Joint Initial Access and Localization in Millimeter Wave Vehicular Networks: a Hybrid Model/Data Driven Approach

High resolution compressive channel estimation provides information for vehicle localization when a hybrid mmWave MIMO system is considered. Complexity and memory requirements can, however, become a bottleneck when high accuracy localization is required. An additional challenge is the need of path order information to apply the appropriate geometric relationships between the channel path parameters and the vehicle, RSU and scatterers position. In this paper, we propose a low complexity channel estimation strategy of the angle of departure and time difference of arrival based on multidimensional orthogonal matching pursuit. We also design a deep neural network that predicts the order of the channel paths so only the LoS and first order reflections are used for localization. Simulation results obtained with realistic vehicular channels generated by ray tracing show that sub-meter accuracy can be achieved for 50% of the users, without resorting to perfect synchronization assumptions or unfeasible all-digital high resolution MIMO architectures.

Multitasking Framework for Unsupervised Simple Definition Generation

The definition generation task can help language learners by providing explanations for unfamiliar words. This task has attracted much attention in recent years. We propose a novel task of Simple Definition Generation (SDG) to help language learners and low literacy readers. A significant challenge of this task is the lack of learner's dictionaries in many languages, and therefore the lack of data for supervised training. We explore this task and propose a multitasking framework SimpDefiner that only requires a standard dictionary with complex definitions and a corpus containing arbitrary simple texts. We disentangle the complexity factors from the text by carefully designing a parameter sharing scheme between two decoders. By jointly training these components, the framework can generate both complex and simple definitions simultaneously. We demonstrate that the framework can generate relevant, simple definitions for the target words through automatic and manual evaluations on English and Chinese datasets. Our method outperforms the baseline model by a 1.77 SARI score on the English dataset, and raises the proportion of the low level (HSK level 1-3) words in Chinese definitions by 3.87%.

SDOAnet: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect Array

Direction of arrival (DOA) estimation is a fundamental problem in both conventional radar and wireless communication applications and emerging integrated sensing and communication (ISAC) systems. Due to many imperfect factors in the low-cost systems, including the antenna position perturbations, the inconsistent gains/phases, the mutual coupling effect, the nonlinear amplifier effect, etc., the performance of the DOA estimation often degrades significantly. To characterize the realistic array more accurately, a novel deep learning (DL)-based DOA estimation method named super-resolution DOA network (SDOAnet) is proposed in this paper. Different from the existing DL-based DOA methods, our proposed SDOAnet employs the sampled received signals, instead of the covariance matrices of the received signals, as the input of the convolution layers for extracting data features. Moreover, the output of SDOAnet is a vector that is independent of the DOA of targets but can be used to estimate their spatial spectrum. As a result, the same training network can be applied with any number of targets, which significantly reduce the implementation complexity. At last, the convergence speed of our SDOAnet with a low-dimension network structure is much faster than existing DL-based methods. Simulation results show that the proposed SDOAnet outperforms the existing DOA estimation methods with the effect of the imperfect array. The code about the SDOAnet is available online https://github.com/chenpengseu/SDOAnet.git.

Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communication

Configuring millimeter wave links following a conventional beam training protocol, as the one proposed in the current cellular standard, introduces a large communication overhead, specially relevant in vehicular systems, where the channels are highly dynamic. In this paper, we propose the use of a passive radar array to sense automotive radar transmissions coming from multiple vehicles on the road, and a radar processing chain that provides information about a reduced set of candidate beams for the links between the road-infrastructure and each one of the vehicles. This prior information can be later leveraged by the beam training protocol to significantly reduce overhead. The radar processing chain estimates both the timing and chirp rates of the radar signals, isolates the individual signals by filtering out interfering radar chirps, and estimates the spatial covariance of each individual radar transmission. Then, a deep network is used to translate features of these radar spatial covariances into features of the communication spatial covariances, by learning the intricate mapping between radar and communication channels, in both line-of-sight and non-line-of-sight settings. The communication rates and outage probabilities of this approach are compared against exhaustive search and pure radar-aided beam training methods (without deep learning-based mapping), and evaluated on multi-user channels simulated by ray tracing. Results show that: (i) the proposed processing chain can reliably isolate the spatial covariances for individual radars, and (ii) the radar-to-communications translation strategy based on deep learning provides a significant improvement over pure radar-aided methods in both LOS and NLOS channels.

YACLC: A Chinese Learner Corpus with Multidimensional Annotation

Learner corpus collects language data produced by L2 learners, that is second or foreign-language learners. This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction. However, there is little focus on learner corpus for Chinese as Foreign Language (CFL) learners. Therefore, we propose to construct a large-scale, multidimensional annotated Chinese learner corpus. To construct the corpus, we first obtain a large number of topic-rich texts generated by CFL learners. Then we design an annotation scheme including a sentence acceptability score as well as grammatical error and fluency-based corrections. We build a crowdsourcing platform to perform the annotation effectively (https://yaclc.wenmind.net). We name the corpus YACLC (Yet Another Chinese Learner Corpus) and release it as part of the CUGE benchmark (http://cuge.baai.ac.cn). By analyzing the original sentences and annotations in the corpus, we found that YACLC has a considerable size and very high annotation quality. We hope this corpus can further enhance the studies on Chinese International Education and Chinese automatic grammatical error correction.

