Abstract:This paper introduces an efficient design approach for a fast-convolution-based variable-bandwidth (VBW) filter. The proposed approach is based on a hybrid of frequency sampling and optimization (HFSO), that offers significant computational complexity reduction compared to existing solutions for a given performance. The paper provides a design procedure based on minimax optimization to obtain the minimum complexity of the overall filter. A design example includes a comparison of the proposed design-based VBW filter and time-domain designed VBW filters implemented in the time domain and in the frequency domain. It is shown that not only the implementation complexity can be reduced but also the design complexity by excluding any computations when the bandwidth of the filter is adjusted. Moreover, memory requirements are also decreased compared to the existing frequency-domain implementations.
Abstract:This letter considers the design of linear-phase finite-length impulse response (FIR) filters for equalization of the frequency responses of digital-to-analog converters (DACs). The letter derives estimates for the filter orders required, as functions of the bandwidth and equalization accuracy, for four DAC pulses that are used in DACs in multiple Nyquist bands. The estimates are derived through a large set of minimax-optimal equalizers and the use of symbolic regression followed by minimax-optimal curve fitting for further enhancement. Design examples included demonstrate the accuracy of the proposed estimates. In addition, the letter discusses the appropriateness of the four types of linear-phase FIR filters, for the different equalizer cases, as well as the corresponding properties of the equalized systems.
Abstract:This paper introduces a low-complexity memoryless linearizer for suppression of distortion in analog-to-digital interfaces. It is inspired by neural networks, but has a substantially lower complexity than the neural-network schemes that have appeared earlier in the literature in this context. The paper demonstrates that the proposed linearizer can outperform the conventional parallel memoryless Hammerstein linearizer even when the nonlinearities have been generated through a memoryless polynomial model. Further, a design procedure is proposed in which the linearizer parameters are obtained through matrix inversion. Thereby, the costly and time consuming numerical optimization that is traditionally used when training neural networks is eliminated. Moreover, the design and evaluation incorporate a large set of multi-tone signals covering the first Nyquist band. Simulations show signal-to-noise-and-distortion ratio (SNDR) improvements of some 25 dB for multi-tone signals that correspond to the quadrature parts of OFDM signals with QPSK modulation.
Abstract:In a hybrid beamforming, a single digital predistortion (DPD) is inefficient to address all the nonlinearities over a subarray of power amplifiers (PAs) with underlying crosstalk in a massive multiple-input multiple-output (mMIMO) transmitter. In this context, the proposed work describes a novel hybrid post-weighting (PW) scheme. Here, it extends the competence of one trained DPD to all PAs exclusively via following PW block associated with optimal coefficients along the basis functions of the DPD. Consequently, it reduces the nonlinear radiation significantly in a wide range of azimuth directions to the transmitter.
Abstract:Efficient and low-complexity beamforming design is an important element of satellite communication systems with mobile receivers equipped with phased arrays. In this work, we apply the simultaneous perturbation stochastic approximation (SPSA) method with successive sub-array selection for finding the optimal antenna weights that maximize the received signal power at a uniform plane array (UPA). The proposed algorithms are based on iterative gradient approximation by injecting some carefully designed perturbations on the parameters to be estimated. Additionally, the successive sub-array selection technique enhances the performance of SPSA-based algorithms and makes them less sensitive to the initial beam direction. Simulation results show that our proposed algorithms can achieve efficient and reliable performance even when the initial beam direction is not well aligned with the satellite direction.