CHANNEL ESTIMATION IS crucial for providing a high-data-rate transmission in wireless communications. This thesis specifically considers multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) channels as many communications standards embrace popular MIMO and OFDM techniques. Although plenty of research has thoroughly investigated relevant channel estimation, some practical aspects that are critical to the power efficiency and system performance have not been properly studied in this context. The two aspects considered herein are the peak-to-average-power ratio (PAR) and phase noise. Mathematically, these two issues can be well represented by sequences that share some structural similarity and enable a common optimization approach.

Communicat...[ Read more ]

CHANNEL ESTIMATION IS crucial for providing a high-data-rate transmission in wireless communications. This thesis specifically considers multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) channels as many communications standards embrace popular MIMO and OFDM techniques. Although plenty of research has thoroughly investigated relevant channel estimation, some practical aspects that are critical to the power efficiency and system performance have not been properly studied in this context. The two aspects considered herein are the peak-to-average-power ratio (PAR) and phase noise. Mathematically, these two issues can be well represented by sequences that share some structural similarity and enable a common optimization approach.

Communication systems have widely employed sequences of low PAR to meet the hardware requirements and maximize the power efficiency. A special case of low PAR constraints is the unimodular constraint. Numerous works have studied the unimodular sequence design and attempted to obtain good correlation properties. Regarding channel estimation, however, sequences of such properties do not necessarily qualify for the mission. Instead, tailored unimodular sequences for specific criteria of interest are more desirable especially when we have access to the prior knowledge of the channel. First, the problems of unimodular sequence design for MIMO channel estimation are formulated by optimizing the minimum mean square error (MMSE) and conditional mutual information (CMI), respectively. The obtained optimization problems are non-convex, for which efficient algorithms based on the majorization-minimization (MM) framework are devised. More general, optimal sequence design with low PAR constraints are formulated and solved following a similar algorithmic approach to the unimodular case.

The other practical issue is phase noise, the correction of which is necessary to exploit full advantage of OFDM systems to provide high-data-rate communications. OFDM channel estimation with simultaneous phase noise compensation has therefore drawn much attention and stimulated continuing efforts. Existing methods, however, are only able to provide estimates of limited applicability due to their heuristic nature or considerable computational complexity. In this thesis, the joint estimation problem is reformulated in the time domain as opposed to the popular frequency-domain approaches. In doing so, much more computationally efficient algorithms can be developed based on the MM framework. Furthermore, to deal with the under-determined nature in the original estimation, dimensionality reduction and regularization are introduced and thus more effective phase noise-compensating algorithms are proposed that outperform the benchmarks without incurring much additional computational cost.