A digital predistorter, modeled by an augmented real-valued time-delay neural network (ARVTDNN), has been proposed and found suitable to mitigate the nonlinear distortions of the power amplifier (PA) along with modulator imperfections for a wideband direct-conversion transmitter. The input signal of the proposed ARVTDNN consists of Cartesian in-phase and quadrature phase (I/Q) components, as well as envelope-dependent terms. Theoretical analysis shows that the proposed model is able to produce a richer basis function containing both the desired odd- and even-order terms, resulting in improved modeling capability and distortion mitigation. Its actual performance has been validated through extensive simulations and experiments. The results show that the compensation and hardware impairment mitigation capabilities of the ARVTDNN are superior to the existing state-of-the-art real-valued focused time-delay neural network (RVFTDNN) by 3-4 dB for the adjacent channel power ratio and by 2-3 dB in terms of the normalized mean square error. Other important features of the proposed model are its reduced complexity, in terms of the number of parameters and floating-point operations, and its improved numerical stability compared to the RVFTDNN model.

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Nonlinearity in power amplifiers and In-phase and Quadrature phase (I/Q) imperfections degrade the performance of direct conversion transmitters. In this letter, a novel rational function based model is proposed to jointly alleviate both these impairments. The performance of the model is evaluated in terms of Normalized mean square error (NMSE) and Adjacent channel error power ratio (ACEPR). Simulation results and measurements show that the model has an improvement of around 2 dB NMSE and around 3 dB in ACEPR than the state of the art parallel Hammerstein based model [4]. Also the model attains a lower complexity while maintaining almost same performance.

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The majority of techniques that deal with the mitigation of in-phase and quadrature-phase (I/Q) imbalance at the transmitter (pre-compensation) require long training sequences, reducing the throughput of the system. These techniques also require a feedback path, which adds more complexity and cost to the transmitter architecture. Blind estimation techniques are attractive for avoiding the use of long training sequences. In this paper, we propose a blind frequency-independent I/Q imbalance compensation method based on the maximum likelihood (ML) estimation of the imbalance parameters of a transceiver. A closed-form joint probability density function (PDF) for the imbalanced I and Q signals is derived and validated. ML estimation is then used to estimate the imbalance parameters using the derived joint PDF of the output I and Q signals. Various figures of merit have been used to evaluate the efficacy of the proposed approach using extensive computer simulations and measurements. Additionally, the bit error rate curves show the effectiveness of the proposed method in the presence of the wireless channel and Additive White Gaussian Noise. Real-world experimental results show an image rejection of greater than 30 dB as compared to the uncompensated system. This method has also been found to be robust in the presence of practical system impairments, such as time and phase delay mismatches.