Speaker
Description
Massive multiple-input multiple-output (MIMO) communication systems are very promising for wireless communication and fifth generation (5G) cellular networks. In massive MIMO, a large number of antennas are employed at the base station (BS), which provides a high degree of spatial freedom and enables the BS to simultaneously communicate with multiple user terminals. Due to the limited angular spread, the user channel vectors lie on low-dimensional subspaces. For each user, we aim to find a low-dimensional beamforming subspace that captures a large amount of the power of its channel vectors. We address this signal subspace estimation problem by finding a good estimator of the signal covariance matrix in terms of a truncated version of the nuclear norm based on the received data samples at the BS. In this talk, theoretical guarantees for signal covariance and subspace estimation are investigated. We derive optimal expectation bounds for every singular value of the deviation of the sample covariance from the true covariance matrix of i.i.d. centered Gaussian random variables. As a consequence, we present an optimal bound on the estimation error in the Massive MIMO setting in terms of the number of observed time samples, the number of sampled entries (antennas), the truncation and noise level.
