Implementation of a distributed compressed sensing algorithm on USRP2 platforms

info:eu-repo/semantics/publishe

Sensitivity of spectrum sensing techniques to RF impairments

info:eu-repo/semantics/nonPublishe

Multiband spectrum sensing for cognitive radios based on distributed compressed measurements

A wideband spectrum sensing method for cognitive radios is presented which is based on compressed measurements. The proposed detector does not require signal reconstruction from the compressed measurements. A fusion centre collects the measurements from different sensing nodes and then makes a sensing decision based on a simplified maximum likelihood criterion which does not require prior signal information. This results in an efficient and low complexity spectrum detector especially for dynamic spectrum occupancy scenarios. The performance of the proposed detector is exhibited by means of numerical simulations for probability of erroneous detection and receiver operating characteristic curves.info:eu-repo/semantics/publishe

Carrier detection using a fourth-order cyclostationary detector

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Spectrum sensing based on the detection of fourth-order cyclic features

info:eu-repo/semantics/publishe

Implementation of a distributed compressed sensing algorithm on USRP2 platforms

info:eu-repo/semantics/nonPublishe

Higher-Order Cyclostationarity Detection for Spectrum Sensing

<p/> <p>Recent years have shown a growing interest in the concept of Cognitive Radios (CRs), able to access portions of the electromagnetic spectrum in an opportunistic operating way. Such systems require efficient detectors able to work in low Signal-to-Noise Ratio (SNR) environments, with little or no information about the signals they are trying to detect. Energy detectors are widely used to perform such blind detection tasks, but quickly reach the so-called SNR wall below which detection becomes impossible Tandra (2005). Cyclostationarity detectors are an interesting alternative to energy detectors, as they exploit hidden periodicities present in man-made signals, but absent in noise. Such detectors use quadratic transformations of the signals to extract the hidden sine-waves. While most of the literature focuses on the second-order transformations of the signals, we investigate the potential of higher-order transformations of the signals. Using the theory of Higher-Order Cyclostationarity (HOCS), we derive a fourth-order detector that performs similarly to the second-order ones to detect linearly modulated signals, at SNR around 0 dB, which may be used if the signals of interest do not exhibit second-order cyclostationarity. More generally this paper reviews the relevant aspects of the cyclostationary and HOCS theory, and shows their potential for spectrum sensing.</p

Multiband maximum likelihood signal detection based on compressive measurements

Cognitive radios impose challenges on the design of efficient signal detectors, including wide bandwidth sensing and large dynamic range support. The recently considered compressed sensing theory helps in relaxing the constraints on the design of the analog front-end. The maximum likelihood method introduced here is computationally simple since it does not require a signal reconstruction, unlike most methods introduced in the current literature. Moreover, the metric is optimum, works for any modulation scheme and is independent of the emitted signal knowledge and the number of occupied bands. The results are supported with Matlab simulations, a statistical study is performed and the probabilities of misdetection and false alarm are plotted for different scenarios, proving the efficiency of the estimator in a range of plausible SNRs and subsampling factors.info:eu-repo/semantics/publishe

Sensitivity of spectrum sensing techniques to RF impairments

Cognitive radios are devices capable of sensing a large range of frequencies in order to detect the presence of primary networks and reuse their bands when they are not occupied. Due to the large spectrum to be sensed and the high power signal dynamics, low-cost implementations of the analog front-ends leads to imperfections. Two of them are studied in this paper: IQ imbalance and sampling clock offset (SCO). Based on a mathematical system model, we study analytically the impact of the two imperfections on the sensing performance of the energy detector and of the cyclostationarity detector. We show that the IQ imbalance does not impact the performance of the two detectors, and that the SCO only impacts significantly the performance of the cyclostationarity detector. © 2010 IEEE.10.1109/VETECS.2010.5493999SCOPUS: cp.p77954900576info:eu-repo/semantics/publishe

Distributed compressed sampling architecture for maximum likelihood signal detection

Cognitive radios are a new technology introduced to resolve the spectrum scarcity problemby superimposing new services in the already allocated bands under a non-interference constraint.It has been recently demonstrated that the challenging implementation of the signal detectors canbe facilitated by using the theory of compressive sampling. In this paper, we consider a distributednetwork of secondary nodes that cooperate to detect the primary signals. Each secondary nodesamples the signal periodically at a rate much smaller than the Nyquist rate. The delays inherentto the propagation channel are used to implement a periodic non-uniform sampling detector whenthe secondary nodes combine their observations. We demonstrate that the proposed detector canefficiently detect the primary user signal, even under fading channels.info:eu-repo/semantics/publishe