Non-coherent Unitary Space-time Modulation And Demodulation Algorithm On Grassmann Manifold

Multiple-input Multiple-output(MIMO) modulation technique is the key techniquefor4G and beyond, since it can provide both extra spatial diversity gain and multiplexinggain to improve spectral efficiency and capacity. In order to adapt to the fast-fadingenvironment in high speed movement，Marzetta and Hochwald proposed the non-coherentunitary space-time communication system，in which neither transmitter nor receiver needsChannel State Information（CSI）for modulation and demodulation. A result of it is thatbandwidth for pilots are saved. Zheng introduced the Grassmann manifold intonon-coherent unitary space-time communications, based on the fact that each point in theunitary constellation corresponds to a point in Grassmann manifold.This thesis, supported by National Natural Science Foundation of China “TheResearch of Key Technology in Noncoherent MIMO Communication System”, isfocused on the modulation and demodulation algorithm of unitary space-time code basedon Grassmann manifold, we propose an antipodal unitary space-time constellation and asimplified demodulation algorithm for antipodal unitary space-time constellation.1) We propose the unitary space-time matrix framework which is based onGrassmann manifold. Under the framework constraints, we define the chordal Frobeniusnorm as the distance criterion. Then we search for the constellation points by changing theamplitude and phase of each element in the unitary matrix with suitable steps, while theminimum chordal Frobenius norm of the constellation points must be longer than thethreshold, and it’s antipodal point belongs to the constellation. Numerical simulationsindicate that this new constellation performances better than the present codes.2) In this part, we analyze the antipodal constellations of the noncoherent unitaryspace-time modulation, and prove the identity relation between the antipodal points.Whereafter, we propose the antipodal demodulation algorithm base on themaxim-likelihood demodulation. Numerical simulations indicate that in spite of thecomputation complexity of our simplified demodulation is half of maxim-likelihooddemodulation, our performance remains the same. Therefore, it is the optimaldemodulation algorithm.