LiFi for Industry 4.0: Main Features, Implementation and Initial Testing of IEEE Std 802.15.13
The recently approved IEEE standard 802.15.13 defines a deterministic medium access scheme with dynamically allocated time slices and two ways of coding and modulation for industrial light communication. We present our work on prototyping the...
Multi-Gbit/s LiFi Experiments with a VCSEL TX and an Enhanced Pulsed Modulation PHY
Enhancing an OOK-based PHY for LiFi, we show that the large bandwidth, high power, and low energy consumption of low-cost VCSEL arrays combined with SFP drivers can be utilized to reach Multi-Gbit/s data rates and enable user mobility in large...
On the Optimal Performance of Distributed Cell-Free Massive MIMO with LoS Propagation
We investigate the capability of team MMSE technique in enhancing distributed beamforming for dense user-centric cell free massive MIMO networks. Considering the anticipated ultra-dense nature of cell-free massive MIMO networks and the...
Doppler Power Spectrum in Channels with von Mises-Fisher Distribution of Scatterers
This paper presents a closed-form expression for Doppler spectrum in mobile wireless channels with von Mises-Fisher (vMF) angular distribution of scatters. The obtained result is employed to analyze the impact of the vMF scattering parameters...
Semantic Security with Infinite-Dimensional Quantum Eavesdropping Channel
We investigate the semantic security of classical and quantum communication channels in the presence of infinite-dimensional side channels. A new method for security proofs is developed, which allows to derive finite block-length results in the...
Joint Waveform Design for Communication and Sensing with Adjustable PAPR
This paper proposes a dual-functional waveform design for integrated sensing and communication systems under adjustable peak-to-average-power-ratio constraints.
Towards Infrastructure-assisted WiFi Rate Adaptation for Converged Networks with Morpheus
The paper discusses a novel framework to integrate heterogeneous Radio Access Technologies (RATs) with the 5G O-RAN for coordination and joint optimization. As an example, it lays out the architecture of a WiFi Radio Intelligent Controller (RIC)...
Lossless Decoupling Networks for RF Self-Interference Cancellation in MIMO Full-Duplex Transceivers
The article presents a novel self-interference cancellation (SIC) technique using a lossless decoupling network in the RF domain for dedicated transmit-receive antennas. It includes antenna coupling analysis, SISO and 2×2 MIMO tests, and a...
Mobilizing the Terahertz Beam: D-Band Analog-Beamforming Front-End Prototyping and Long-Range 6G Trials
This article presents a novel D-band (150-175 GHz) wireless transceiver, achieving 48 dBm EIRP with eight RF beams via a Rotman lens, outperforming prior versions. Developed with InGaAs MMIC technology, it enabled 6G trials, including a 9-meter...
Correlation Properties in Channels With von Mises-Fisher Distribution of Scatterers
The von Mises-Fisher (vMF) scattering model is widely recognized for characterizing angular power distributions in MIMO systems and demonstrates versatility in modeling both communication and radar channels. This paper introduces exact and simple...
Towards Application Level Energy Monitoring for Green 6G Networks
We present a method to monitor network side energy consumption by an application running on the UE by fusing energy monitoring data with in band network telemetry.
Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks
We present a novel approach that combines the high rendering quality of NeRF-based 3D-aware GANs with the flexibility and computational advantages of 3DGS. By training a decoder that maps implicit NeRF representations to explicit 3D Gaussian...
Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond
In recent years, training data attribution (TDA) methods have emerged as a promising direction for the interpretability of neural networks. While research around TDA is thriving, limited effort has been dedicated to the evaluation of...
Experimental Characterization of a WR3-Coupled Photodiode Transmitter for High-Speed Terahertz Wireless Communication
We analyze the system performance of a large bandwidth photonic THz transmitter based on a WR3 coupled photodiode. Employing probabilistic constellation shaping (PCS) alongside a 64-QAM base constellation, we achieve net data rates of up to 76.8...
Multi-Resolution Generative Modeling of Human Motion from Limited Data
We present a generative model that learns to synthesize human motion from limited training sequences. Our framework provides conditional generation and blending across multiple temporal resolutions. moOurdel adeptly captures human motion patterns...
Robust mmWave/sub-THz multi-connectivity using minimal coordination and coarse synchronization
How to provide robust connectivity against signal blockage in 6G mmWave/sub-THz networks? This study demonstrates that carefully designed multi-connectivity schemes can realize full macrodiversity gains and significant SNR gains through canonical...
Real-time Experiments towards an Automotive OFDMA Communication Bus
We present a new automotive bus system which allows enhanced channel adaptation and fine-granular multi-user access based on OFDMA. We highlight advantages of OFDMA in automotive applications, sketch the concept and present an FPGA prototype.
Time Adaptive Probabilistic Shaping for Combined Optical/THz Links
We investigate the applicability of PAS for outdoor THz wireless links in simulations with weather-dependent loss models. Link performances are evaluated and optimal shaping entropies are determined to adjust error rates to a given FEC threshold....
Experimental Dataset for Developing and Testing ML Models in Optical Communication Systems
We present here a public dataset for developing and testing ML models. The dataset is developed in a laboratory setting and includes 12672 samples including data points with different modulation formats, symbol rates, distances, WDM channel...
Causes of Outcome Learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome
Nearly all diseases are caused by different combinations of exposures. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of...