Related papers: Multimodal Transformers for Wireless Communication…
Beamforming is the key enabler for wireless communications in the mmWave bands. 802.11ad and WiGig are wireless technologies that currently use the 60 GHz unlicensed mmWave spectrum via beamforming techniques. It is likely that 5G systems…
The future of vehicular communication networks relies on mmWave massive multi-input-multi-output antenna arrays for intensive data transfer and massive vehicle access. However, reliable vehicle-to-infrastructure links require exact…
Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications and big data, Transformer-based multimodal learning has become a…
The mechanism of connecting multimodal signals through self-attention operation is a key factor in the success of multimodal Transformer networks in remote sensing data fusion tasks. However, traditional approaches assume access to all…
Millimeter-wave (mm-wave) communications requirebeamforming and consequent precise beam alignmentbetween the gNodeB (gNB) and the user equipment (UE) toovercome high propagation losses. This beam alignment needs tobe constantly updated for…
Large multiple antenna arrays coupled with accurate beamforming are essential in terahertz (THz) communications to ensure link reliability. However, as the number of antennas increases, beam alignment (focusing) and beam tracking in mobile…
In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented using a hardware- or software-based…
In Transport Mode Detection, a great diversity of methodologies exist according to the choice made on sensors, preprocessing, model used, etc. In this domain, the comparisons between each option are not always complete. Experiments on a…
Millimeter-wave (mmWave) communication enables high data rates for cellular-connected Unmanned Aerial Vehicles (UAVs). However, a robust beam management remains challenging due to significant path loss and the dynamic mobility of UAVs,…
Millimeter-wave (mmWave) communication systems rely on narrow beams for achieving sufficient receive signal power. Adjusting these beams is typically associated with large training overhead, which becomes particularly critical for…
The literature is abundant with methodologies focusing on using transformer architectures due to their prominence in wireless signal processing and their capability to capture long-range dependencies via attention mechanisms. In particular,…
In low altitude UAV communications, accurate channel estimation remains challenging due to the dynamic nature of air to ground links, exacerbated by high node mobility and the use of large scale antenna arrays, which introduce hybrid near…
The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway (HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel…
Huge overhead of beam training poses a significant challenge to mmWave communications. To address this issue, beam tracking has been widely investigated whereas existing methods are hard to handle serious multipath interference and…
This paper aims to predict radio channel variations over time by deep learning from channel observations without knowledge of the underlying channel dynamics. In next-generation wideband cellular systems, multicarrier transmission for…
Multivariate time series forecasting focuses on predicting future values based on historical context. State-of-the-art sequence-to-sequence models rely on neural attention between timesteps, which allows for temporal learning but fails to…
Beamforming has proven to be valuable in enabling full-duplex massive MIMO base stations, but doing so effectively often requires knowledge of the self-interference channel matrix H. Estimating this high-dimensional channel is costly in…
The reconstruction of a high resolution image given a low resolution observation is an ill-posed inverse problem in imaging. Deep learning methods rely on training data to learn an end-to-end mapping from a low-resolution input to a…
This paper focuses on advancing outdoor wireless systems to better support ubiquitous extended reality (XR) applications, and close the gap with current indoor wireless transmission capabilities. We propose a hybrid knowledge-data driven…
Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction. It has been used in several engineering applications such as radar, sonar, acoustics,…