Tensor Signal Processing for MIMO Communication and Sensing Systems
Fundamentals and Applications
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Beschreibung
This groundbreaking book presents the first comprehensive treatment of tensor signal processing for the critical challenges in modern MIMO communications and integrated sensing and communications (ISAC) systems. Moving decisively beyond matrix-based approaches, it demonstrates how tensor algebra and decompositions unlock unprecedented capabilities for modeling, estimation, and design.
Key Features: Fundamentals to Frontiers: Provides a solid grounding in tensor algebra (CPD, Tucker, TTD) and systematically applies it to core MIMO problems like channel estimation, precoding, decoding, and radar beamforming/DOA estimation.
Cutting-Edge System Coverage: Offers in-depth solutions for the most advanced architectures, including Millimeter-Wave Massive MIMO, XL-MIMO with Dynamic Metasurface Antennas, Movable-Antenna Systems, and Hybrid Analog-Digital MIMO-ISAC.
Practical Tensor Methodologies: Details novel tensor-based algorithms for complex scenarios like wideband frequency-selective channels, time-varying channels, and unified channel/target parameter estimation in ISAC, supported by performance analysis and numerical results.
Future-Proofing Technology: Explores tensor modeling for emerging paradigms such as Near-Field Communications/Localization, IRS-Assisted Systems, UAV Communications/Sensing, and Massive Access.
Integrated Sensing and Communications (ISAC) Focus: Features dedicated chapters on unified tensor frameworks and channel-training aided sensing specifically for MIMO-ISAC, a cornerstone of 6G.
Essential for researchers and engineers, this book establishes tensor processing as the indispensable mathematical foundation for designing, analyzing, and optimizing the ultra-complex wireless and sensing systems of today and tomorrow.
This groundbreaking book presents the first comprehensive treatment of tensor signal processing for the critical challenges in modern MIMO communications and integrated sensing and communications (ISAC) systems. Moving decisively beyond matrix-based approaches, it demonstrates how tensor algebra and decompositions unlock unprecedented capabilities for modeling, estimation, and design.
Key Features: Fundamentals to Frontiers: Provides a solid grounding in tensor algebra (CPD, Tucker, TTD) and systematically applies it to core MIMO problems like channel estimation, precoding, decoding, and radar beamforming/DOA estimation.
Cutting-Edge System Coverage: Offers in-depth solutions for the most advanced architectures, including Millimeter-Wave Massive MIMO, XL-MIMO with Dynamic Metasurface Antennas, Movable-Antenna Systems, and Hybrid Analog-Digital MIMO-ISAC.
Practical Tensor Methodologies: Details novel tensor-based algorithms for complex scenarios like wideband frequency-selective channels, time-varying channels, and unified channel/target parameter estimation in ISAC, supported by performance analysis and numerical results.
Future-Proofing Technology: Explores tensor modeling for emerging paradigms such as Near-Field Communications/Localization, IRS-Assisted Systems, UAV Communications/Sensing, and Massive Access.
Integrated Sensing and Communications (ISAC) Focus: Features dedicated chapters on unified tensor frameworks and channel-training aided sensing specifically for MIMO-ISAC, a cornerstone of 6G.
Essential for researchers and engineers, this book establishes tensor processing as the indispensable mathematical foundation for designing, analyzing, and optimizing the ultra-complex wireless and sensing systems of today and tomorrow.
Explores advanced mathematical tools for designing and improving next-generation wireless systems Contains practical framework to design efficient, high-performance wireless networks for future connectivity Written by leading experts in the field
Autor*in
Ruoyu Zhang
Themen in »Tensor Signal Processing for MIMO Communication and Sensing Systems«
Tensor Signal Processing Wireless Communications Radar Sensing MIMO Systems Machine Learning for Signal Processing