On the separation of correlation-assisted sum capacities of multiple access channels

by   Akshay Seshadri, et al.

The capacity of a channel characterizes the maximum rate at which information can be transmitted through the channel asymptotically faithfully. For a channel with multiple senders and a single receiver, computing its sum capacity is possible in theory, but challenging in practice because of the nonconvex optimization involved. In this work, we study the sum capacity of a family of multiple access channels (MACs) obtained from nonlocal games. For any MAC in this family, we obtain an upper bound on the sum rate that depends only on the properties of the game when allowing assistance from an arbitrary set of correlations between the senders. This approach can be used to prove separations between sum capacities when the senders are allowed to share different sets of correlations, such as classical, quantum or no-signalling correlations. We also construct a specific nonlocal game to show that the usual approach of bounding the sum capacity by relaxing the nonconvex optimization can give arbitrarily loose bounds. Towards a potential solution to this problem, we first prove a Lipschitz-like property for the mutual information. Using a modification of existing algorithms for optimizing Lipschitz-continuous functions, we then show that it is possible to compute the sum capacity of an arbitrary two-sender MAC to a fixed additive precision in quasi-polynomial time. We showcase our method by efficiently computing the sum capacity of a family of two-sender MACs for which one of the input alphabets has size two. Furthermore, we demonstrate with an example that our algorithm may compute the sum capacity to a higher precision than using the convex relaxation.


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