Simulation-Based Computation of Information Rates for Channels With Memory

By Dieter-Michael Arnold, Hans-Andrea Loeliger, P. Vontobel, A. Kavcic, W. Zeng. IEEE Transactions on Information Theory, 2006

544 citations recorded by the source corpus.

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Smart meter privacy using a rechargeable battery: Minimizing the rate of information leakage (2011, methodology): “For continuous loads and batteries, upper and lower bounds on the information leakage rate can be obtained [10].”
Energy Harvesting Wireless Communications: A Review of Recent Advances (2015, background): “Finally, Section X provides the conclusions for the article and lists some future directions in the broad area of energy harvesting communications.”
Quantized Precoding for Massive MU-MIMO (2016, methodology): “One can, however, lower-bound the achievable rate using the so-called “auxiliary-channel lower bound” [45, p. 3503], which gives the rates achievable with a mismatched decoder (see [46, ch. 1 ] for a recent review on the subject).”
Rate Adaptation and Reach Increase by Probabilistically Shaped 64-QAM: An Experimental Demonstration (2016, background): “The concept of mismatched decoding [22], [23] is a powerful tool that allows to derive achievable rates and decoding rules in situations when the exact channel law is not known [24]–[26].”
Achievable Information Rate in Nonlinear WDM Fiber-Optic Systems With Arbitrary Modulation Formats and Dispersion Maps (2013, methodology): “The mismatched decoding approach provides also a simple but accurate method for the numerical estimation of the AIR for a very general class of channels and modulations, without requiring an explicit knowledge of the actual channel statistics [21].”