Task-Oriented Communication Design in Cyber-Physical Systems: A Survey on Theory and Applications

in IEEE Access (2022)

Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era ... [more ▼]

Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bit rate, but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm. In parallel, it is projected that by 2023, half of the worldwide network connections will be among machines rather than humans. In this context, it is crucial to establish a new paradigm for designing communication strategies for multi-agent cyber-physical systems. This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science in order to formalize a general framework for task-oriented communication designs. In this direction, this paper reviews and structures the relevant theoretical work across a wide range of scientific communities. Subsequently, it proposes a general conceptual framework for task-oriented communication design, along with its specializations according to targeted use cases. Furthermore, it provides a survey of relevant contributions in dominant applications, such as industrial internet of things, multi-unmanned aerial vehicle (UAV) systems, autonomous vehicles, distributed learning systems, smart manufacturing plants, 5G and beyond self-organizing networks, and tactile internet. Finally, this paper also highlights the most important open research topics from both the theoretical framework and application points of view. [less ▲]

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

in IEEE Communications Surveys & Tutorials (2022), 24(4), 2633-2672

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for ... [more ▼]

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks. [less ▲]

Learning-based Physical Layer Communications for Multiagent Collaboration

in Mostaani, Arsham; Simeone, Osvaldo; Chatzinotas, Symeon (Eds.) et al PIMRC 2019 Proceedings (2019, September 11)

Consider a collaborative task carried out by two autonomous agents that can communicate over a noisy channel. Each agent is only aware of its own state, while the accomplishment of the task depends on the ... [more ▼]

Consider a collaborative task carried out by two autonomous agents that can communicate over a noisy channel. Each agent is only aware of its own state, while the accomplishment of the task depends on the value of the joint state of both agents. As an example, both agents must simultaneously reach a certain location of the environment, while only being aware of their own positions. Assuming the presence of feedback in the form of a common reward to the agents, a conventional approach would apply separately: (\emph{i}) an off-the-shelf coding and decoding scheme in order to enhance the reliability of the communication of the state of one agent to the other; and (\emph{ii}) a standard multiagent reinforcement learning strategy to learn how to act in the resulting environment. In this work, it is argued that the performance of the collaborative task can be improved if the agents learn how to jointly communicate and act. In particular, numerical results for a baseline grid world example demonstrate that the jointly learned policy carries out compression and unequal error protection by leveraging information about the action policy. [less ▲]