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Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present informative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and communication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant attributes in a distributed manner, without any exchange of raw data, thought two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data. [ABSTRACT FROM AUTHOR]

This study considers the simultaneous optimization problem of the transmit beamforming at the access point and the reflecting beamforming at the intelligent reflecting surface (IRS) in an IRS-assisted multiuser downlink system. This joint optimization problem is nonconvex and challenging due to the intricate coupling of transmit and reflecting beamforming variables and the highly nonconvex constant modulus constraints. All the known solutions apply the alternating optimization framework to decouple the joint optimization problem into two problems. Then, these problems are optimized individually with different optimization methods in an alternating manner. However, such an alternative procedure may cause performance degradation. On the basis of the cross-entropy (CE) framework, which was developed initially for machine learning applications, we propose a machine learning-inspired algorithmic framework for simultaneously optimizing transmit and reflecting beamforming in an IRS-assisted wireless system. Extensive simulation results reveal that the proposed CE-based algorithms significantly perform better than the state-of-the-art test algorithms for various system configurations. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a rise of learning-based (i.e., artificial intelligence driven or AI-driven) video transport design, in order to achieve consistently high performance, even when the modern Internet is becoming increasingly heterogeneous while the applications are becoming unprecedentedly demanding (i.e., the simultaneous high-throughput and low-latency requirements of HD video telephony or intelligent remote driving). While separate evaluation using proprietary platform has shown the advantage of AI-driven algorithms over their non-AI counterparts, a systematic study is missing for directly comparing these AI-driven design under a uniform and practical platform. To bridge the gap, in this work, we first design and implement a full-fledged evaluation platform named Arsenal, which incorporates multiple state-of-the-art congestion control algorithms, most of which are AI-driven. Using Arsenal, we carry out a thorough comparative study of the algorithms, over massive traces collected from heterogeneous networks including WiFi, 4G, 3G and even the rare commercial 5G wireless networks. In particular, to enable convincing measurements for the dominated real-time video applications, we collect millions of practical video sessions in cooperation with a prevailing video service provider. The evaluation provides a handful of important observations, which are undiscovered before and have important impacts on future protocol design. Moreover, we will make the platform and algorithms open-source to enrich the research tools in the intelligent transportation community. [ABSTRACT FROM AUTHOR]