NUMERICAL COMPUTATION OF THE CAPACITY OF CONTINUOUS MEMORYLESS CHANNELS

We extend the Blahut-Arimoto algorithm to continuous memoryless channels by means of sequential Monte Carlo integration in conjunction with steepest descent. As an illustration, we consider the peak power constrained AWGN channel.

[1]  Aleksandar Kavcic,et al.  Markov sources achieve the feedback capacity of finite-state machine channels , 2002, Proceedings IEEE International Symposium on Information Theory,.

[2]  Suguru Arimoto,et al.  An algorithm for computing the capacity of arbitrary discrete memoryless channels , 1972, IEEE Trans. Inf. Theory.

[3]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

[4]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[5]  Larry A. Wasserman,et al.  Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk , 2001, UAI.

[6]  Frank R. Kschischang,et al.  Capacity-achieving probability measure for conditionally Gaussian channels with bounded inputs , 2005, IEEE Transactions on Information Theory.

[7]  Gerald Matz,et al.  Information geometric formulation and interpretation of accelerated Blahut-Arimoto-type algorithms , 2004, Information Theory Workshop.

[8]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[9]  Chein-I Chang,et al.  On calculating the capacity of an infinite-input finite (infinite)-output channel , 1988, IEEE Trans. Inf. Theory.

[10]  Justin Dauwels,et al.  Computation of Information Rates by Particle Methods , 2004, IEEE Transactions on Information Theory.

[11]  Richard E. Blahut,et al.  Computation of channel capacity and rate-distortion functions , 1972, IEEE Trans. Inf. Theory.

[12]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .