Spectral Sensing at CIP 2010
Surveys and comparisons of spectrum sensing strategiesThe works included in this review will present a slightly different classification to the previous ones (ICASSP, ICC). I will start discussing four works that either survey or compare different spectrum sensing techniques:
In "Overview of Spectrum Sensing for Cognitive Radio", Erik Axell, Geert Leus and Erik G. Larsson present a survey of state-of-the-art algorithms for spectrum sensing. The algorithms discussed range from energy detection to feature detectors exploiting some known structure of the transmitted signal (cyclostationarity properties, known eigenvalue structure of the signal's covariance...), including cooperative detection schemes.
A more involved review on cooperative detection schemes is presented by Luca Bixio, Marina Ottonello, Mirco Raffetto, Carlo S Regazzoni and Claudio Armani in the paper "A Comparison among Cooperative Spectrum Sensing Approaches for Cognitive Radios". This paper compares the three main fusion rules in cooperative spectrum sensing, i.e. OR, AND and optimal, in terms of required processing capabilities at the fusion center and at the secondary terminals, and required control channel capacity.
In "Multiantenna spectrum sensing for Cognitive Radio: overcoming noise uncertainty" Roberto López Valcarce, Gonzalo Vazquez-Vilar and Josep Sala propose a novel multiantenna spectrum sensing paradigm for detection of rank-1 primary signals with uncalibrated multiantenna detectors. However this paper also includes in the discussion most of the previously proposed multiantenna detectors derived under several assumptions for both calibrated and uncalibrated receivers.
In "Performance Comparison for Low Complexity Blind Sensing Techniques in Cognitive Radio Systems" Bassem Zayen, Wael Guibène and Aawatif Hayar classify and compare different low complexity sensing strategies. Specifically, two blind sensing algorithms: the distribution analysis detector and the algebraic detector, which are compared with the energy detector as reference algorithm.
Mixing topicsEstimating the rank of the covariance matrix of a given random process is a recurrent topic in the literature. However it received limited attention in the context of Cognitive Radio systems. In "Estimating the Number of Signals Observed by Multiple Sensors" Marco Chiani and Moe Win show how the exact (non asymptotic) distribution of the eigenvalues of the empirical covariance matrix can be used to find the ML estimate of the actual eigenvalues. However, since this procedure presents a high complexity, they finally propose a rank estimator based on the usual eigenvalue estimate.
Erik Axell and Erik G. Larsson (authors of the overview previously commented) also present "Optimal and Near-Optimal Spectrum Sensing of OFDM Signals in AWGN Channels". In this work they derive the OFDM signal GLR detector for unknown noise and signal powers, which exploits the non-stationary correlation structure of the OFDM signal. Additionally they discuss the optimality of Energy Detection when the noise level is known.
In every conference focused on Cognitive Radio there are some papers on Compressed sensing. In "Compressive sampling based MVDR spectrum sensing" Ying Wang, Ashish Pandharipande and Geert Leus use the minimum variance distortionless response (MVDR) estimator to perform detection from a set of compressed measurements. As opposed to other works they derive the probability distribution of the CS MVDR spectrum estimate, which can be used to determine the detection thresholds.
Finally, Miguel López-Benítez and Ferran Casadevall explore in "On the Spectrum Occupancy Perception of Cognitive Radio Terminals in Realistic Scenarios" the perceived spectrum occupancy in different practical scenarios via empirical measurements. Nice to see that things work in practice.
Cooperative Spectrum SensingTo finish with, just a list of the works presented in the session on cooperative spectrum sensing for cognitive radio networks:
- Sensor Fusion by Two-Layer Conflict Solving. Volker Lohweg, Uwe Mönks. Approach to data fusion which provides a stable conflict scenario handling, extendable to fuzzy classification.
- On Multi-Step Sensor Scheduling via Convex Optimization. Marco Huber. Two efficient multi-step sensor scheduling approaches are proposed in this paper for optimization over long time horizons.
- Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing. Pablo Viñuelas-Peris and Antonio Artés-Rodríguez. Distributed Compressed Sensing (DCS) of sparsely correlated signals.
- A Robust Approach for Optimization of The Measurement Matrix in Compressed Sensing Vahid Abolghasemi, Delaram Jarchi and Saeid Sanei. Optimized matrices can improve the quality of reconstruction and satisfy the conditions for efficient sampling.
- A novel adaptive algorithm for diffusion networks using projections onto hyperslabs. Symeon Chouvardas, Konstantinos Slavakis and Sergios Theodoridis. A new diffusion based algorithm to implement cooperation among neighboring nodes and the corresponding analysis.
- Node Localization and Tracking Using Distance and Acceleration Measurements. Benjamin R. Hamilton, Xiaoli Ma, Robert John Baxley and Brett Walkenhorst. Algorithm to combine acceleration measurements with RSS readings to achieve accurate localization of a distributed sensor network.
While the CIP is just a small workshop compared to the main conferences on signal processing, we have seen that the CIP 2010 proceedings include very trendy papers.