Multitone NB-IoT optimization based on filtered OFDM waveform

Narrowband Internet of Things (NB-IoT) is standardized by 3GPP as a novel radio-access scheme for next-generation IoT technology. In-band operation mode, as one of its deployment methods, shares the spectrum of LTE. To avoid interference leakage on adjacent resource blocks (RBs), the spectrum sharing system needs a spectrally well-localized waveform. In this thesis, we investigate filtered-OFDM waveform for NB-IoT in-band system. This is achieved by designing and exploiting optimized filter for each sub-band. Specifically, the optimum filter needs a suitable length, a relatively narrowed transition band, and adequate stopband attenuation, which efficiently reduces the required guard-band, minimizing the related overhead in resource usage. In the experiments, we simplify the system model by shifting the NB-IoT RB to the center of the LTE spectrum. Firstly, we test potential filter types with various transition bands, selecting suitable filter configurations with acceptable performance when the system operates under carrier frequency offset (CFO) of half subcarrier spacing. Then, we define two different power level test cases, which are based on the minimum SNR for 1% uncoded bit-error rate (BER), for examining NB-IoT and LTE error tolerance in asynchronous cases, when NB-IoT system fails to synchronize to the time-frequency alignment of LTE. Finally, the system performance in a multipath channel is evaluated. With filtered-OFDM, the out-of-band emission is suppressed effectively and the tolerance to time and frequency offset is significantly improved, which makes the proposed scheme suitable for supporting asynchronous NB-IoT operation

Fast signal quality monitoring for coherent communications enabled by CNN-based EVM estimation

We propose a fast and accurate signal quality monitoring scheme that uses convolutional neural networks (CNN) for error vector magnitude (EVM) estimation in coherent optical communications. We build a regression model to extract EVM information from complex signal constellation diagrams using a small number of received symbols. For the additive white Gaussian noise (AWGN) impaired channel, the proposed EVM estimation scheme shows a normalized mean absolute estimation error of 3.7% for quadrature phase shift keying (QPSK), 2.2% for 16-ary quadrature amplitude modulation (16QAM), and 1.1% for 64QAM signals, requiring only 100 symbols per constellation cluster in each observation period. Therefore, it can be used as a low-complexity alternative to conventional bit-error-rate (BER) estimation, enabling solutions for intelligent optical performance monitoring

Deep Learning Assisted Pre-Carrier Phase Recovery EVM Estimation for Coherent Transmission Systems

We exploit deep supervised learning and amplitude histograms of coherent optical signals captured before carrier phase recovery (CPR) to perform time-sensitive and accurate error vector magnitude (EVM) estimation for 32 Gbaud mQAM signal monitoring purposes

Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs

Despite the recent progress in text summarization made by large language models (LLMs), they often generate summaries that are factually inconsistent with original articles, known as "hallucinations" in text generation. Unlike previous small models (e.g., BART, T5), current LLMs make fewer silly mistakes but more sophisticated ones, such as imposing cause and effect, adding false details, overgeneralizing, etc. These hallucinations are challenging to detect through traditional methods, which poses great challenges for improving the factual consistency of text summarization. In this paper, we propose an adversarially DEcoupling method to disentangle the Comprehension and EmbellishmeNT abilities of LLMs (DECENT). Furthermore, we adopt a probing-based efficient training to cover the shortage of sensitivity for true and false in the training process of LLMs. In this way, LLMs are less confused about embellishing and understanding; thus, they can execute the instructions more accurately and have enhanced abilities to distinguish hallucinations. Experimental results show that DECENT significantly improves the reliability of text summarization based on LLMs

Laser Linewidth Tolerant EVM Estimation Approach for Intelligent Signal Quality Monitoring Relying on Feedforward Neural Networks

Robustness against the large linewidth semiconductor laser-induced impairments in coherent systems is experimentally demonstrated for a feedforward neural network-enabled EVM estimation scheme. A mean error of 0.4% is achieved for 28 Gbaud square and circular QAM signals and linewidths up to 12.3 MHz

Feedforward Neural Network-Based EVM Estimation: Impairment Tolerance in Coherent Optical Systems

Error vector magnitude (EVM) is commonly used for evaluating the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques for EVM estimation extend the functionality of conventional optical performance monitoring (OPM). In this article, we evaluate the tolerance of our developed EVM estimation scheme against various impairments in coherent optical systems. In particular, we analyze the signal quality monitoring capabilities in the presence of residual in-phase/quadrature (IQ) imbalance, fiber nonlinearity, and laser phase noise. We use feedforward neural networks (FFNNs) to extract the EVM information from amplitude histograms of 100 symbols per IQ cluster signal sequence captured before carrier phase recovery. We perform simulations of the considered impairments, along with an experimental investigation of the impact of laser phase noise. To investigate the tolerance of the EVM estimation scheme to each impairment type, we compare the accuracy for three training methods: 1) training without impairment, 2) training one model for all impairments, and 3) training an independent model for each impairment. Results indicate a good generalization of the proposed EVM estimation scheme, thus providing a valuable reference for developing next-generation intelligent OPM systems

