Research On Wideband And Dual-band Digital Predistortion

Owing to its advantages of moderate cost, high flexibility and superior performance, digital predistortion (DPD) has become the mainstream linearization technique in modern communication transmitters. Spurred by the demands of high-speed wideband data service and multi-standard/multi-band communication systems, wideband and dual-band DPD are the highlights in DPD research at present. This dissertation focuses on behavioral modeling, model identification and DPD learning architectures for wideband and dual-band DPD.To reduce the sampling rate of analog to digital converters in the distortion acquisition path, the feedback loop is band-limited in a wideband DPD system, which requires a band-limited modeling technique for satisfactory performance. The author proves that the band-limited modeling technique can be formulated as a generalized least squares problem, with clear physical meaning from a frequency domain perspective. A frequency domain data based model extraction algorithm is proposed, which provides as good linearization performance as the conventional time domain data based algorithm with greatly reduced computational complexity. Conventional DPD learning architectures are extended for band-limited DPD, and their linearization performances are studied with the proposed algorithm.To compensate for cross-band modulation distortion as well as intra-band intermodulation distortion in a concurrent dual-band power amplifier (PA), a dual-band behavioral model of either band takes the form of a dual-input-single-output system. The reported two dimensional memory polynomial (2D-MP) shows high modeling accuracy for characterizing distortion behaviors in a concurrent dual-band PA, but it exhibits numerical instability and suffers from high complexity. To alleviate the numerical instability problem,2D orthogonal polynomials are proposed, which show robust performance in finite precision processing. To reduce the model complexity of2D-MP, a simplified2D-MP model with envelop cross terms of equal order is proposed, which proves to be a good tradeoff between complexity and performance, and facilates implementation with1D lookup tables. A multiplicative cell based dual-band model is also proposed, which yields good linearization performance with low model complexity. A one-step model identification algorithm is proposed for direct learning DPD. The expected predistortion signal is firstly derived by iterative injection of the baseband distortion components, which facilitates one-step model extraction of the DPD model by least squares estimation afterwards. The algorithm yields comparable linearization performance to the conventional algorithm, but bears much lower computational complexity. It is applied to model extraction of direct learning based wideband and dual-band DPD systems, and good linearization performance is achieved, which validates the effectiveness of the proposed algorithm.