Periodic Reporting for period 1 - MASCOT (An Information-Theoretic Perspective on Massive Asynchronous Connectivity)
Période du rapport: 2021-10-01 au 2023-09-30
This project is dedicated to addressing the challenges posed by the massive connectivity problem in the context of asynchronous communications from an information theory perspective. The central challenge is to understand and develop efficient methods for communication in situations where a large number of devices need to communicate asynchronously over a shared communication medium while maintaining energy efficiency and low computational complexity.
The significance of this research extends to a wide range of real-world applications. In a society increasingly reliant on interconnected devices and communication technologies, solving the massive connectivity problem is critical. From the Internet of Things (IoT) to 5G networks and beyond, MASCOT is paving the way to understand how to design protocols specifically for this type of services and applications, thereby enabling a revolution in wireless communication systems, making them more reliable, energy-efficient, and capable of accommodating the diverse needs of a modern, interconnected world.
MASCOT is primarily focused on comprehending the fundamental limits of a group of wireless communication systems where a massive number of battery-limited devices sporadically access the communication medium to transmit short data packets. This type of communication, commonly referred to as massive machine-type communications, finds its application in IoT scenarios and sensor networks, poised to shape the future landscape of wireless connectivity.
The goal is to gain a thorough understanding of the fundamental limits inherent in this type of communication. Subsequently, this understanding will be used as inspiration for crafting efficient communication schemes that can closely approach these fundamental limits. In essence, the understanding of these fundamental limits and their derivation will serve as a well of inspiration for fellow researchers and developers, driving the creation of communication schemes tailored to this emerging field of services and applications.
One of the primary objectives of MASCOT centers around the creation of fundamental limits that are easy to compute and insightful, rendering them accessible to other researchers for benchmarking the performance of various transmission schemes.
Additionally, MASCOT also aims to develop some of such transmission schemes, drawing from a wide range of knowledge domains that have the potential to yield innovative results. These include fields such as compressed sensing and machine learning.
The significance of this research extends to a wide range of real-world applications. In a society increasingly reliant on interconnected devices and communication technologies, solving the massive connectivity problem is critical. From the Internet of Things (IoT) to 5G networks and beyond, MASCOT is paving the way to understand how to design protocols specifically for this type of services and applications, thereby enabling a revolution in wireless communication systems, making them more reliable, energy-efficient, and capable of accommodating the diverse needs of a modern, interconnected world.
MASCOT is primarily focused on comprehending the fundamental limits of a group of wireless communication systems where a massive number of battery-limited devices sporadically access the communication medium to transmit short data packets. This type of communication, commonly referred to as massive machine-type communications, finds its application in IoT scenarios and sensor networks, poised to shape the future landscape of wireless connectivity.
The goal is to gain a thorough understanding of the fundamental limits inherent in this type of communication. Subsequently, this understanding will be used as inspiration for crafting efficient communication schemes that can closely approach these fundamental limits. In essence, the understanding of these fundamental limits and their derivation will serve as a well of inspiration for fellow researchers and developers, driving the creation of communication schemes tailored to this emerging field of services and applications.
One of the primary objectives of MASCOT centers around the creation of fundamental limits that are easy to compute and insightful, rendering them accessible to other researchers for benchmarking the performance of various transmission schemes.
Additionally, MASCOT also aims to develop some of such transmission schemes, drawing from a wide range of knowledge domains that have the potential to yield innovative results. These include fields such as compressed sensing and machine learning.
The first work developed within the context of MASCOT studies the fundamental limits of the multiple access channel when a massive number of battery limited devices connect to the network and the access point does not know the number of devices that needs to be handled. This gave rise to a publication in the IEEE Transactions on Information Theory.
During these two years, a channel model for the massive connectivity problem was identified, the so called “A-channel”, which turned out to be very relevant for this project. The A-channel is a collision channel that permits channel coding and that presents unique characteristics that make it particularly well-suited for transmission schemes in the massive connectivity context. In particular, these schemes effectively divide the available degrees of freedom into manageable blocks from a computational perspective, which align with MASCOT's goal of maintaining algorithmic tractability.
MASCOT's research has yielded promising results and valuable contributions. The focus was extended beyond the simple additive Gaussian noise model and efforts were directed toward investigating the A-channel. Such investigations encompassed both non-asymptotic bounds and expansions, as well as the development of transmission schemes tailored to this specific channel model. These studies gave rise to two conference contributions, which also cover the milestones and deliverables within the first work package, but for this different mathematical model.
Furthermore, the exploration of asynchronous communications initially began with a more fundamental channel model, the binary adder channel. While the initial intention was to explore the implications of Gaussian noise, the results in this context turned out to be both insightful and intriguing. This research gave rise to a conference paper at the IEEE International Symposium on Information Theory.
