Cooperation via Trellis Pruning

This paper introduces a new approach to cooperative communications. Consider two partner nodes that cooperate to convey their data to a common destination, each transmitting not only its own "local" data but also acting as a relay for its partner, thereby effecting spatial diversity. In the proposed scheme, each partner multiplexes local data with relayed data and convolutionally encodes the resulting bitstream prior to transmission; the other partner then uses its knowledge of the relayed data to prune edges from the code trellis, thereby decoding at a lower (and more robust) rate. The destination observes two copies of each source's data - one transmitted as local information, the other transmitted as relayed information - and employs iterative decoding to recover the same. The resulting performance is superior to that of cooperation schemes based on time sharing - for example, a gain of 4.1 dB is observed at a frame-error rate of 10
^-3
with memory M = 3 codes - as well as schemes employing the newer algebraic superposition approach of Xiao et al., with trellis pruning enjoying an advantage of 0.55 dB to 1.04 dB over algebraic superposition, depending on M. The new approach requires some additional decoding complexity at each partner node, but the decoding complexity at the destination node is kept constant.