In this paper, we consider the problem of spectrum sensing for advanced television systems committee (ATSC) digital television (DTV) signal detection. To exploit the cyclostationarity of the ATSC DTV signals, we employ spectral correlation density (SCD) as the decision statistic and propose an optimal detection algorithm. The major difficulty is in obtaining the probability distribution functions of the SCD. To overcome the difficulty, we probabilistically model the pilot frequency location and employ Gaussian approximation for the SCD distribution. Then, we obtain a practically implementable detection algorithm that outperforms the industry leading systems by 2-3 dB. We also propose various techniques that greatly reduce the system complexity with performance degradation by only a few tenths of decibels. Finally, we show how robust the system is to the estimation errors of the noise power spectral density level and the probability distribution of the pilot frequency location.

Recently, various subspace‐based direction of arrival (DOA) estimation algorithms have been proposed that do not require the computationally expensive eigen‐decomposition or singular value decomposition process. In particular, a recently proposed algorithm based on the cross‐correlation matrix between data vectors collected by the component uniform linear arrays (ULAs) of an L‐shaped array is reported as providing the best performance with the least complexity among such algorithms. Proposed is an algorithm based on the autocorrelation matrix of the data vector collected by a single ULA and methods are proposed to further improve performance with a minimal increased in complexity.

Summary form only given. The performance of wideband communication systems is heavily dependent on the characteristics of the channel. A set of second order statistics are defined for the characterization of the diversity of multipath fading channels. The frequency mean square correlation (FMSC) characterizes the frequency selectivity of a channel, while the time mean square correlation (TMSC) represents the time selectivity. Frequency-time mean square correlation is defined to characterize both the frequency and the time selectivities of a channel. For a wide-sense stationary uncorrelated scattering (WSSUS) channel, these parameters can be obtained from the scattering function. We show that these parameters are very closely related to the system performance. Because of this close relationship with the system performance, these parameters can be used in the system design. In this paper we give several examples of the usage of these parameters in the system design.

We propose a low complexity subspace-based direction-of-arrival (DOA) estimation algorithm employing a direct signal space construction method (DSPCM) by subsampling the autocorrelation matrix of a uniform linear array (ULA). Three major contributions of this paper are as follows. First of all, we introduce the method of autocorrelation matrix subsampling which enables us to employ a low complexity algorithm based on a ULA without computationally complex eigenvalue decomposition or singular-value decomposition. Secondly, we introduce a signal vector separation method to improve the distinguishability among signal vectors, which can greatly improve the performance, particularly, in low signal-to-noise ratio (SNR) regime. Thirdly, we provide a root finding (RF) method in addition to a spectral search (SS) method as the angle finding scheme. Through simulations, we illustrate that the performance of the proposed scheme is reasonably close to computationally much more expensive MUSIC- (MUltiple SIgnal Classification-) based algorithms. Finally, we illustrate that the computational complexity of the proposed scheme is reduced, in comparison with those of MUSIC-based schemes, by a factor ofO(N2/K), whereKis the number of sources andNis the number of antenna elements.