DuoSpaceNet: Leveraging Both Bird's-Eye-View and Perspective View Representations for 3D Object Detection

Recent advances in multi-view camera-only 3D object detection either rely on an accurate reconstruction of bird's-eye-view (BEV) 3D features or on traditional 2D perspective view (PV) image features. While both have their own pros and cons, few have found a way to stitch them together in order to benefit from "the best of both worlds". To this end, we explore a duo space (i.e., BEV and PV) 3D perception framework, in conjunction with some useful duo space fusion strategies that allow effective aggregation of the two feature representations. To the best of our knowledge, our proposed method, DuoSpaceNet, is the first to leverage two distinct feature spaces and achieves the state-of-the-art 3D object detection and BEV map segmentation results on nuScenes dataset.

Joint Port Selection and Beamforming Design for Fluid Antenna Assisted Integrated Data and Energy Transfer

Integrated data and energy transfer (IDET) has been of fundamental importance for providing both wireless data transfer (WDT) and wireless energy transfer (WET) services towards low-power devices. Fluid antenna (FA) is capable of exploiting the huge spatial diversity of the wireless channel to enhance the receive signal strength, which is more suitable for the tiny-size low-power devices having the IDET requirements. In this letter, a multiuser FA assisted IDET system is studied and the weighted energy harvesting power at energy receivers (ERs) is maximized by jointly optimizing the port selection and transmit beamforming design under imperfect channel state information (CSI), while the signal-to-interference-plus-noise ratio (SINR) constraint for each data receiver (DR) is satisfied. An efficient algorithm is proposed to obtain the suboptimal solutions for the non-convex problem. Simulation results evaluate the performance of the FA-IDET system, while also demonstrate that FA outperforms the multi-input-multi-output (MIMO) counterpart in terms of the IDET performance, as long as the port number is large enough.

MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR

Motion prediction has been an essential component of autonomous driving systems since it handles highly uncertain and complex scenarios involving moving agents of different types. In this paper, we propose a Multi-Granular TRansformer (MGTR) framework, an encoder-decoder network that exploits context features in different granularities for different kinds of traffic agents. To further enhance MGTR's capabilities, we leverage LiDAR point cloud data by incorporating LiDAR semantic features from an off-the-shelf LiDAR feature extractor. We evaluate MGTR on Waymo Open Dataset motion prediction benchmark and show that the proposed method achieved state-of-the-art performance, ranking 1st on its leaderboard (https://waymo.com/open/challenges/2023/motion-prediction/).

Performance Analysis of Integrated Data and Energy Transfer Assisted by Fluid Antenna Systems

Fluid antenna multiple access (FAMA) is capable of exploiting the high spatial diversity of wireless channels to mitigate multi-user interference via flexible port switching, which achieves a better performance than traditional multi-input-multi-output (MIMO) systems. Moreover, integrated data and energy transfer (IDET) is able to provide both the wireless data transfer (WDT) and wireless energy transfer (WET) services towards low-power devices. In this paper, a FAMA assisted IDET system is studied, where $N$ access points (APs) provide dedicated IDET services towards $N$ user equipments (UEs). Each UE is equipped with a single fluid antenna. The performance of WDT and WET , \textit{i.e.}, the WDT outage probability, the WET outage probability, the reliable throughput and the average energy harvesting amount, are analysed theoretically by using time switching (TS) between WDT and WET. Numerical results validate our theoretical analysis, which reveals that the number of UEs and TS ratio should be optimized to achieve a trade-off between the WDT and WET performance. Moreover, FAMA assisted IDET achieves a better performance in terms of both WDT and WET than traditional MIMO with the same antenna size.