Transmission signals of modern communication systems,
such as orthogonal frequency-division multiplexing signals,
usually have large peak-to-average power ratio. These signals are
sensitive to the power amplifier’s nonlinearity, which generates
both in-band distortion and out-of-band spectral regrowth. The
adaptive digital predistortion (DPD) is an efficient linearization
technique without sacrificing the power efficiency. To estimate
the DPD coefficients, numerical instability and computational
complexity are bottlenecks. In this paper, we propose a general
approach to derive orthonormal basis functions, which can
improve the numerical stability during the coefficients estimation.
By applying the orthonormal basis functions, we further
propose an adaptive algorithm that exhibits a low computation
complexity of least mean squares algorithm while retaining the
fast convergence speed of recursive least squares algorithm. Both
simulation and experimental results validate the effectiveness of
the proposed algorithm.
Transmission signals of modern communication systems,such as orthogonal frequency-division multiplexing signals,usually have large peak-to-average power ratio. These signals aresensitive to the power amplifier’s nonlinearity, which generatesboth in-band distortion and out-of-band spectral regrowth. Theadaptive digital predistortion (DPD) is an efficient linearizationtechnique without sacrificing the power efficiency. To estimatethe DPD coefficients, numerical instability and computationalcomplexity are bottlenecks. In this paper, we propose a generalapproach to derive orthonormal basis functions, which canimprove the numerical stability during the coefficients estimation.By applying the orthonormal basis functions, we furtherpropose an adaptive algorithm that exhibits a low computationcomplexity of least mean squares algorithm while retaining thefast convergence speed of recursive least squares algorithm. Bothsimulation and experimental results validate the effectiveness ofthe proposed algorithm.
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