Augmented Real-Valued Time-Delay Neural Network for Compensation of Distortions and Impairments in Wireless Transmitters

Wang, Dongming; Aziz, Mohsin; Helaoui, Mohamed; Ghannouchi, Fadhel M.

doi:10.1109/tnnls.2018.2838039

Cited by 150 publications

(108 citation statements)

References 25 publications

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“…There is disagreement about whether the brain represents time on a linear (Gibbon, 1977 ; Gibbon and Church, 1981 ; Roberts, 1981 ; Church and Gibbon, 1982 ; Gallistel, 1999 ; Wearden and Jones, 2007 ) or logarithmic (Church and Deluty, 1977 ; Staddon and Higa, 1999 ; Roberts, 2006 ; Yi, 2009 ) scale. Some studies have reported linear changes in neural activity over time (e.g., Komura et al, 2001 ; Machens et al, 2010 ), but we showed that activity profiles of mPFC neurons are better described by logarithmic than linear functions in the current task (Kim et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Distinct Dynamics of Striatal and Prefrontal Neural Activity During Temporal Discrimination

Kim

Jung

2018

Front. Integr. Neurosci.

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The frontal cortex-basal ganglia circuit plays an important role in interval timing. We examined neuronal discharges in the dorsomedial and dorsolateral striatum (DMS and DLS) in rats performing a temporal categorization task and compared them with previously recorded neuronal activity in the medial prefrontal cortex (mPFC). All three structures conveyed significant temporal information, but striatal neurons seldom showed the prolonged, full-interval spanning ramping activity frequently observed in the mPFC. Instead, the majority fired briefly during sample intervals. Also, the precision of neural time decoding became progressively worse with increasing time duration in the mPFC, but not in the striatum. With the caveat that mPFC and striatal units were recorded from different animals, our results suggest that the striatum and mPFC convey temporal information via distinct neural processes.

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Section: Discussionmentioning

confidence: 99%

Distinct Dynamics of Striatal and Prefrontal Neural Activity During Temporal Discrimination

Kim

Jung

2018

Front. Integr. Neurosci.

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“…Real-valued time-delay neural network (RVTDNN) [4] is a well-known model of the I/Q separation type. In particular, the augmented real-valued time-delay neural network (ARVTDNN) [5] works by adding envelope signals to the input signals. Moreover, reference [6] and [7] present methods for MIMO transmitters that compensate not only PA distortion but also cross talk.…”

Section: Introductionmentioning

confidence: 99%

Efficient Digital Predistortion Using Sparse Neural Network

2020

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This paper proposes an efficient neural-network-based digital predistortion (DPD), named as envelope time-delay neural network (ETDNN) DPD. The method complies with the physical characteristics of radio-frequency (RF) power amplifiers (PAs) and uses a more compact DPD model than the conventional neural-network-based DPD. Additionally, a structured pruning technique is presented and used to reduce the computational complexity. It is shown that the resulting ETDNN obtained after applying pruning becomes so sparse that its complexity is comparable to that of conventional DPDs such as memory polynomial(MP) and generalized memory polynomial (GMP), while the degradation in performance due to the pruning is negligible. In an experiment on a 3.5-GHz GaN Doherty power amplifier (PA), our method with the proposed pruning had only one-thirtieth the computational complexity of the conventional neural-network-based DPD for the same adjacent channel leakage ratio (ACLR). Moreover, compared with memory-polynomial-based digital predistortion, our method with the proposed pruning achieved a 7-dB improvement in ACLR, despite it having lower computational complexity. INDEX TERMS digital predistortion, generalized memory polynomial, memory polynomial, neural network, pruning technique Recently, a number of neural network-based DPDs have been presented [4], [5], [8], [9]. Their accurate modeling capability of neural networks enables them to outperform Volterra-based DPDs but at the expense of computational complexity.

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“…The neural network models used for PA behavioral modeling mainly include bidirectional long short-term memory (BiLSTM) neural network [9], radial basis function (RBF) neural network [10] and time delay neural network (TDNN) [8]. However, there are few DPD models to jointly compensate I/Q imbalance and PA nonlinearities [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments

Cai

Yao

et al. 2020

IEEE Access

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In this paper, a novel digital predistortion (DPD) model based on complex-valued pipelined Chebyshev functional link recurrent neural network (CPCFLRNN) for joint compensation of wideband transmitter distortions and impairments is proposed. The functional link neural network (FLNN) model has attracted much attention from scholars, and many improved models using this structure, such as Chebyshev FLNN, have been applied in the DPD of power amplifiers (PAs). However, these existing neural network models cannot deal with complex-valued input signals simultaneously, and the real-valued model structure will introduce cumbersome training algorithm and result in a long training time. The pipelined recurrent neural network (PRNN) has been successfully applied to nonlinear signal prediction because of its excellent ability for dealing with nonlinear nonstationary signals. Therefore, the PRNN model containing Chebyshev structure is extended to complex domain for the first time to obtain the CPCFLRNN model for DPD application. Considering the strong correlation of in-phase and quadrature phase (I/Q) components of the transmitter signal, the real time recurrent learning (RTRL) algorithm based on fully complex activation function is selected and extended to complex domain to obtain the complex-valued RTRL (CRTRL) algorithm for CPCFLRNN model training. A GaN PA was employed to verify the effectiveness of the proposed models. And the input signal is a 30MHz LTE signals which consists of I/Q imbalance and dc offsets. The experimental results show that the proposed CPCFLRNN model have more accurate modeling effect and better linearization performance compared with the conventional DPD models. INDEX TERMS Digital predistortion (DPD), complex-valued pipelined Chebyshev functional link recurrent neural network (CPCFLRNN), I/Q imbalance, dc offset, power amplifier (PA).

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Augmented Real-Valued Time-Delay Neural Network for Compensation of Distortions and Impairments in Wireless Transmitters

Cited by 150 publications

References 25 publications

Distinct Dynamics of Striatal and Prefrontal Neural Activity During Temporal Discrimination

Distinct Dynamics of Striatal and Prefrontal Neural Activity During Temporal Discrimination

Efficient Digital Predistortion Using Sparse Neural Network

Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments

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