97 research outputs found
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
The latest satellite communication (SatCom) missions are characterized by a
fully reconfigurable on-board software-defined payload, capable of adapting
radio resources to the temporal and spatial variations of the system traffic.
As pure optimization-based solutions have shown to be computationally tedious
and to lack flexibility, machine learning (ML)-based methods have emerged as
promising alternatives. We investigate the application of energy-efficient
brain-inspired ML models for on-board radio resource management. Apart from
software simulation, we report extensive experimental results leveraging the
recently released Intel Loihi 2 chip. To benchmark the performance of the
proposed model, we implement conventional convolutional neural networks (CNN)
on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy,
precision, recall, and energy efficiency for different traffic demands. Most
notably, for relevant workloads, spiking neural networks (SNNs) implemented on
Loihi 2 yield higher accuracy, while reducing power consumption by more than
100 as compared to the CNN-based reference platform. Our findings point
to the significant potential of neuromorphic computing and SNNs in supporting
on-board SatCom operations, paving the way for enhanced efficiency and
sustainability in future SatCom systems.Comment: currently under review at IEEE Transactions on Machine Learning in
Communications and Networkin
UAV Based 5G Network: A Practical Survey Study
Unmanned aerial vehicles (UAVs) are anticipated to significantly contribute
to the development of new wireless networks that could handle high-speed
transmissions and enable wireless broadcasts. When compared to communications
that rely on permanent infrastructure, UAVs offer a number of advantages,
including flexible deployment, dependable line-of-sight (LoS) connection links,
and more design degrees of freedom because of controlled mobility. Unmanned
aerial vehicles (UAVs) combined with 5G networks and Internet of Things (IoT)
components have the potential to completely transform a variety of industries.
UAVs may transfer massive volumes of data in real-time by utilizing the low
latency and high-speed abilities of 5G networks, opening up a variety of
applications like remote sensing, precision farming, and disaster response.
This study of UAV communication with regard to 5G/B5G WLANs is presented in
this research. The three UAV-assisted MEC network scenarios also include the
specifics for the allocation of resources and optimization. We also concentrate
on the case where a UAV does task computation in addition to serving as a MEC
server to examine wind farm turbines. This paper covers the key implementation
difficulties of UAV-assisted MEC, such as optimum UAV deployment, wind models,
and coupled trajectory-computation performance optimization, in order to
promote widespread implementations of UAV-assisted MEC in practice. The primary
problem for 5G and beyond 5G (B5G) is delivering broadband access to various
device kinds. Prior to discussing associated research issues faced by the
developing integrated network design, we first provide a brief overview of the
background information as well as the networks that integrate space, aviation,
and land
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Deployment of an aerial platform system for rapid restoration of communications links after a disaster: A machine learning approach
Having reliable telecommunication systems in the immediate aftermath of a catastrophic event makes a huge difference in the combined effort by local authorities, local fire and police departments, and rescue teams to save lives. This paper proposes a physical model that links base stations that are still operational with aerial platforms and then uses a machine learning framework to evolve ground-to-air propagation model for such an ad hoc network. Such a physical model is quick and easy to deploy and the underlying air-to-ground (ATG) propagation models are both resilient and scalable and may use a wide range of link budget, grade of service (GoS), and quality of service (QoS) parameters to optimise their performance and in turn the effectiveness of the physical model. The prediction results of a simulated deployment of such a physical model and the evolved propagation model in an ad hoc network offers much promise in restoring communication links during emergency relief operations
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Multi-channel Stochastic Resource Allocation and Dynamic Access Scheduling
Modern communication systems often have the ability to transmit signals on multiple communication mediums (e.g., RF, visible light) or interfaces (e.g., MAC layer protocols) at the same time. While each channel has different characteristics, a centralized controller with channel condition information will be able to schedule the resource allocated to each channel to achieve various optimization criteria. In this thesis, we focus on two usage scenarios: Indoor hybrid free space optical (FSO)-WiFi femtocells and multi-channel satellite communication (SATCOM). For the Indoor hybrid free space optical (FSO)-WiFi femtocells, a smart network controller is designed to determine which channel/interface to use for a specific user/time slot combination to maximize some pre-specified objectives such as load balance. In particular, this problem is modeled as a dynamic scheduling problem, which is a Markov decision process problem that is solved using a deep-Q reinforcement learning (RL) framework. For the SATCOM scenario, a smart network controller is proposed to transmit information securely on different channels to mitigate jamming and eavesdropping attacks. The proposed approaches combine elements from game theory and information theory to provide provably secure protocols from an information theoretic viewpoint
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