Effective Low-Complexity Optimization Methods for Joint Phase Noise and Channel Estimation in OFDM

Abstract

Phase noise correction is crucial to exploit full advantage of orthogonal frequency division multiplexing (OFDM) in modern high-data-rate communications. OFDM channel estimation with simultaneous phase noise compensation has therefore drawn much attention and stimulated continuing efforts. Existing methods, however, either have not taken into account the fundamental properties of phase noise or are only able to provide estimates of limited applicability owing to considerable computational complexity. In this paper, we have reformulated the joint estimation problem in the time domain as opposed to existing frequency-domain approaches, which enables us to develop much more efficient algorithms using the majorizationminimization technique. In addition, we propose a method based on dimensionality reduction and the Bayesian Information Criterion (BIC) that can adapt to various phase noise levels and accomplish much lower mean squared error than the benchmarks without incurring much additional computational cost. Several numerical examples with phase noise generated by free-running oscillators or phase-locked loops demonstrate that our proposed algorithms outperform existing methods with respect to both computational efficiency and mean squared error within a large range of signal-to-noise ratios.Index Terms-Carrier frequency offset (CFO), channel estimation, majorization-minimization (MM), orthogonal frequency division multiplexing (OFDM), phase noise.

Compensation of Phase Noise and IQ Imbalance in Multi-Carrier Systems

“…The PN estimated from solving the optimization problem is then used to finally obtain the estimated channel. It is pointed out that, to improve the quality of the estimated channel, the minimum mean squared error (MMSE) method is used in this paper rather than the least square (LS) method as in [14], [20], [21]. Furthermore, the proposed algorithm estimates the channel impulse response rather than the channel frequency response as proposed in [22].…”

“…This is especially useful when the MIMO scenario is considered later where only one preamble is used to estimate multiple channel impulse responses. From (21), the estimated channel impulse response obtained with the MMSE method is…”

“…The optimization problem in [14] is revisited in [20]- [22] with more realistic constraints. Compared to [20], the proposed algorithms in [21], [22] have lower complexity.…”

Phase Noise Compensation for OFDM Systems Exploiting Coherence Bandwidth

“…A popular approach is to utilize the fact that the channel impulse response h t in the time domain has fewer parameters than in the frequency response, resulting in time-domain channel estimation with a smaller number of unknowns. Based on this fact, the joint estimation algorithms for frequency-domain PN [31], [32], and time-domain PN [33], [34], respectively, have been presented. The basic technique used in [31]- [34] is a joint least-squares estimation.…”

“…Especially, [32] introduced a new constraint by the geometrical property of spectral PN components to complement the weakness of the relaxed constraint used in [31]. Further, [34] showed to be able to reduce the computational complexity of least-squares estimation significantly by using the majorization-minimization technique. However, the above least-squares estimation methods require a full-pilot OFDM symbol to perform joint PN and channel estimation, translating into significant pilot overhead.…”