Many communications signal formats are not power efficient because of their large peak-to-average power ratios (PARs). Moreover, in the presence of nonlinear devices such as power amplifiers (PAs) or mixers, the non-constant-modulus signals may generate both in-band distortion and out-of-band interference. Backing off the signal to the linear region of the device further reduces the system power efficiency. To improve the power efficiency of the communication system, one can pursue two approaches: i) linearize the PA; ii) reduce the high PAR of the input signal.
In this dissertation, we first explore the optimal nonlinearity under the peak power constraint. We show that the optimal nonlinearity is a soft limiter with a specific gain calculated based on the peak power limit, noise variance, and the probability density function of the input amplitude. The result is also extended to the fading channel case.
Next, we focus on digital baseband predistortion linearization for power amplifiers with memory effects. We build a high-speed wireless test-bed and carry out digital baseband predistortion linearization experiments. To implement adaptive PA linearization in wireless handsets, we propose an adaptive digital predistortion linearization architecture that utilizes existing components of the wireless transceiver to fulfill the adaptive predistorter training functionality.
We then investigate the topic of PAR reduction for OFDM signals and forward link CDMA signals. To reduce the PAR of the OFDM signal, we propose a dynamic selected mapping (DSLM) algorithm with a two-buffer structure to reduce the computational requirement of the SLM method without sacrificing the PAR reduction capability. To reduce the PAR of the forward link CDMA signal, we propose a new PAR reduction algorithm by introducing a relative offset between the in-phase branch and the quadrature branch of the transmission system.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/11649 |
| Date | 14 July 2005 |
| Creators | Qian, Hua |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 1678935 bytes, application/pdf |
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