AIMS AND CONTENT

LEARNING OUTCOMES

This subject introduces architectures of next-generation mobile communication systems (6G) based on software-defined and cognitive radio exploiting interaction and fusion between sensing and communications. The subject provides basic knowledge for co-design of sensing and communication, processing heterogeneous multisensory signals acquired by autonomous systems, and AI based approaches for jammer and anomaly detection.

AIMS AND LEARNING OUTCOMES

-Basic and advanced knowledge on design of integrated subsystems of  autonomous cognitive agents including sensing and mobile telecommunication functionalities 
- Knowledge on Integrated Sensing and Communications goals in 6G and software radio
- Knowledge on radio channel models including deterministic and statistical channel models; brief recall of digital modulation techniques.
--Knowledge on Software and cognitive radio for ISAC and on evolution of mobile communications towards 6G 

- Knowledge on sensing models and topologies  used by autonomous agents for joint estimation of object dynamics and optimal wireless data transmission    

- Knowledge on digital waveforms used for ISAC: OFDM, OTFS 
- Knowledge on iterative Bayesian filters for estimating object dynamics from ISAC waveforms. 
- Knowledge and capabilities on Matlab ISAC examples useful to implement learned concepts.
- Knowledge and capabilities on lab defined case studies: design of integrated sensing and communication blocks for self aware autonomous systems 

PREREQUISITES

The module requires that concepts on wireless channels related to FDM and TDM, Wireless free space channel model Digital modulations: bandpass/baseband equivalent, random process, ASK, PSK, FSK have been already acquired in first year master courses, as well as a good understanding of probability theory and its applications.

TEACHING METHODS

The course is divided in two parts. Lectures in frontal teaching modality presented together with slides will aim at describing the theroretical concepts and the techniques. Such lectures will cover 80%  hours and can  be recorded and made available on those channels recommended by Univeristy of Genova. The second part  20% is done within a laboratory carried on by an expert of the field and will involve application of programs in Matlab framework that correspond to theories and techniqeus shon at lectures. Students will be required to present a report at the end of each lab experience. 3-4 Lab experiences are planned. 
Exam will consist of an oral exam consisting of two parts First a report on a case study chosen jointly with  the student involving Isac aspects will be presented by the student discussed. Than a oral will follow including questions on course aspects.     

SYLLABUS/CONTENT

The module can be associated with ONU 2023 Objectived for Sustinable Development Nr. 4,9 and 11
- Introduction to integrated sensing and communications in cognitive dynamic systems
- Wireless telecommunications systems

        Recall of   FDM and TDM, Wireless free space channel model Digital modulations: bandpass/baseband equivalent, random process, ASK, PSK, FSK
        Statistical radio channel Multipath channel model 
        Time variant pulse response and its transforms
        Lab experience on Matlab for channel model simulation and modulation
- Sensing models and topologies

        Radar ambiguity function
        Monostatic, Bistatic, Full duplex topologies
        Sensing parameters and observation models: range, velocity, angles
         Estimation of parameters from observed wireless signals 

-   ISAC Waveforms
      General principles for optimal ISAC waveform design
      OFDM 
      OTFS
       Lab onofdm and otfs with isac
-  Application domains:

     Software Defined Radio
        SDR Architectures - RF frontends
      6G: Evolution ogf mobile comms and Isac standardization

       Multisensor ISAC
- Bayesian filters for dynamic estimation of multiple objects trajectories
      Bayesian filter
      Kalman filter
      Extended Kalman filter and Jointy Probabilistic Data Association filter
      Particle filter
      Switching model filters: Markov Jump PF   
     Labs on Kalman, Particle and MJPF

RECOMMENDED READING/BIBLIOGRAPHY

-   F. Molisch,  Wireless Communications, 2nd ed., Wiley-IEEE Press, 2011.

- F. Liu, C. Masouros, and A. Petropulu,  Joint Radar-Communication Systems,  Cambridge University Press, 2023.

- F. Liu, C. Masouros, A. Petropulu, H. V. Poor, and L. Hanzo,  “Integrated Sensing and Communications: Toward Dual-Functional Wireless  Networks for 6G and Beyond,”  IEEE Journal on Selected Areas in Communications, vol. 40, no. 6,  pp. 1728–1767, Jun. 2022.

- H. Wymeersch, F. Liu, T. Chen, E. de Carvalho, C. Masouros, and M. Z. Win,  “Integrated Sensing and Communications: Survey and Recent Advances,”  IEEE Communications Surveys & Tutorials, vol. 24, no. 3,  pp. 1947–1978, 2022.

- Y. Cui, F. Liu, X. Jing, and J. Mu,  “Integrating Sensing and Communications for Cooperative Perception in Autonomous Driving,”  IEEE Wireless Communications, vol. 29, no. 6,  pp. 12–19, Dec. 2022.

- K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y.-J. A. Zhang,  “The Roadmap to 6G: AI-Empowered Wireless Networks,”  IEEE Communications Magazine, vol. 57, no. 8,  pp. 84–90, Aug. 2019.

- DeepSense 6G Team,  “DeepSense 6G: A Large-Scale Multi-Modal Sensing and Communication Dataset,” 2024. [Online]. Available: https://www.deepsense6g.net/

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/11962

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Exam is structured in a written plus an oral part

The written part consists of presentation of a report describing a set of activities and results done by the student aiming at demonstrating knowledge and capabilities he acquired along lecures and lab attendance. The student is asked to agree with the professor a topic on which to present a report covering a case study  and providing a deeper insight with respect to knowledge and capabilities acquired in lessons and lab experiences. 

The  case of study have to be assigned/agreed at least three weeks before oral exam date on the basis of student request. The report will have to be delivered at indicated online repository at least 3 days before oral exam. The outcome of the evaluation of the written exam will be communicated the day before the oral exam. 

Oral exam will consist of discussion of the written exam, The student will have to prepare max 15  slides to describe results and approaches presented in the report.  Oral discussion will be oriented to demonstrate knowledge and capabilities to describe choices performed when developing report, as well as to comment rchoices, esults and performances obtained/expected for the chosen problem.

The oral exam will be passed in case the student will be admitted to the oral with at least 12 over 20 AND if the outcome of the oral will be at least 6 over 10. Laude can be assigned during the oral part.   

ASSESSMENT METHODS

Exam aims at assessing the following aspects about acquired student's knowldge and capabilities: 
- Level of Knowledge acquired with respect to theories and methods presented in course lectures by means of oral questions
-Level of practical and integration capabilities of techniques learned in lab with respect to either the assigned data analytics problem (in case of dataset processingchoice) or the design and the specifications of the data analysis system (in case of case of study choice)  chosen for the written part of the exam.
-Level of Capability and knowledge assessed by including in a report for the written part and discussing in the oral   motivate performed choices and obtained results on the assigned task perfrmed in the practical part.   

Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case- by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.