118 research outputs found
Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance
The pervasive nature of wireless telecommunication has made it the foundation
for mainstream technologies like automation, smart vehicles, virtual reality,
and unmanned aerial vehicles. As these technologies experience widespread
adoption in our daily lives, ensuring the reliable performance of cellular
networks in mobile scenarios has become a paramount challenge. Beamforming, an
integral component of modern mobile networks, enables spatial selectivity and
improves network quality. However, many beamforming techniques are iterative,
introducing unwanted latency to the system. In recent times, there has been a
growing interest in leveraging mobile users' location information to expedite
beamforming processes. This paper explores the concept of contextual
beamforming, discussing its advantages, disadvantages and implications.
Notably, the study presents an impressive 53% improvement in signal-to-noise
ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared
to scenarios without beamforming. It further elucidates how MRT contributes to
contextual beamforming. The importance of localization in implementing
contextual beamforming is also examined. Additionally, the paper delves into
the use of artificial intelligence schemes, including machine learning and deep
learning, in implementing contextual beamforming techniques that leverage user
location information. Based on the comprehensive review, the results suggest
that the combination of MRT and Zero forcing (ZF) techniques, alongside deep
neural networks (DNN) employing Bayesian Optimization (BO), represents the most
promising approach for contextual beamforming. Furthermore, the study discusses
the future potential of programmable switches, such as Tofino, in enabling
location-aware beamforming
Current advancements of numerical methods and experimental means for the integration of future propulsion systems.
To integrate advanced propulsion systems and to assess and verify the related benefit (e.g. fuel burn, noise) suitable design, evaluation and measurement tools are required. For that reason, the so-called Cross-Capability-Demonstrator (XDC) has been set up as one major activity of the Large Passenger Aircraft (LPA) Platform 1 of the Clean Sky 2 initiative. The XDC is intended to demonstrate high-fidelity CFD-tools, further developed prediction tools for noise and aero-elastics as well as advanced testing tools for measuring e.g. the flow field, the deformation and the acoustics. The article will provide an update on activities within the XDC and presents some examples of recent accomplishments related to this demonstrator
An Anchor-Point Based Image-Model for Room Impulse Response Simulation with Directional Source Radiation and Sensor Directivity Patterns
The image model method has been widely used to simulate room impulse
responses and the endeavor to adapt this method to different applications has
also piqued great interest over the last few decades. This paper attempts to
extend the image model method and develops an anchor-point-image-model (APIM)
approach as a solution for simulating impulse responses by including both the
source radiation and sensor directivity patterns. To determine the orientations
of all the virtual sources, anchor points are introduced to real sources, which
subsequently lead to the determination of the orientations of the virtual
sources. An algorithm is developed to generate room impulse responses with APIM
by taking into account the directional pattern functions, factional time
delays, as well as the computational complexity. The developed model and
algorithms can be used in various acoustic problems to simulate room acoustics
and improve and evaluate processing algorithms.Comment: 19 pages, 8 figure
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