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Abstract-In this paper, a new direction of arrival (DOA) estimation approach is addressed for the case of more sources than physical receiving antennas by considering a novel nonuniform array design. The new design utilizes the concept of minimum sparse rulers which are rulers having incomplete marks. The differences between marks in a sparse ruler cover all lags of the autocorrelation. In array processing, this set of differences can be used as a basis to construct a virtual uniform linear array having a higher number of antennas than the actual linear array. In order to attain the required rank condition of the observation matrix, the most recent spatial smoothing method is used. The MUSIC algorithm can then be applied leading to the desired high resolution result. It is also possible to compromise the resolution for a lower complexity level by exploiting the least-squares approach to generate the angular spectrum.Index Terms-direction of arrival (DOA) estimation, nonuniform array, sparse rulers, MUSIC algorithm, spatial smoothing

Spectrum Sensing is an important functionality of Cognitive Radio (CR). Accuracy and speed of estimation are the key indicators to select the appropriate spectrum sensing technique. Conventional spectrum estimation techniques which are based on Short Time Fourier Transform (STFT) suffer from familiar problems such as low frequency resolution, high variance of estimated power spectrum and high side lobes/leakages. Methods such as Multi Taper Spectrum Estimation successfully alleviate these infarctions but exact a high price in terms of complexity. On these accounts, it appears that the filter bank spectrum estimation formulated by F. Boroujeny and wavelet based spectrum estimates are the most promising and pragmatic approaches for CR applications. This article surveys and appraises available literature on various spectrum sensing techniques and discusses spectrum sensing as a key element of CR system design.978-1-4244-4583-7/09/$25.00

Abstract-Power spectrum blind sampling (PSBS) consists of a sampling procedure and a reconstruction method that is capable of perfectly reconstructing the unknown power spectrum of a signal from the obtained samples. In this letter, we propose a solution to the PSBS problem based on a periodic sampling procedure and a simple least squares (LS) reconstruction method. For this PSBS technique, we derive the lowest possible average sampling rate, which is much lower than the Nyquist rate of the signal. Note the difference with spectrum blind sampling (SBS) where the goal is to perfectly reconstruct the spectrum and not the power spectrum of the signal, in which case sub-Nyquist rate sampling is only possible if the spectrum is sparse. In the current work, we can perform sub-Nyquist rate sampling without making any constraints on the power spectrum, because we try to reconstruct the power spectrum and not the spectrum. In many applications, such as spectrum sensing for cognitive radio, the power spectrum is of interest and estimating the spectrum is basically overkill.Index Terms-Cognitive radio, , compressive sampling, power spectrum estimation.