energy detection [25], overhead protocol analysis [26], distributed network sensing [27], distributed routing analysis [28], waveform design [29], and compressive sensing [30,31,32]. The emergence of UWB requires pragmatic inclusion of threat and situation awareness in spectrum sensing and spectrum data sharing.…”

Recovery Using Backup Channels in Channel-Hopping Cognitive Networks

“…The mechanism is implemented atop the transmission tax-based MAC protocol [17], which is one of the few MAC protocols to integrate regular operation of a piconet (i.e., data transmission and reception, and bandwidth allocation) with sensing activities that aim to ensure smooth piconet operation in an environment with unpredictable primary user activity. A promising approach based on dynamic compressive sensing in a heterogeneous evironment was recently proposed [18].…”