LR-FHSS-Sim: An Open-Source Discrete-Event Simulator for Evaluating LR-FHSS Networks
Konsep Inti
LR-FHSS-Sim is an open-source discrete-event simulator written in Python that enables flexible and extensible modeling of LR-FHSS networks for various research and development purposes.
Abstrak
The LR-FHSS-Sim is a discrete-event simulator for LR-FHSS networks, developed using the SimPy framework in Python. The simulator provides a modular and extensible architecture, allowing researchers to easily integrate new algorithms, signal processing techniques, and network modeling components.
Key highlights:
The core simulator includes classes representing the fundamental structures of LR-FHSS networks, such as Packets, Fragments, Nodes, and the Base station.
The simulator supports various traffic models, including Exponential, Uniform, and Markovian, which can be easily extended to accommodate different traffic patterns.
An example extension is provided for the Asynchronous Contention Resolution Diversity Aloha (ACRDA) technique, demonstrating the flexibility of the simulator to incorporate new functionalities.
The results showcase the impact of different traffic models on the network's average success rate and throughput, both for the standard LR-FHSS and the ACRDA-enabled LR-FHSS networks.
The modular design and open-source nature of the LR-FHSS-Sim aim to foster collaboration and further development within the research community, enabling the exploration of diverse algorithms and network scenarios for LR-FHSS technologies.
LR-FHSS-Sim: A Discrete-Event Simulator for LR-FHSS Networks
Statistik
LR-FHSS packets are composed of a header and a payload.
The header contains metadata about the transmission parameters and the seed to generate the pseudorandom hop sequence.
The payload is channel coded and split into small fragments of duration 102.4 ms.
The number of payload fragments is given by f = ⌊(b + 3) / (6 CR)⌋, where b is the payload length (in bytes) and CR is the coding rate (1/3 or 2/3).
Kutipan
"The main idea of this technique is to model a system whose global state changes over time. This is relevant, for example, in cases where there is a strong temporal correlation (e.g., sequential transmissions, different transmission durations), especially because they are not easily modeled by relatively simple mathematical frameworks or Monte Carlo simulations."
"Unlike prior LR-FHSS works, our simulation environment is not tailored to specific scenarios and can accommodate various algorithms and signal processing techniques. Unlike LoRaSim, we have enhanced the code structure and publication methods to foster usability across diverse research projects."
How can the LR-FHSS-Sim be extended to incorporate more realistic channel models, considering factors such as satellite movement, atmospheric conditions, and interference from other wireless systems
What are the potential challenges and trade-offs in designing scheduling and resource allocation algorithms for LR-FHSS networks, and how can the LR-FHSS-Sim be used to evaluate their performance
Given the growing interest in integrating machine learning and artificial intelligence techniques into 6G and beyond wireless systems, how can the LR-FHSS-Sim be further developed to enable the evaluation of such advanced signal processing and network optimization approaches
LR-FHSS-Sim: An Open-Source Discrete-Event Simulator for Evaluating LR-FHSS Networks
LR-FHSS-Sim: A Discrete-Event Simulator for LR-FHSS Networks
How can the LR-FHSS-Sim be extended to incorporate more realistic channel models, considering factors such as satellite movement, atmospheric conditions, and interference from other wireless systems
What are the potential challenges and trade-offs in designing scheduling and resource allocation algorithms for LR-FHSS networks, and how can the LR-FHSS-Sim be used to evaluate their performance
Given the growing interest in integrating machine learning and artificial intelligence techniques into 6G and beyond wireless systems, how can the LR-FHSS-Sim be further developed to enable the evaluation of such advanced signal processing and network optimization approaches