References

References

  1. F. Liu, C. Masouros, A. P. Petropulu, H. Griffiths, and L. Hanzo, Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead, IEEE Transactions on Communications, 2020

    A comprehensive survey and tutorial on dual-function radar-communication (DFRC) systems. Covers waveform design strategies (radar-centric, communication-centric, and joint), beamforming for MIMO DFRC, and performance metrics. The primary reference for the ISAC system model and the joint beamforming formulation in Sections 29.1 and 29.4.

  2. C. Sturm and W. Wiesbeck, Waveform Design and Signal Processing Aspects for Fusion of Wireless Communications and Radar Sensing, Proceedings of the IEEE, 2011

    A seminal paper on OFDM radar processing. Derives the element-wise division technique for removing communication data from the radar processing chain and presents the 2D-FFT range-Doppler processing framework for OFDM waveforms. The primary reference for the OFDM radar signal model in Section 29.3.

  3. J. Li and P. Stoica, MIMO Radar with Colocated Antennas, IEEE Signal Processing Magazine, 2007

    A tutorial on MIMO radar with colocated antennas, introducing the virtual aperture concept and waveform diversity. Derives the virtual array steering vector via the Kronecker product of transmit and receive steering vectors and analyses the angular resolution improvement. The primary reference for Section 29.4 on MIMO radar.

  4. D. W. Bliss, Cooperative Radar and Communications Signaling: The Estimation and Information Theory Odd Couple, IEEE Radar Conference, 2014

    An information-theoretic perspective on joint radar-communication systems. Introduces mutual information as a radar waveform design metric and analyses the fundamental trade-off between communication rate and radar estimation accuracy. Provides the theoretical basis for the MI vs CRB waveform optimisation discussion in Section 29.3.

  5. A. Hassanien and S. A. Vorobyov, Phased-MIMO Radar: A Tradeoff between Phased-Array and MIMO Radars, IEEE Transactions on Signal Processing, 2010

    Introduces the phased-MIMO radar concept, which bridges conventional phased-array radar (coherent gain, narrow beam) and MIMO radar (waveform diversity, virtual aperture) through a flexible beamspace partitioning. Provides insight into the trade-off between coherent processing gain and spatial diversity that is fundamental to ISAC beamforming design.

  6. E. J. Candes, The Restricted Isometry Property and Its Implications for Compressed Sensing, 2008

    Establishes the restricted isometry property (RIP) as the key condition for reliable sparse recovery via basis pursuit and LASSO. Proves that random matrices satisfy the RIP with O(s log(G/s)) measurements, providing the theoretical foundation for compressed sensing in RF imaging.

  7. F. Liu, Y. Cui, C. Masouros, J. Xu, T. X. Han, Y. C. Eldar, and S. Buzzi, Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond, IEEE Journal on Selected Areas in Communications, 2022

    A state-of-the-art overview of ISAC for 6G, covering the rate-CRB Pareto frontier, MIMO ISAC beamforming design, waveform optimisation, and standardisation prospects. Provides the formulation of the joint communication-sensing optimisation problem and the Pareto trade-off analysis presented in Section 29.4.

Further Reading

For readers who want to go deeper into specific topics from this chapter.

  • Ambiguity function theory and radar waveform design

    N. Levanon and E. Mozeson, "Radar Signals," Wiley, 2004; and M. A. Richards, J. A. Scheer, and W. A. Holm (eds.), "Principles of Modern Radar: Basic Principles," SciTech Publishing, 2010

    This chapter introduces the ambiguity function as a waveform characterisation tool. These textbooks provide comprehensive treatments of ambiguity function properties, waveform catalogue (chirps, Barker codes, polyphase codes, noise waveforms), and practical radar signal processing algorithms including CFAR detection and sidelobe control.

  • Compressed sensing theory and algorithms

    S. Foucart and H. Rauhut, "A Mathematical Introduction to Compressive Sensing," Springer, 2013; and Y. C. Eldar and G. Kutyniok (eds.), "Compressed Sensing: Theory and Applications," Cambridge University Press, 2012

    The RF imaging section introduces the RIP and LASSO at a high level. These references provide rigorous proofs of recovery guarantees, detailed analysis of algorithms (basis pursuit, OMP, CoSaMP, ISTA/FISTA), and extensions to structured sparsity models relevant to radar imaging.

  • ISAC standardisation and 3GPP activities

    3GPP TR 22.837, "Study on Integrated Sensing and Communication," Release 19, 2024; and ITU-R M.2516, "Framework and Overall Objectives of the Future Development of IMT for 2030 and Beyond," 2023

    ISAC is transitioning from academic research to industrial standardisation. These documents describe the use cases, requirements, and evaluation methodologies being defined for ISAC in 5G-Advanced and 6G. Understanding the standardisation landscape is essential for translating the theoretical foundations of this chapter into practical system design.

  • Deep learning for radar and ISAC

    A. M. Elbir, K. V. Mishra, S. A. Vorobyov, and R. W. Heath Jr., "Twenty-Five Years of Advances in Deep Learning for Radar: A Survey," IEEE Signal Processing Magazine, 2023

    Section 29.5 briefly introduces deep unfolding (LISTA) for RF imaging. This survey covers the broader landscape of deep learning applied to radar signal processing, including target detection, classification, tracking, and waveform design. It provides context for how neural networks are reshaping traditional radar processing pipelines in ISAC systems.

ExercisesCh 30 →