Part 3: Cell-Free and Distributed MIMO
Chapter 16: Joint Detection and Positioning
Research~250 min
Learning Objectives
- Relate geometric ranging (TOA, TDOA, AOA, RSSI) to statistical estimation bounds
- Derive the Fisher information matrix for a multi-anchor positioning system
- Formulate UL-TDOA and multi-RTT positioning on a distributed cell-free infrastructure
- Jointly detect user data and estimate user position from the same uplink waveform
- Compare iterative (decoupled) and joint (ML/EM) detection-positioning schemes
- Compute the Position Error Bound (PEB) and the Angle Error Bound (AEB)
- Characterize the rate-PEB tradeoff for Integrated Sensing and Communication (ISAC)
- Apply cell-free APs to distributed target sensing and beampattern design
Sections
Prerequisites
Cell-free system model and macro-diversity (MIMO Ch. 11)User-centric clustering and distributed cooperation (MIMO Ch. 12-13)Cell-free performance analysis and achievable rates (MIMO Ch. 15)Channel estimation, pilots, and synchronization (MIMO Ch. 3)MMSE and ML estimation theory (any statistical signal processing text)Basic Cramer-Rao bound and Fisher information
💬 Discussion
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