2 research outputs found
Atomic Norm Denoising-Based Joint Channel Estimation and Faulty Antenna Detection for Massive MIMO
We consider joint channel estimation and faulty
antenna detection for massive multiple-input multiple-output
(MIMO) systems operating in time-division duplexing (TDD)
mode. For systems with faulty antennas, we show that the impact
of faulty antennas on uplink (UL) data transmission does not
vanish even with unlimited number of antennas. However, the
signal detection performance can be improved with a priori
knowledge on the indices of faulty antennas. This motivates us
to propose the approach for simultaneous channel estimation
and faulty antenna detection. By exploiting the fact that the
degrees of freedom of the physical channel matrix are smaller
than the number of free parameters, the channel estimation
and faulty antenna detection can be formulated as an extended
atomic norm denoising problem and solved efficiently via the
alternating direction method of multipliers (ADMM). Furthermore,
we improve the computational efficiency by proposing
a fast algorithm and show that it is a good approximation of
the corresponding extended atomic norm minimization method.
Numerical simulations are provided to compare the performances
of the proposed algorithms with several existing approaches and
demonstrate the performance gains of detecting the indices of
faulty antennas