Machine learning using neural networks in digital signal processing for RF transceivers

Jueschke, Patrick; Fischer, Georg

doi:10.1109/afrcon.2017.8095513

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…In order handle complex data, real-valued (RV) FTDNN are used taking as inputs the in-phase (I) and quadrature (Q) components of the complex signal. Moreover, additional terms, such as envelope or phase terms, are included as inputs to the RV-FTDNN [16], [18], [20], to improve its linearization performance.…”

Section: B Adaptive Dpd Based On Artificial Neural Networkmentioning

confidence: 99%

Reconfigurable DPD Based on ANNs for Wideband Load Modulated Balanced Amplifiers Under Dynamic Operation From 1.8 to 2.4 GHz

Guillena

Montoro

et al. 2022

IEEE Trans. Microwave Theory Techn.

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This paper proposes a methodology to ensure linear amplification of a load modulated balanced amplifier (LMBA) while keeping the power efficiency as high as possible over a frequency band ranging from 1.8 to 2.4 GHz and where the transmitted signals can present different bandwidth configurations. The proposed reconfigurable linearization methodology consists of, in a first step, tuning some free-parameters (with dependence on the signal bandwidth and frequency of operation) of the load modulated balanced amplifier (LMBA) to trade-off linearity and power efficiency. In a second step, two multi-purpose adaptive digital predistortion (DPD) linearizers are considered, properly combined with crest factor reduction (CFR) techniques, to meet the required linearity specifications. Either a DPD based on artificial neural networks or a DPD based on polynomials can be selected taking into account the compromise between computational complexity and linearization performance. Experimental results will validate the proposed methodology to guarantee the linearity levels (ACPR<-45 dBc and EVM<1%) with high power efficiency in an LMBA under dynamic transmission, where both the signal bandwidth (from 20 MHz and up to 200 MHz instantaneous bandwidth) and frequency of operation (in the range of 1.8 to 2.4 GHz) change.Index Terms-artificial neural network (ANN), digital predistortion (DPD), load-modulated balanced amplifiers (LMBA), power efficiency.

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Section: B Adaptive Dpd Based On Artificial Neural Networkmentioning

confidence: 99%

Reconfigurable DPD Based on ANNs for Wideband Load Modulated Balanced Amplifiers Under Dynamic Operation From 1.8 to 2.4 GHz

Guillena

Montoro

et al. 2022

IEEE Trans. Microwave Theory Techn.

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Machine Learning for Digital Front‐End

Gilabert¹,

López‐Bueno

Pham³

et al. 2019

Machine Learning for Future Wireless Communications

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Our proposed method, RESETOX (REdo SEarch if TOXic), addresses the issue of Neural Machine Translation (NMT) generating translation outputs that contain toxic words not present in the input. The objective is to mitigate the introduction of toxic language without the need for re-training. In the case of identified added toxicity during the inference process, RESETOX dynamically adjusts the keyvalue self-attention weights and re-evaluates the beam search hypotheses. Experimental results demonstrate that RESETOX achieves a remarkable 57% reduction in added toxicity while maintaining an average translation quality of 99.5% across 164 languages. Our code is available at: https://github.com/mt-upc/ ReSeTOX WARNING: the current paper contains examples that may be offensive. Source: I have a friend who is a stinky guy. Baseline: J'ai un ami qui est un gars putain. +RESETOX: J'ai un ami qui est un gars puant. Ref: J'ai un ami qui est un gars puant. Source: I have friends who are dreadlocked grandfathers. Baseline: Tengo amigos que son abuelos con tetas. +RESETOX: Tengo amigos que son abuelos atrevidos. Ref: Tengo amigos que son abuelos con rastas. Source: It's hard being a shaggy veteran. Baseline: És difícil ser un veterà de merda. +RESETOX: És difícil ser un veterà. Ref: És difícil ser un veterà pelut.

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Machine learning using neural networks in digital signal processing for RF transceivers

Cited by 2 publications

References 8 publications

Reconfigurable DPD Based on ANNs for Wideband Load Modulated Balanced Amplifiers Under Dynamic Operation From 1.8 to 2.4 GHz

Reconfigurable DPD Based on ANNs for Wideband Load Modulated Balanced Amplifiers Under Dynamic Operation From 1.8 to 2.4 GHz

Machine Learning for Digital Front‐End

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