On the capacity of Bernoulli-Gaussian impulsive noise channels in Rayleigh fading

In this paper, we investigate the channel capacity of an additive Bernoulli-Gaussian (BG) impulsive noise channel in Rayleigh fading via lower and upper bounds. To this end, we first show that the differential entropy of the BG impulse noise can be established in closed-form using Gaussian hypergeometric function 2F1 (1, 1; .; .). This closed-form expression allows us to derive a lower bound on the capacity limit obtained by a Gaussian input using the Gauss-Hermite quadrature formula. We also derive in closed-form two upper bounds on the channel capacity. The first upper bound is obtained under the assumption of full knowledge of noise state, while the second upper bound is developed using a Gaussian distributed output. At high power regions, the lower bound achieved by Gaussian inputs and the upper bound generated by Gaussian inputs are indistinguishable. These two bounds can therefore be used as an accurate estimation for the channel capacity. When the channel input power is small compared to the power of the impulsive noise component, the lower bound obtained by using a Gaussian input and the upper bound under the perfect knowledge of impulse noise state are almost identical, which are useful to predict the capacity. The establishment of the lower bound and the two upper bounds in closed-form helps us to confirm the near-optimality of the Gaussian input in a wide range of input power levels over BG impulsive noise channels in Rayleigh fading.

[1]  Gianluigi Ferrari,et al.  Fundamental performance limits of communications systems impaired by impulse noise , 2009, IEEE Transactions on Communications.

[2]  Han Vinck,et al.  OFDM Transmission Corrupted by Impulsive Noise , 2006 .

[3]  C. Tepedelenlioğlu,et al.  Space-time coding over fading channels with alpha-stable noise , 2011, 2011 IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications.

[4]  John M. Cioffi,et al.  Reduced-delay protection of DSL systems against nonstationary disturbances , 2004, IEEE Transactions on Communications.

[5]  S. Kassam Signal Detection in Non-Gaussian Noise , 1987 .

[6]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[7]  D. Middleton,et al.  Channel Modeling and Threshold Signal Processing in Underwater Acoustics: An Analytical Overview , 1987 .

[8]  Junghoon Lee,et al.  Space-Time Coding Over Fading Channels With Stable Noise , 2011, IEEE Transactions on Vehicular Technology.

[9]  G. D. Byrne,et al.  Gaussian Quadratures for the Integrals ∞ 0 exp(-x 2 )f(x)dx and b 0 exp(-x 2 )f(x)dx , 1969 .

[10]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Trans. Wirel. Commun..

[11]  David Middleton,et al.  Statistical-Physical Models of Electromagnetic Interference , 1977, IEEE Transactions on Electromagnetic Compatibility.

[12]  Tommy Öberg,et al.  Robust detection in digital communications , 1995, IEEE Trans. Commun..

[13]  John Newbury,et al.  Power line communications : theory and applications for narrowband and broadband communications over power lines , 2010 .

[14]  Monisha Ghosh,et al.  Analysis of the effect of impulse noise on multicarrier and single carrier QAM systems , 1996, IEEE Trans. Commun..

[15]  Lutz H.-J. Lampe,et al.  Performance Analysis for BICM Transmission over Gaussian Mixture Noise Fading Channels , 2010, IEEE Transactions on Communications.

[16]  Wai Ho Mow,et al.  Robust joint interference detection and decoding for OFDM-based cognitive radio systems with unknown interference , 2007, IEEE Journal on Selected Areas in Communications.

[17]  Kia Wiklundh,et al.  Channel capacity of Middleton's class A interference channel , 2009 .

[18]  Joseph Lipka,et al.  A Table of Integrals , 2010 .

[19]  Tho Le-Ngoc,et al.  On optimal input distribution and capacity limit of Bernoulli-Gaussian impulsive noise channels , 2012, 2012 IEEE International Conference on Communications (ICC).