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In this paper, we present a comprehensive study of the monotonicity and log-concavity of the generalized Marcum and Nuttall Q -functions. More precisely, a simple probabilistic method is first given to prove the monotonicity of these two functions. Then, the log-concavity of the generalized Marcum Q -function and its deformations is established with respect to each of the three parameters. Since the Nuttall Q -function has similar probabilistic interpretations as the generalized Marcum Q -function, we deduce the log- concavity of the Nuttall Q -function. By exploiting the log-concavity of these two functions, we propose new tight lower and upper bounds for the generalized Marcum and Nuttall Q -functions. Our proposed bounds are much tighter than the existing bounds in the literature in most of the cases. The relative errors of our proposed bounds converge to 0 as 6 → ∞. The numerical results show that the absolute relative errors of the proposed bounds are less than 5% in most of the cases. The proposed bounds can be effectively applied to the outage probability analysis of interference-limited systems such as cognitive radio and wireless sensor network, in the study of error performance of various wireless communication systems operating over fading channels and extracting the log-likelihood ratio for differential phase-shift keying (DPSK) signals. [ABSTRACT FROM AUTHOR]

In this paper, we study the generalized Marcum Q-function Qv(a,b), where a,v > 0 and b ≥ 0. Our aim is to extend the results of Corazza and Ferrari (IEEE Trans. Inf. Theory, vol. 48, pp. 3003-3008, 2002) to the generalized Marcum Q-function in order to deduce some new tight lower and upper bounds. The key tools in our proofs are some monotonicity properties of certain functions involving the modified Bessel function of the first kind and some classical inequalities, i.e., the Cauchy-Buniakowski-Schwarz and Chebyshev integral inequalities. These bounds are shown to be very tight for large b, i.e., the relative errors of our bounds converge to zero as b increases. Both theoretical analysis and numerical results are provided to show the tightness of our bounds. [ABSTRACT FROM AUTHOR]

In this correspondence, we point out two typographical errors in Chai and Tjhung's paper, and we offer the correct formula of the unified Laguerre polynomial-series-based cumulative distribution function (cdf) for small-scale fading distributions. A Laguerre polynomial-series-based cdf formula for noncentral chi-square distribution is also provided as a special case of our unified cdf result. [ABSTRACT FROM PUBLISHER]