TY - GEN
T1 - Asymptotics of eigenbased collaborative sensing
AU - Bianchi, Pascal
AU - Najim, Jamal
AU - Alfano, G.
AU - Debbah, Mérouane
PY - 2009/12/7
Y1 - 2009/12/7
N2 - In this contribution, we propose a new technique for collaborative sensing based on the analysis of the normalized (by the trace) largest eigenvalues of the sample covariance matrix. Assuming that several base stations are cooperating and without the knowledge of the noise variance, the test is able to determine the presence of mobile users in a network when only few samples are available. Unlike previous heuristic techniques, we show that the test has roots within the Generalized Likelihood Ratio Test and provide an asymptotic random matrix analysis enabling to determine adequate threshold detection values (probability of false alarm). Simulations sustain our theoretical claims.
AB - In this contribution, we propose a new technique for collaborative sensing based on the analysis of the normalized (by the trace) largest eigenvalues of the sample covariance matrix. Assuming that several base stations are cooperating and without the knowledge of the noise variance, the test is able to determine the presence of mobile users in a network when only few samples are available. Unlike previous heuristic techniques, we show that the test has roots within the Generalized Likelihood Ratio Test and provide an asymptotic random matrix analysis enabling to determine adequate threshold detection values (probability of false alarm). Simulations sustain our theoretical claims.
UR - https://www.scopus.com/pages/publications/76249106400
U2 - 10.1109/ITW.2009.5351479
DO - 10.1109/ITW.2009.5351479
M3 - Conference contribution
AN - SCOPUS:76249106400
SN - 9781424449835
T3 - 2009 IEEE Information Theory Workshop, ITW 2009
SP - 515
EP - 519
BT - 2009 IEEE Information Theory Workshop, ITW 2009
T2 - 2009 IEEE Information Theory Workshop, ITW 2009
Y2 - 11 October 2009 through 16 October 2009
ER -