Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding

In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms. Simulations focusing on closely-spaced sources, where they are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052

Random Matrix Theory applied to the Estimation of Collision Multiplicities

This paper presents two techniques in order to estimate the collision multiplicity, i.e., the number of users involved in a collision [1]. This estimation step is a key task in multi-packet reception approaches and in collision resolution techniques. The two techniques are proposed for IEEE 802.11 networks but they can be used in any OFDM-based system. The techniques are based on recent advances in random matrix theory and rely on eigenvalue statistics. Provided that the eigenvalues of the covariance matrix of the observations are above a given threshold, signal eigenvalues can be separated from noise eigenvalues since their respective probability density functions are converging toward two different laws: a Gaussian law for the signal eigenvalues and a Tracy-Widom law for the noise eigenvalues. The first technique has been designed for the white noise case, and the second technique has been designed for the colored noise case. The proposed techniques outperform current estimation techniques in terms of mean square error. Moreover, this paper reveals that, contrary to what is generally assumed in current multi-packet reception techniques, a single observation of the colliding signals is far from being sufficient to perform a reliable estimation of the collision multiplicities