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We consider a massive MIMO system based on Time Division Duplexing (TDD) andchannel reciprocity, where the base stations (BSs) learn the channel vectors oftheir users via the pilots transmitted by the users in the uplink (UL). It iswell-known that, in the limit of very large number of BS antennas, the systemperformance is limited by pilot contamination, due to the fact that the sameset of orthogonal pilots is reused in multiple cells. In this paper, we proposea low-complexity algorithm that uses the received UL wideband pilot snapshotsin an observation window comprising several coherence blocks (CBs) to obtain anestimate of the angle-delay Power Spread Function (PSF) of the received signal.This is generally given by the sum of the angle-delay PSF of the desired userand the angle-delay PSFs of the copilot users (CPUs), i.e., the users re-usingthe same pilot dimensions in other cells/sectors. We propose supervised andunsupervised clustering algorithms to decompose the estimated PSF and isolatethe part corresponding to the desired user only. We use this decomposition toobtain an estimate of the covariance matrix of the user wideband channelvector, which we exploit to decontaminate the desired user channel estimate byapplying Minimum Mean Squared Error (MMSE) smoothing filter, i.e., the optimalchannel interpolator in the MMSE sense. We also propose an effectivelow-complexity approximation/implementation of this smoothing filter. We usenumerical simulations to assess the performance of our proposed method, andcompare it with other recently proposed schemes that use the same idea ofseparability of users in the angle-delay domain.

Massive MIMO Pilot Decontamination and Channel Interpolation via Wideband Sparse Channel Estimation