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 the first phase of the project spent at MIT, 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 was initiated during the first phase. 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 results of this contribution were presented in December 2023 in person at the conference.
During the second phase, a work to summarize all the results achieved in terms of interference rejection algorithms using deep learning was developed. The work is already being reviewed by the coauthors and will be submitted soon. This work closes in a very well-rounded way the research goals of MASCOT.
Furthermore, during the second phase of MASCOT at the UC3M, teaching has been one of the primary focus as it was needed to achieve one of the main goals of the postdoctoral fellow.
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.
