Ben Issaid, Chaouki; Rached, Nadhir B.; Kammoun, Abla; Alouini, Mohamed-Slim; Tempone, Raul(2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Institute of Electrical and Electronics Engineers (IEEE), 2017-05-12)[Conference Paper]
The Gamma-Gamma distribution has recently emerged in a number of applications ranging from modeling scattering and reverbation in sonar and radar systems to modeling atmospheric turbulence in wireless optical channels. In this respect, assessing the outage probability achieved by some diversity techniques over this kind of channels is of major practical importance. In many circumstances, this is intimately related to the difficult question of analyzing the statistics of a sum of Gamma-Gamma random variables. Answering this question is not a simple matter. This is essentially because outage probabilities encountered in practice are often very small, and hence the use of classical Monte Carlo methods is not a reasonable choice. This lies behind the main motivation of the present work. In particular, this paper proposes a new approach to estimate the left tail of the sum of independent and identically distributed Gamma-Gamma variates. More specifically, we propose a mean-shift importance sampling scheme that efficiently evaluates the outage probability of L-branch maximum ratio combining diversity receivers over Gamma-Gamma fading channels. The proposed estimator satisfies the well-known bounded relative error criterion. We show the accuracy and the efficiency of our approach compared to naive Monte Carlo via some selected numerical simulations.
Chaaban, Anas; Rezki, Zouheir; Alomair, Basel; Alouini, Mohamed-Slim(2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Institute of Electrical and Electronics Engineers (IEEE), 2017-05-12)[Conference Paper]
The N-antenna MISO Gaussian wiretap channel with imperfect channel-state information at the transmitter (CSIT) is studied in terms of secrecy rate scaling versus the signal-to-noise ratio (SNR) and N. Two schemes are considered, beamforming (BF) and artificial noise injection (AN). It is shown that if the CSIT error is independent of SNR, then both schemes do not achieve scaling versus SNR. However, if this error vanishes as SNR increases, then AN achieves the optimal scaling versus SNR, contrary to BF. Scaling can be achieved in BF by increasing N. In fact, BF achieves the optimal scaling versus N. In the AN scheme however, injecting noise in multiple direction deteriorates its scaling versus N. Nevertheless, AN can achieve the optimal scaling if noise is sent in only one direction. This leads to better performance than BF if the CSIT error is smaller than a threshold which is also derived.
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