Radar Aided mmWave Vehicle-to-InfrastructureLink Configuration Using Deep Learning

The high overhead of the beam training process is the main challenge when establishing mmWave communication links, especially for vehicle-to-everything (V2X) scenarios where the channels are highly dynamic. In this paper, we obtain prior information to speed up the beam training process by implementing two deep neural networks (DNNs) that realize radar-to-communication (R2C) channel information translation in a vehicle-to-infrastructure (V2I) system. Specifically, the first DNN is built to extract the information from the radar azimuth power spectrum (APS) to reconstruct the communication APS, while the second DNN exploits the information extracted from the spatial covariance of the radar channel to realize R2C covariance prediction. The achieved data rate and the similarity between the estimated and the true communication APS are used to evaluate the prediction performance. The covariance estimation method generally provides higher similarity, as the APS predictions cannot always capture the mismatch between the radar and communication APS. Compared to the beam training method which exploits directly the radar APS without an attempt to translate it to the communication channel, our proposed deep learning (DL) aided methods remarkably reduce the beam training overhead, resulting in a 13.3% and 21.9% rate increase when using the communication APS prediction and covariance prediction, respectively.

Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation

This paper demonstrates that multilingual pretraining, a proper fine-tuning method and a large-scale parallel dataset from multiple auxiliary languages are all critical for zero-shot translation, where the NMT model is tested on source languages unseen during supervised training. Following this idea, we present SixT++, a strong many-to-English NMT model that supports 100 source languages but is trained once with a parallel dataset from only six source languages. SixT++ initializes the decoder embedding and the full encoder with XLM-R large, and then trains the encoder and decoder layers with a simple two-stage training strategy. SixT++ achieves impressive performance on many-to-English translation. It significantly outperforms CRISS and m2m-100, two strong multilingual NMT systems, with an average gain of 7.2 and 5.0 BLEU respectively. Additionally, SixT++ offers a set of model parameters that can be further fine-tuned to develop unsupervised NMT models for low-resource languages. With back-translation on monolingual data of low-resource language, it outperforms all current state-of-the-art unsupervised methods on Nepali and Sinhal for both translating into and from English.

What Will the Future of UAV Cellular Communications Be? A Flight from 5G to 6G

What will the future of UAV cellular communications be? In this tutorial article, we address such a compelling yet difficult question by embarking on a journey from 5G to 6G and sharing a large number of realistic case studies supported by original results. We start by overviewing the status quo on UAV communications from an industrial standpoint, providing fresh updates from the 3GPP and detailing new 5G NR features in support of aerial devices. We then show the potential and the limitations of such features. In particular, we demonstrate how sub-6 GHz massive MIMO can successfully tackle cell selection and interference challenges, we showcase encouraging mmWave coverage evaluations in both urban and suburban/rural settings, and we examine the peculiarities of direct device-to-device communications in the sky. Moving on, we sneak a peek at next-generation UAV communications, listing some of the use cases envisioned for the 2030s. We identify the most promising 6G enablers for UAV communication, those expected to take the performance and reliability to the next level. For each of these disruptive new paradigms (non-terrestrial networks, cell-free architectures, artificial intelligence, reconfigurable intelligent surfaces, and THz communications), we gauge the prospective benefits for UAVs and discuss the main technological hurdles that stand in the way. All along, we distil our numerous findings into essential takeaways, and we identify key open problems worthy of further study.

Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders

Previous works mainly focus on improving cross-lingual transfer for NLU tasks with multilingual pretrained encoder (MPE), or improving the translation performance on NMT task with BERT. However, how to improve the cross-lingual transfer of NMT model with multilingual pretrained encoder is under-explored. In this paper, we focus on a zero-shot cross-lingual transfer task in NMT. In this task, the NMT model is trained with one parallel dataset and an off-the-shelf MPE, then is directly tested on zero-shot language pairs. We propose SixT, a simple yet effective model for this task. The SixT model leverages the MPE with a two-stage training schedule and gets further improvement with a position disentangled encoder and a capacity-enhanced decoder. The extensive experiments prove that SixT significantly improves the translation quality of the unseen languages. With much less computation cost and training data, our model achieves better performance on many-to-English testsets than CRISS and m2m-100, two strong multilingual NMT baselines.

Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning

Most existing Grammatical Error Correction (GEC) methods based on sequence-to-sequence mainly focus on how to generate more pseudo data to obtain better performance. Few work addresses few-shot GEC domain adaptation. In this paper, we treat different GEC domains as different GEC tasks and propose to extend meta-learning to few-shot GEC domain adaptation without using any pseudo data. We exploit a set of data-rich source domains to learn the initialization of model parameters that facilitates fast adaptation on new resource-poor target domains. We adapt GEC model to the first language (L1) of the second language learner. To evaluate the proposed method, we use nine L1s as source domains and five L1s as target domains. Experiment results on the L1 GEC domain adaptation dataset demonstrate that the proposed approach outperforms the multi-task transfer learning baseline by 0.50 $F_{0.5}$ score on average and enables us to effectively adapt to a new L1 domain with only 200 parallel sentences.