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 phase noise and channel estimation problem in the time domain as opposed to existing frequency-domain approaches, which enables us to develop much more efficient algorithms using the majorization-minimization technique. In addition, we propose two methods based on dimensionality reduction and regularization, respectively, that can adapt to various phase noise levels and SNR and achieve much lower estimation errors 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

Existing System

Monte Carlo method, Convex Optimization.

Proposed System

We have proposed efficient algorithms for the joint phase noise and channel estimation in OFDM. The algorithms are devised based on the majorization-minimization technique and apply to two canonical models of phase noise—Wiener process and Gaussian process. To properly address the underdetermined nature in the original estimation problem, dimensionality reduction and regularization have been proposed with similar MM algorithms provided. The simulation results have shown that when the same dimensionality reduction is employed, our proposed algorithms achieve the same MSE as that of the benchmark but consume much less time. By further selecting the optimal dimensionality reduction with BIC or imposing appropriate regularization, our proposed algorithms produce significantly better estimates for moderate SNR without demanding much additional computation time. It is expected that in modern applications of OFDM, where a large number of subcarriers are deployed, the advantage of our methods should be outstanding.

Architecture