
A subspace projection to improve channel estimation in massive multi-antenna systems is proposed and analyzed. Together with power-controlled hand-off, it can mitigate the pilot contamination problem without the need for coordination among cells. The proposed method is blind in the sense that it does not require pilot data to find the appropriate subspace. It is based on the theory of large random matrices that predicts that the eigenvalue spectra of large sample covariance matrices can asymptotically decompose into disjoint bulks as the matrix size grows large. Random matrix and free probability theory are utilized to predict under which system parameters such a bulk decomposition takes place. Simulation results are provided to confirm that the proposed method outperforms conventional linear channel estimation if bulk separation occurs.
The condition that the coherence time C must be greater that the number of receive antennas R was removed. Some Figures were updated. Now showing both cases with RC
ta113, FOS: Computer and information sciences, multiple-input multiple-output (MIMO) systems, ta213, ta114, principal component analysis, Computer Science - Information Theory, Information Theory (cs.IT), ta111, channel estimation, Signalbehandling, random matrices, free probability, Telekommunikation, multiple-input multiple-output, Signal Processing, Telecommunications, massive MIMO, Multiple antennas, spread-spectrum, (MIMO) systems, eigenvalue spectrum
ta113, FOS: Computer and information sciences, multiple-input multiple-output (MIMO) systems, ta213, ta114, principal component analysis, Computer Science - Information Theory, Information Theory (cs.IT), ta111, channel estimation, Signalbehandling, random matrices, free probability, Telekommunikation, multiple-input multiple-output, Signal Processing, Telecommunications, massive MIMO, Multiple antennas, spread-spectrum, (MIMO) systems, eigenvalue spectrum
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