Multistream Distributed Cophasing

In this paper, we develop a distributed cophasing (DCP) technique for physical layer fusion of multiple data streams in a wireless sensor network with multiple destination nodes (DNs). The DNs can either be connected to a fusion center (referred to as centralized data processing; CDP) or process data independently and communicate with each other via a rate-limited link (referred to as distributed data processing; DDP). In the first stage of this two-stage cophasing scheme, sensors estimate the channel to the DNs using pilot symbols transmitted by the latter; following which they simultaneously transmit multiple streams of data symbols by prerotating them according to the estimated channel phases to the different DNs. The achievable rates for both CDP and DDP are derived to quantify the gains obtainable by the multistream DCP. In order to aid data detection at the receiver, we propose a least-squares-based iterative algorithm for blind channel estimation in CDP–DCP. Following this, we develop a message passing based blind channel estimation algorithm for DDP–DCP. It is found using Monte Carlo simulations that for the CDP system, the proposed blind channel estimation algorithm achieves a probability of error performance very close to that with perfect CSI at the DNs, while using only a moderate number of unknown data symbols for channel estimation. We also derive approximate expressions for the error probability performance of the proposed system for both CDP and DDP and validate their accuracy using Monte Carlo simulations.