Low-Complexity Sub-band Digital Predistortion for Spurious Emission Suppression in Noncontiguous Spectrum Access

Noncontiguous transmission schemes combined with high power-efficiency requirements pose big challenges for radio transmitter and power amplifier (PA) design and implementation. Due to the nonlinear nature of the PA, severe unwanted emissions can occur, which can potentially interfere with neighboring channel signals or even desensitize the own receiver in frequency division duplexing (FDD) transceivers. In this article, to suppress such unwanted emissions, a low-complexity sub-band DPD solution, specifically tailored for spectrally noncontiguous transmission schemes in low-cost devices, is proposed. The proposed technique aims at mitigating only the selected spurious intermodulation distortion components at the PA output, hence allowing for substantially reduced processing complexity compared to classical linearization solutions. Furthermore, novel decorrelation based parameter learning solutions are also proposed and formulated, which offer reduced computing complexity in parameter estimation as well as the ability to track time-varying features adaptively. Comprehensive simulation and RF measurement results are provided, using a commercial LTE-Advanced mobile PA, to evaluate and validate the effectiveness of the proposed solution in real world scenarios. The obtained results demonstrate that highly efficient spurious component suppression can be obtained using the proposed solutions

Sub-Band Digital Predistortion for Noncontiguous Carriers: Implementation and Testing

To facilitate increasing data-rate demands and spectrum scarcity, non-contiguous transmission schemes are becoming more popular. However the non-contiguous carriers of such schemes intermodulate due to the nonlinear nature of power amplifiers (PAs). This may cause emissions which interfere with nearby channels or with one's own receiver in a frequency division duplexing transceiver. We implement a low-complexity, sub-band, block-adaptive, digital predistortion (DPD) solution that corrects the distortion in a real PA. Using WARPLab we correct up to ninth-order nonlinearities, and using a real-time FPGA design we correct up to third-order nonlinearities. This is done by targeting the most problematic spurious distortion components at the PA output and performing least mean squares training to adapt an inverse spur to inject. This sub-band method allows for reduced processing complexity over other full-band predistortion solutions. Using these techniques, we are able to suppress spurious emissions in WARP by over 20 dB

Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband

\u3cp\u3eDigital predistortion is the process of using digital signal processing to correct nonlinearities caused by the analog RF front-end of a wireless transmitter. These nonlinearities contribute to adjacent channel leakage, degrade the error vector magnitude of transmitted signals, and often force the transmitter to reduce its transmission power into a more linear but less power-efficient region of the device. Most predistortion techniques are based on polynomial models with an indirect learning architecture which have been shown to be overly sensitive to noise. In this work, we use neural network based predistortion with a novel neural network training method that avoids the indirect learning architecture and that shows significant improvements in both the adjacent channel leakage ratio and error vector magnitude. Moreover, we show that, by using a neural network based predistorter, we are able to achieve a 42% reduction in latency and 9.6% increase in throughput on an FPGA accelerator with 15% fewer multiplications per sample when compared to a similarly performing memory-polynomial implementation.\u3c/p\u3

Predistortion of OFDM Waveforms using Guard-band Subcarriers

Digital predistortion (DPD) is an important technique that is commonly used in wireless transmitters to reduce the out-of-band emissions caused by power amplifier (PA) nonlinearities. This frequency-domain goal is generally achieved by learning a baseband equivalent time-domain inverse transfer function of the PA and applying it to the transmitted digital baseband signal. In this work, we take advantage of the frequency-domain nature of orthogonal frequency-division multiplexing (OFDM) signals by injecting cancellation tones into the guard-band subcarriers to perform DPD. We experimentally evaluate our OFDM-based DPD (ODPD) method using a Doherty PA and show that, when compared to a standard generalized memory polynomial solution, the ODPD can achieve better suppression of out-of-band emissions with lower complexity. Moreover, when combined with a neural network model of the PA, our proposed ODPD method only requires oversampling the transmitted signal by a factor of two, which has important implications for the analog transceiver front-end.</p

Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband

Digital predistortion is the process of using digital signal processing to correct nonlinearities caused by the analog RF front-end of a wireless transmitter. These nonlinearities contribute to adjacent channel leakage, degrade the error vector magnitude of transmitted signals, and often force the transmitter to reduce its transmission power into a more linear but less power-efficient region of the device. Most predistortion techniques are based on polynomial models with an indirect learning architecture which have been shown to be overly sensitive to noise. In this work, we use neural network based predistortion with a novel neural network training method that avoids the indirect learning architecture and that shows significant improvements in both the adjacent channel leakage ratio and error vector magnitude. Moreover, we show that, by using a neural network based predistorter, we are able to achieve a 42% reduction in latency and 9.6% increase in throughput on an FPGA accelerator with 15% fewer multiplications per sample when compared to a similarly performing memory-polynomial implementation

Low-complexity, Multi Sub-band Digital Predistortion: Novel Algorithms and SDR Verification

The nonlinearities of power amplifiers combined with non-contiguous transmissions found in modern, frequency-agile, wireless standards create undesirable spurious emissions through the nearby spectrum of data carriers. Digital predistortion (DPD) is an effective way of combating spurious emission violations without the need for a significant power reduction in the transmitter leading to better power efficiency and network coverage. In this paper, an iterative, multi sub-band version of the sub-band DPD, proposed earlier by the authors, is presented. The DPD learning is iterated over intermodulation distortion (IMD) sub-bands until a satisfactory performance is achieved for each of them. A sequential DPD learning procedure is also presented to reduce the hardware complexity when higher order nonlinearities are incorporated in the DPD learning. Improvements in the convergence speed of the adaptive DPD learning are also achieved via incorporating a variable learning rate and interpolation of previously trained DPD coefficients. A WarpLab implementation of the proposed DPD is also shown with excellent suppression of the targeted spurious emissions

OFDM-Based Beam-Oriented Digital Predistortion for Massive MIMO

Linearization of massive MIMO arrays is a significant computational challenge that typically scales with the number of antennas. In this work, we introduce a beam-oriented digital predistortion (DPD) scheme for OFDM-based massive MIMO systems that applies predistortion before the precoder in the OFDM guard-band subcarriers. Using simulation results, we show that, for a 64 antenna massive MIMO array, our proposed method can achieve the same DPD performance as a conventional DPD method while requiring an order of magnitude fewer multiplications. © 2021 IEE

OFDM-based beam-oriented digital predistortion for massive MIMO

Linearization of massive MIMO arrays is a significant computational challenge that typically scales with the number of antennas. In this work, we introduce a beam-oriented digital predistortion (DPD) scheme for OFDM-based massive MIMO systems that applies predistortion before the precoder in the OFDM guard-band subcarriers. Using simulation results, we show that, for a 64 antenna massive MIMO array, our proposed method can achieve the same DPD performance as a conventional DPD method while requiring an order of magnitude fewer multiplications

Predistortion of OFDM Waveforms using Guard-band Subcarriers

Digital predistortion (DPD) is an important technique that is commonly used in wireless transmitters to reduce the out-of-band emissions caused by power amplifier (PA) nonlinearities. This frequency-domain goal is generally achieved by learning a baseband equivalent time-domain inverse transfer function of the PA and applying it to the transmitted digital baseband signal. In this work, we take advantage of the frequency-domain nature of orthogonal frequency-division multiplexing (OFDM) signals by injecting cancellation tones into the guard-band subcarriers to perform DPD. We experimentally evaluate our OFDM-based DPD (ODPD) method using a Doherty PA and show that, when compared to a standard generalized memory polynomial solution, the ODPD can achieve better suppression of out-of-band emissions with lower complexity. Moreover, when combined with a neural network model of the PA, our proposed ODPD method only requires oversampling the transmitted signal by a factor of two, which has important implications for the analog transceiver front-end