Improving MIMO Phase Noise Estimation by Exploiting Spatial Correlations

Abstract

Phase locked loops (PLL) for RF carrier synthesis often employ oscillators that insert a considerable amount of time varying phase noise into the received signal. That noise must then be removed in digital basebandreceiver. This phase noise is an indivisible superposition of noise components from receiver and transmitter. Regarding to systems with multiple transmit and receive antennas (MIMO) and if multiple PLLs for carrier synthesis are used each of the superposed phase noise processes per transmit and receive antenna pair can be measured at the receiver. This paper provides a new scheme for high SNR scenarios that exploits spatial correlation between these overlaying phase noise processes at the receiver in order to improve estimation and compensation of the phase noise. Therefore the Wiener filter approach is applied.

Joint Estimation of Channel and Oscillator Phase Noise in MIMO Systems

“…In the case of MIMO systems, each transmit and receive antenna may be equipped with an independent oscillator. For example, in the case of line-of-sight (LoS) MIMO systems 1 , a single oscillator cannot be used for all the transmit or receive antennas since the antennas need to be placed far apart from one another [13] 2 . Similarly, in multiuser MIMO or space division multiple access (SDMA) systems, multiple users with independent oscillators transmit their signals to a common receiver [15].…”

“…Similarly, in multiuser MIMO or space division multiple access (SDMA) systems, multiple users with independent oscillators transmit their signals to a common receiver [15]. Thus, in order to comprehensively address the problem of phase noise mitigation in MIMO systems, algorithms that can jointly estimate multiple phase noise parameters at the receiver are of particular interest [13], [15].…”

“…The Cramér-Rao lower bounds (CRLB) and algorithms for estimation of phase noise in single-input single-output (SISO) systems are extensively and thoroughly analyzed in [3], [16]- [30]. However, these results are not applicable to MIMO systems, where a received signal may be affected by multiple phase noise parameters that need to be jointly estimated at the receiver [13], [31]- [33]. As a matter of fact, for SISO systems, Kalman filterbased methods have been effectively applied in [16], [20], [25], [29] for signal detection in the presence of phase noise.…”

“…However, the impact of imperfect knowledge of phase noise and channel on the performance of a MIMO system for different numbers of antennas, modulation schemes, and phase noise variances is not investigated. In [13], pilot-aided estimation of phase noise in a MIMO system is investigated, where a Wiener filtering approach is applied to estimate the phase noise parameters corresponding to the transmit and receive antennas. However, the scheme in [13] is based on the assumption that the MIMO channel is perfectly known at the receiver and it requires that while one antenna transmits its pilots, the remaining antennas stay silent, which is bandwidth inefficient and results in significant overhead.…”

“…In [13], pilot-aided estimation of phase noise in a MIMO system is investigated, where a Wiener filtering approach is applied to estimate the phase noise parameters corresponding to the transmit and receive antennas. However, the scheme in [13] is based on the assumption that the MIMO channel is perfectly known at the receiver and it requires that while one antenna transmits its pilots, the remaining antennas stay silent, which is bandwidth inefficient and results in significant overhead. In addition, in [13], the CRLB for the estimation problem is not derived and the performance of the proposed MIMO phase noise estimator is not investigated.…”