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Distributed space-time coding is a cooperative transmission scheme for wireless relay networks. With this scheme, antennas of the distributive relays work as transmit antennas of the sender and generate a space-time code at the receiver. It achieves the maximum diversity. Although the scheme needs no channel information at relays, it does require full channel information, both the channels from the transmitter to relays and the channels from relays to the receiver, at the receiver. In this paper, we propose a differential transmission scheme, which requires channel information at neither relays nor the receiver, for wireless relay networks. As distributed space-time coding can be seen as the counterpart of space-time coding in the network setting, this scheme is the counterpart of differential space-time coding. Compared to coherent distributed space-time coding, the differential scheme is 3dB worse. In addition, we show that Alamouti, square real orthogonal, and Sp(2) codes can be used differentially in networks with corresponding numbers of relays. We also propose distributed differential space-time codes that work for networks with any number of relays using circulant matrices. Index Terms--Wireless relay networks, cooperative diversity, differential transmission, space-time block coding.

In this paper, channel training and coherent decodings under channel estimation error are investigated for relay networks with one single-antenna transmitter, R single-antenna relays, and one R-antenna receiver. A two-stage training scheme is proposed to estimate both the relay-receiver and the transmitter-relay channels at the receiver, which are commonly required in amplify-and-forward (AF) relay networks. We use distributed space-time coding (DSTC) for data transmission and investigate the effect of channel estimation errors on network performance. Two coherent decodings are considered: mismatched decoding in which channel estimations are treated as if perfect, and matched decoding in which estimation error is taken into consideration. We show that for full diversity, with mismatched decoding, at least 3R symbol intervals are required for training; while with matched decoding, R+2 symbol intervals for training are enough. The complexities of the decoding schemes are investigated. To achieve a balance between performance and complexity, an adaptive decoding scheme is proposed. Simulated error rates are shown to justify the analytical results. [ABSTRACT FROM PUBLISHER]