The emerging 6G vision of seamless connectivity between the cyber and physical worlds will shape next-generation networks and services. Intended applications are, e.g., self-driving connected vehicles, health IoT-based services, and critical infrastructure surveillance, which integrate communication and sensing functionalities. Besides, MADDIE foresees that future networks will incorporate devices with quantum information processing capabilities, which may either boost network performance or act as adversaries that jeopardize network security. Therefore, for upcoming services and applications to become a reality, wireless and communication technologies must not only meet the demands of extremely high data throughput and low latency, but also ensure cybersecurity in hybrid classical-quantum networks. 

Leveraging MADDIE participants’ know-how and expertise, this project will make use of classical and quantum coding, communications, and signal processing advanced techniques that will play a crucial role in answering the aforementioned challenges. Specifically, MADDIE will design coding and cryptographic algorithms for IoT-based applications in the context of classical-quantum networks. Furthermore, MADDIE will also explore innovative wireless technologies for the operation in the mmWave band and beyond, where future communication networks will be allocated to leverage massive bandwidth availability. Regarding co-existing sensing and communication networks, MADDIE will investigate distributed processing techniques that may possibly be combined with recent advances in graph signal processing to make efficient use of resources and increase performance. Finally, MADDIE will assess the impact of quantum devices in communication networks from both a computational and security perspective.

In particular, MADDIE focuses on the following objectives:

• To develop low-latency and low-complexity analog source-channel coding methods useful for wireless IoT-based scenarios.

• To propose post-quantum low-latency cryptographic algorithms for critical infrastructure surveillance, and analyze their performance experimentally.

• To develop innovative MIMO transmission methods suitable for mmWave bands, incorporating advanced technologies such as reconfigurable intelligent surfaces (RISs), time-modulated arrays (TMAs), non-coherent communications, and line-of-sight MIMO processing schemes.

• To explore new distributed processing techniques, possibly graph-based, for co-existing sensing and communication networks, and for detecting and tracking people using multiple nodes. 

• To study the quantum capacity of quantum noise channels, and propose novel coding techniques and machine learning-based error-correction techniques for current noisy intermediate-scale quantum computers. 

• To analyze the computational advantage of network-integrated quantum information processing devices using complexity theory.

• To conduct experimental tests of joint communications and sensing techniques and advanced MIMO processing schemes in the sub-6GHz and mmWave bands.

• To simulate error-correction techniques for noisy quantum computers with PennyLane's quantum machine learning tool.