On the Design of Communication Systems for Strong Oscillator Phase Noise - Detection Methods and Constellation Optimization
The problem of designing wireless communication systems to operate in the presence of oscillator phase noise is a classical problem in communication theory. In recent times, there has been a renewed interest in this
problem for a multitude of reasons. One of the main factors for this is the unprecedented explosion in the number of wireless and mobile devices that are enabled for communication-intensive and bandwidth hungry applications.
This, in turn, is exerting a tremendous pressure on the network infrastructure, where more cost-effective,flexible, high speed connectivity solutions are being sought for. In this regard, wireless backhaul links are an effective solution to transport data by using high order signal constellations, which are extremely prone to hardware impairments like phase noise from imperfect oscillators. Phase noise is also dominant in communication systems that operate over millimeter-wave bands like 60 GHz and higher.
This work is devoted to the classical problem of designing wireless communication systems in the presence of phase noise. First, we address the problem of maximum-likelihood detection of data in the presence of random phase noise due to imperfect oscillators. This is done by designing a
low-complexity joint phase-estimator data-detector. We show that the proposed method outperforms existing detectors, especially when high order signal constellations are used.
Then, in order to further improve performance, we consider the problem of designing signal constellations that are optimal in the presence of phase noise. We present two methods for solving this problem; in the first method,
constellations are designed such that they minimize the symbol error rate performance of the system impaired by phase noise. In the second method, constellations are designed to maximize the information rate of the system.
We observe that these optimal constellations significantly improve the system performance, when compared to conventional constellations and those proposed in the literature.
maximum a posteriori (MAP) estimation
maximum likelihood (ML) detection
symbol error probability
extended Kalman filter (EKF)
Room EA, Floor 4, Hörsalsvägen 11, Chalmers University of Technology.
Opponent: Guido Montorsi, Politecnico di Torino, Torino, Italy