113 research outputs found
Spectrum sensing by cognitive radios at very low SNR
Spectrum sensing is one of the enabling functionalities for cognitive radio
(CR) systems to operate in the spectrum white space. To protect the primary
incumbent users from interference, the CR is required to detect incumbent
signals at very low signal-to-noise ratio (SNR). In this paper, we present a
spectrum sensing technique based on correlating spectra for detection of
television (TV) broadcasting signals. The basic strategy is to correlate the
periodogram of the received signal with the a priori known spectral features of
the primary signal. We show that according to the Neyman-Pearson criterion,
this spectral correlation-based sensing technique is asymptotically optimal at
very low SNR and with a large sensing time. From the system design perspective,
we analyze the effect of the spectral features on the spectrum sensing
performance. Through the optimization analysis, we obtain useful insights on
how to choose effective spectral features to achieve reliable sensing.
Simulation results show that the proposed sensing technique can reliably detect
analog and digital TV signals at SNR as low as -20 dB.Comment: IEEE Global Communications Conference 200
Hard Decision Cooperative Spectrum Sensing Based on Estimating the Noise Uncertainty Factor
Spectrum Sensing (SS) is one of the most challenging issues in Cognitive
Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed to enhance
the detection reliability of a Primary User (PU) in fading environments. In
this paper, we propose a hard decision based CSS algorithm using energy
detection with taking into account the noise uncertainty effect. In the
proposed algorithm, two dynamic thresholds are toggled based on predicting the
current PU activity, which can be successfully expected using a simple
successive averaging process with time. Also, their values are evaluated using
an estimated value of the noise uncertainty factor. These dynamic thresholds
are used to compensate the noise uncertainty effect and increase (decrease) the
probability of detection (false alarm), respectively. Theoretical analysis is
performed on the proposed algorithm to deduce its enhanced false alarm and
detection probabilities compared to the conventional hard decision CSS.
Moreover, simulation analysis is used to confirm the theoretical claims and
prove the high performance of the proposed scheme compared to the conventional
CSS using different fusion rules.Comment: 5 pages, 4 figures, IEEE International Conference on Computer
Engineering and Systems (ICCES 2015). arXiv admin note: text overlap with
arXiv:1505.0558
Sensing Throughput Tradeoff for Cognitive Radio Networks with Noise Variance Uncertainty
This paper proposes novel spectrum sensing algorithm, and examines the
sensing throughput tradeoff for cognitive radio (CR) networks under noise
variance uncertainty. It is assumed that there are one white sub-band, and one
target sub-band which is either white or non-white. Under this assumption,
first we propose a novel generalized energy detector (GED) for examining the
target sub-band by exploiting the noise information of the white sub-band,
then, we study the tradeoff between the sensing time and achievable throughput
of the CR network. To study this tradeoff, we consider the sensing time
optimization for maximizing the throughput of the CR network while
appropriately protecting the primary network. The sensing time is optimized by
utilizing the derived detection and false alarm probabilities of the GED. The
proposed GED does not suffer from signal to noise ratio (SNR) wall (i.e.,
robust against noise variance uncertainty) and outperforms the existing signal
detectors. Moreover, the relationship between the proposed GED and conventional
energy detector (CED) is quantified analytically. We show that the optimal
sensing times with perfect and imperfect noise variances are not the same. In
particular, when the frame duration is 2s, and SNR is -20dB, and each of the
bandwidths of the white and target sub-bands is 6MHz, the optimal sensing times
are 28.5ms and 50.6ms with perfect and imperfect noise variances, respectively.Comment: Accepted in CROWNCOM, June 2014, Oulu, Finlan
Throughput analysis for cognitive radio networks with multiple primary users and imperfect spectrum sensing
In cognitive radio networks, the licensed frequency bands of the primary users (PUs) are available to the secondary user (SU) provided that they do not cause significant interference to the PUs. In this study, the authors analysed the normalised throughput of the SU with multiple PUs coexisting under any frequency division multiple access communication protocol. The authors consider a cognitive radio transmission where the frame structure consists of sensing and data transmission slots. In order to achieve the maximum normalised throughput of the SU and control the interference level to the legal PUs, the optimal frame length of the SU is found via simulation. In this context, a new analytical formula has been expressed for the achievable normalised throughput of SU with multiple PUs under prefect and imperfect spectrum sensing scenarios. Moreover, the impact of imperfect sensing, variable frame length of SU and the variable PU traffic loads, on the normalised throughput has been critically investigated. It has been shown that the analytical and simulation results are in perfect agreement. The authors analytical results are much useful to determine how to select the frame duration length subject to the parameters of cognitive radio network, such as network traffic load, achievable sensing accuracy and number of coexisting PUs
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