Joint phase noise estimation and data detection in coded multi‐input–multi‐output systems

Abstract

In this paper, the problem of joint oscillator phase noise (PHN) estimation and data detection for multi-input multi-output (MIMO) systems using bit-interleaved coded modulation (BICM) is analyzed. A new MIMO receiver that iterates between the estimator and the detector, based on the expectation-maximization (EM) framework, is proposed. It is shown that at high signal-to-noise ratios, a maximum a posteriori estimator (MAP) can be used to carry out the maximization step of the EM algorithm. Moreover, to reduce the computational complexity of the proposed EM algorithm, a soft decision-directed extended Kalman filter-smoother (EKFS) is applied instead of the MAP estimator to track the PHN parameters. Numerical results show that by combining the proposed EKFS based approach with an iterative detector that employs low density parity check (LDPC) codes, PHN can be accurately tracked. Simulations also demonstrate that compared to existing algorithms, the proposed iterative receiver can significantly enhance the performance of MIMO systems in the presence of PHN. Index TermsMulti-input multi-output (MIMO), phase noise (PHN), joint phase noise estimation and data detection, bitinterleaved-coded-modulation (BICM).

Iterative Detection and Phase-Noise Compensation for Coded Multichannel Optical Transmission

“…Moreover, in the context of multiple-input multiple-output (MIMO) systems for wireless transmission, various methods have been proposed for jointchannel phase-noise compensation [23]- [25]. In [26], [27], several algorithms were proposed for joint phase-noise estimation and data detection using the aforementioned frameworks, and in [28], [29], joint channel and phase-noise estimation for MIMO systems was proposed. The majority of the work has focused on multichannel models entailing oscillator phase noise that is either identical or independent between antennas in the MIMO system, in addition to channel mixing.…”

Phase noise in broadcasting: an outer bound for capacity region and corresponding simulations

“…So the values larger than 32 are not practical. In (6), N i shows the phase noise increment. It is an iid real Gaussian process with zero mean and variance σ N i 2 = γ 2 Δ and its probability distribution function as follows [15]:…”

“…The phase noise in the output of microwave oscillators, in OFDM systems, is modelled as a Wiener-Lévy process as usual [3]. Also in [5][6][7], the systems with phase noise estimator have been studied for the channels affected by Wiener-Lévy phase noise. In optical fibre communications, the phase noise resulted from some laser oscillators has been modelled as Wiener phase process ( [8][9][10] and references therein).…”