Computation over multiple-access channels
The problem of reliably reconstructing a function of sources over a multiple-access channel (MAC) is considered. It is shown that there is no source-channel separation theorem even when the individual sources are independent. Joint source-channel strategies are developed that are optimal when the structure of the channel probability transition matrix and the function are appropriately matched. Even when the channel and function are mismatched, these computation codes -often outperform separation-based strategies. Achievable distortions are given for the distributed refinement of the sum of Gaussian sources over a Gaussian multiple-access channel with a joint source-channel lattice code. Finally, computation codes are used to determine the multicast capacity of finite-field multiple-access networks, thus linking them to network coding.
Keywords: distributed computation ; joint source-channel coding ; lattice codes ; linear codes ; multiple-access channel (MAC) ; network coding ; separation theorem ; Correlated Sources ; Side Information ; Awgn Channel ; Networks ; Capacity ; Codes ; Multicast
Record created on 2011-10-17, modified on 2016-08-09