Linear Regression vs. Deep Learning for Signal Quality Monitoring in Coherent Optical Systems

Error vector magnitude (EVM) is a metric for assessing the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques, e.g., feedforward neural networks (FFNNs) -based EVM estimation scheme leverage fast signal quality monitoring in coherent optical communication systems. Such a scheme estimates EVM from amplitude histograms (AHs) of short signal sequences captured before carrier phase recovery (CPR). In this work, we explore further complexity reduction by proposing a simple linear regression (LR) -based EVM monitoring method. We systematically compare the performance of the proposed method with the FFNN-based scheme and demonstrate its capability to infer EVM from an AH when the modulation format information is known in advance. We perform both simulation and experiment to show that the LR-based EVM estimation method achieves a comparable accuracy as the FFNN-based scheme. The technique can be embedded with modulation format identification modules to provide comprehensive signal information. Therefore, this work paves the way to design a fast-learning scheme with parsimony as a future intelligent OPM enabler

Optical Performance Monitoring in Digital Coherent Communications: Intelligent Error Vector Magnitude Estimation

The rapid development of data-driven techniques brings us new applications, such asfifth-generation new radio (5G NR), high-definition video, Internet of things (IoT),etc., which has greatly facilitated our daily lives. Optical networks as one fundamen-tal infrastructure are evolving to simultaneously support these high dimensional dataservices, with a feature of flexible, dynamic, and heterogeneous. Optical performancemonitoring (OPM) is a key enabler to guarantee reliable network management andmaintenance, which improving network controllability and resource efficiency. Accu-rately telemetry key performance indicators (KPIs) such as bit error rate (BER) canextend monitoring functionality and secure network management. However, retrievingthe BER level metric is time-consuming and inconvenient for OPM. Low-complexityOPM strategies are highly desired for ubiquitous departments at optical network nodes.This thesis investigates machine learning (ML) based intelligent error vector mag-nitude (EVM) estimation schemes in digital coherent communications, where EVMis widely used as an alternative BER metric for multilevel modulated signals. Wepropose a prototype of EVM estimation, which enables monitoring signal quality froma short observation period. Three alternative ML algorithms are explored to facilitatethe implementation of this prototype, namely convolutional neural networks (CNNs),feedforward neural networks (FFNNs), and linear regression (LR). We show that CNNconjunction with graphical signal representations, i.e., constellation diagrams and am-plitude histograms (AHs), can achieve decent EVM estimation accuracy for signalsbefore and after carrier phase recovery (CPR), which outperforms the conventionalEVM calculation. Moreover, we show that an FFNN-based scheme can reduce poten-tial energy and keep the estimation accuracy by directly operating with AH vectors.Furthermore, the estimation capability is thoroughly studied when the system hasdifferent impairments. Lastly, we demonstrate that a simple LR-designed model canperform as well as FFNN when the information on modulation formats is known. SuchLR-based can be easily implemented with modulation formats identification modulein OPM, providing accurate signal quality information

Multitone NB-IoT optimization based on filtered OFDM waveform

Narrowband Internet of Things (NB-IoT) is standardized by 3GPP as a novel radio-access scheme for next-generation IoT technology. In-band operation mode, as one of its deployment methods, shares the spectrum of LTE. To avoid interference leakage on adjacent resource blocks (RBs), the spectrum sharing system needs a spectrally well-localized waveform. In this thesis, we investigate filtered-OFDM waveform for NB-IoT in-band system. This is achieved by designing and exploiting optimized filter for each sub-band. Specifically, the optimum filter needs a suitable length, a relatively narrowed transition band, and adequate stopband attenuation, which efficiently reduces the required guard-band, minimizing the related overhead in resource usage. In the experiments, we simplify the system model by shifting the NB-IoT RB to the center of the LTE spectrum. Firstly, we test potential filter types with various transition bands, selecting suitable filter configurations with acceptable performance when the system operates under carrier frequency offset (CFO) of half subcarrier spacing. Then, we define two different power level test cases, which are based on the minimum SNR for 1% uncoded bit-error rate (BER), for examining NB-IoT and LTE error tolerance in asynchronous cases, when NB-IoT system fails to synchronize to the time-frequency alignment of LTE. Finally, the system performance in a multipath channel is evaluated. With filtered-OFDM, the out-of-band emission is suppressed effectively and the tolerance to time and frequency offset is significantly improved, which makes the proposed scheme suitable for supporting asynchronous NB-IoT operation