On the sum-capacity of Gaussian MAC with peak constraint

This paper addresses a two-user Gaussian Multiple Access Channel (MAC) under peak constraints at the transmitters. It is shown that generating the code-books of both users according to discrete distributions achieves the largest sum-rate in the network. In other words, sum-capacity achieving input distributions for this channel are discrete with a finite number of mass points. We also demonstrate uniqueness of the input distributions which achieve rates at any of the corner points of the capacity region of the channel.

[1]  Aslan Tchamkerten,et al.  On the discreteness of capacity-achieving distributions , 2004, IEEE Transactions on Information Theory.

[2]  Ibrahim C. Abou-Faycal,et al.  The capacity of discrete-time memoryless Rayleigh-fading channels , 2001, IEEE Trans. Inf. Theory.

[3]  Dudley,et al.  Real Analysis and Probability: Measurability: Borel Isomorphism and Analytic Sets , 2002 .

[4]  Joel G. Smith,et al.  The Information Capacity of Amplitude- and Variance-Constrained Scalar Gaussian Channels , 1971, Inf. Control..

[5]  Erik Ordentlich Maximizing the entropy of a sum of independent bounded random variables , 2006, IEEE Transactions on Information Theory.

[6]  A. Robert Calderbank,et al.  On the capacity of the discrete-time channel with uniform output quantization , 2009, 2009 IEEE International Symposium on Information Theory.

[7]  J. H. van Lint,et al.  Functions of one complex variable II , 1997 .

[8]  Shlomo Shamai,et al.  The capacity of average and peak-power-limited quadrature Gaussian channels , 1995, IEEE Trans. Inf. Theory.

[9]  Upamanyu Madhow,et al.  On the limits of communication with low-precision analog-to-digital conversion at the receiver , 2009, IEEE Transactions on Communications.

[10]  A. Das Capacity-achieving distributions for non-Gaussian additive noise channels , 2000, 2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060).

[11]  Erik Ordentlich,et al.  Maximizing the entropy of a sum of independent random variables , 1997, Proceedings of IEEE International Symposium on Information Theory.