Sensing, sensing and more sensing at ICASSP

After enjoying a conference reception colored by an unconveniently loud experimental jazz group, the actual ICASSP begins. Here I will give my impressions on some of the papers cited in the previous post. Two of today's sessions were about Spectrum Sensing for Cognitive Radio, and the works presented covered all the current trends.

Sensing paradigms

The paper SEMI-BLIND LOCALLY OPTIMUM DETECTION FOR SPECTRUM SENSING IN COGNITIVE RADIO by Marco Cardenas-Juarez et al. assumes the secondary node slot-synchronized (strong assumption) to a primary system transmitting a training sequence. Then this work proposes a detector based on a mixed matched filter/energy detection strategy.

On A PERFORMANCE STUDY OF NOVEL SEQUENTIAL ENERGY DETECTION METHODS FOR SPECTRUM SENSING by Nikhil Kundargi et al. an adaptive energy detector is proposed. While the classical energy detector compares the energy received during a given time with a threshold, the proposed sequential energy detection compares the likelihood ratio of the signal up to a time instant with two thresholds, deciding hypothesis H0, H1 or, if the likelihood falls between the two threshold values undecided. In this case the operation is repeated with a longer sample of the signal.

Related to cyclostationarity is the work in AM-SIGNAL DETECTION IN COGNITIVE RADIOS USING FIRST-ORDER CYCLOSTATIONARITY by Yi Zhou et al. As the title describes they propose a cyclostationarity based detector for AM signals, what is an interesting theoretical exercise but it is in my opinion not very practical. They assume only rough information on the signal bandwidth and carrier frequency, but this implies that a search of the right cyclic frequency has to be performed.
Similarly, SPECTRAL COVARIANCE FOR SPECTRUM SENSING, WITH APPLICATION TO IEEE 802.22 by Jaeweon Kim et al. uses existing spectral correlation to propose a detector robust to noise uncertainty. However, like other cyclostationarity based approaches offers poor performance in OFDM signal detection.

An interesting idea is presented in SPECTRUM SENSING OF ORTHOGONAL SPACE-TIME BLOCK CODED SIGNALS WITH MULTIPLE RECEIVE ANTENNAS by Erik Axell et al. In this paper the authors use the aditional structure of space-time coded signals to increase detection performace. In fact I had tried this idea recetly with Alamouti coded signals observing no detection gain... my naive result is confirmed by this work since when noise power is known OSTBC based detection offers no gains with respect to energy detection. However for unknown noise level (here was the key point I had missed) there exists a significant gain.

MULTIANTENNA SPECTRUM SENSING: DETECTION OF SPATIAL CORRELATION AMONG TIME-SERIES WITH UNKNOWN SPECTRA by David Ramirez et al. asumes a multiantenna cognitive node. Then it uses the spatial uncorreldaness of the noise process to derive a detector robust to noise level (and temporal correlation) uncertainty.

Multichannel spectrum sensing

In TWO-STAGE SPECTRUM SENSING FOR COGNITIVE RADIOS Sina Maleki et al. propose the use of a two level detector with a first (energy based) coarse detection phase followed by a (cyclostationarity based) fine detection stage. The most interesting part is that when the two stages thresholds are optimized the global detector takes the advantages of the two detection schemes. Unfortunately to optimize the detection thresholds working SNR must be known.

With the almost equal title TWO-STAGE SPECTRUM DETECTION IN COGNITIVE RADIO NETWORKS Siavash Fazeli-Dehkordy et al. present a different idea. The authors propose to use energy detection in both detection stages in order to reduce the average empty channel search time. The first stage is short and defides if a channel is candidate for a (longer) second test.

Similarly, ADAPTIVE SPECTRUM SENSING FOR AGILE COGNITIVE RADIOS by Ali Tajer et al. proposes an adaptive sensing scheme for the band of interest based on discarding first the clearly ocuppied channels in order to concentrate sensing resources in the remaining doubtful channels. As oposed to the previous paper, it estimates the set of empty channels and not only one empty channel.

Seung-Jun Kim et al. pose the problem of determining the best sensing strategy for a band with multiple channels, taking into account that when sensing is performed not useful transmission is performed by the secondary system. This paper entitled SEQUENTIAL COOPERATIVE SENSING FOR MULTI-CHANNEL COGNITIVE RADIOS was presented by G. Giannakis in a heavy lecture.

The paper WIDEBAND SPECTRAL ESTIMATION FROM COMPRESSED MEASUREMENTS EXPLOITING SPECTRAL A PRIORI INFORMATION IN COGNITIVE RADIO SYSTEMS by G Vazquez-Vilar (that's me) et al. poses the estimation (and as a byproduct detection) of multiple primary signals from compressed meassurements. As opposed to other compressed sampling (CS) reconstruction schemes the paper starts with the MAP estimator derivation and incidentally produces a formulation similar to other CS methods. A (quite trivial) greedy approach is used to deal with the norm 0 term.

The work presented in COMPRESSIVE DETECTION FOR WIDE-BAND SPECTRUM SENSING by Veria Havary-Nassab et al. proposes a wideband detector of spectral holes. To this end wideband signal is fed into a set of random overlapping filters, then the energies of the filter outputs are used as compressed measurements to reconstruct the signal energy in each channel. The white spaces are then detected by comparing the energy vector with a given threshold.

Collaborative spectrum sensing

The paper COLLABORATIVE SPECTRUM SENSING FROM SPARSE OBSERVATIONS USING MATRIX COMPLETION FOR COGNITIVE RADIO NETWORKS by Jia (Jasmine) Meng et al. assumes multiple nodes taking a linear combination of the sparse signals to estimate. To guarantee the sparsity of the solution the estimation is performed based on a nuclear norm minimization algorithm robust to transmission loss of measurements matrix entries.

To finish with this long post a comment on two last papers. While BANDWIDTH EFFICIENT COMBINATION FOR COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS by Xiangwei Zhou et al. studies the possibility of combining likelihoods on the air to increase the bandwidth efficiency of the transmission of sensing data obtained by multiple cognitive nodes; the work in DIVERSITY-BASED SPECTRUM SENSING POLICY FOR DETECTING PRIMARY SIGNALS OVER MULTIPLE FREQUENCY BANDS by Jan Oksanen et al. proposes a cooperative spectrum sensing scheme that facilitates mitigating the effects of shadowing and fading through spatial diversity.