Computing the Channel Capacity of a Communication System Affected by Uncertain Transition Probabilities
We study the problem of computing the capacity of a discrete memoryless channel under uncertainty affecting the channel law matrix, and possibly with a constraint on the average cost of the input distribution. The problem has been formulated in the literature as a max-min problem. We use the robust optimization methodology to convert the max-min problem to a standard convex optimization problem. For small-sized problems, and for many types of uncertainty, such a problem can be solved in principle using interior point methods (IPMs). However, for large-scale problems, IPMs are not practical. Here, we suggest an O(1/T) first-order algorithm based on  which is applied directly to the max-min problem.
|Keywords||Channel capacity, convex optimization, duality|
|Persistent URL||dx.doi.org/10.1109/TIT.2018.2821674, hdl.handle.net/1765/110497|
|Journal||IEEE Transactions on Information Theory|
Postek, K.S, & Ben-Tal, A. (Aharon). (2018). Computing the Channel Capacity of a Communication System Affected by Uncertain Transition Probabilities. IEEE Transactions on Information Theory, 64(10), 6803–6815. doi:10.1109/TIT.2018.2821674