Additionally, in line with the evolving landscape of MASCOT's research, the study of interference management through the lens of machine learning has been initiated. In particular, the potential of deep learning in mitigating interference in multiple access environments has been explored. These environments involve devices from different wireless technologies accessing the communication medium simultaneously at the same frequency, creating interference in an asynchronous manner. Some of the findings have already been published in conference papers, with a particular highlight being a contribution to NeurIPS, one of the most prestigious international conferences on machine learning.
The execution of MASCOT has also adhered quite closely to the dissemination, exploitation, and communication plan. MASCOT researchers have participated in seminars, conferences, and even conducted a tutorial to disseminate their work to the research community.
Furthermore, the relevant code associated with their publications on Github has been shared.
Finally, the researchers have been actively promoting their work by providing updates on LinkedIn and Twitter.
During these two years, a channel model for the massive connectivity problem was identified, the so called “A-channel”, which turned out to be very relevant for this project. The A-channel is a collision channel that permits channel coding and that presents unique characteristics that make it particularly well-suited for transmission schemes in the massive connectivity context. In particular, these schemes effectively divide the available degrees of freedom into manageable blocks from a computational perspective, which align with MASCOT's goal of maintaining algorithmic tractability.
MASCOT's research has yielded promising results and valuable contributions. The focus was extended beyond the simple additive Gaussian noise model and efforts were directed toward investigating the A-channel. Such investigations encompassed both non-asymptotic bounds and expansions, as well as the development of transmission schemes tailored to this specific channel model. These studies gave rise to two conference contributions, which also cover the milestones and deliverables within the first work package, but for this different mathematical model.
Furthermore, the exploration of asynchronous communications initially began with a more fundamental channel model, the binary adder channel. While the initial intention was to explore the implications of Gaussian noise, the results in this context turned out to be both insightful and intriguing. This research gave rise to a conference paper at the IEEE International Symposium on Information Theory.
Additionally, in line with the evolving landscape of MASCOT's research, the study of interference management through the lens of machine learning has been initiated. In particular, the potential of deep learning in mitigating interference in multiple access environments has been explored. These environments involve devices from different wireless technologies accessing the communication medium simultaneously at the same frequency, creating interference in an asynchronous manner. Some of the findings have already been published in conference papers, with a particular highlight being a contribution to NeurIPS, one of the most prestigious international conferences on machine learning.
The execution of MASCOT has also adhered quite closely to the dissemination, exploitation, and communication plan. MASCOT researchers have participated in seminars, conferences, and even conducted a tutorial to disseminate their work to the research community.
Furthermore, the relevant code associated with their publications on Github has been shared.
Finally, the researchers have been actively promoting their work by providing updates on LinkedIn and Twitter.
The additional research directions within the project's scope underscore the commitment to adapt and refine the approach to address the challenges and opportunities that have emerged during the course of this research. This commitment ensures the continuation to make meaningful contributions to the field of massive connectivity and asynchronous communications, transcending the current state of the art.
Consequently, in the future, the plan is to:
• Explore coding schemes that approach the fundamental limits of the A-channel. If these coding schemes can maintain low complexity, the possibility of engaging with policy makers to gauge their suitability for standardization in future wireless communication systems will be increased.
• Investigate the performance of LDPC codes in asynchronous access to the communication medium under more realistic wireless communication impairments. This investigation aims to confirm the benefits of asynchrony in certain communication scenarios.
• Extend the machine learning interference rejection algorithms to operate efficiently in real congested wireless communication environments, while minimizing computational overhead. This effort aims to bridge the gap between synthetic experiments and practical operations.
This project possesses the potential for substantial societal impact. For example, designing energy-efficient communication systems can directly enhance the quality of communications and have a positive environmental impact by reducing the energy required to transmit information.
Consequently, in the future, the plan is to:
• Explore coding schemes that approach the fundamental limits of the A-channel. If these coding schemes can maintain low complexity, the possibility of engaging with policy makers to gauge their suitability for standardization in future wireless communication systems will be increased.
• Investigate the performance of LDPC codes in asynchronous access to the communication medium under more realistic wireless communication impairments. This investigation aims to confirm the benefits of asynchrony in certain communication scenarios.
• Extend the machine learning interference rejection algorithms to operate efficiently in real congested wireless communication environments, while minimizing computational overhead. This effort aims to bridge the gap between synthetic experiments and practical operations.
This project possesses the potential for substantial societal impact. For example, designing energy-efficient communication systems can directly enhance the quality of communications and have a positive environmental impact by reducing the energy required to transmit information.