Beamforming Optimization for Robust Sensing and Communication in Dynamic mmWave MIMO Networks

Acquiring accurate channel state information (CSI) at low overhead is crucial for millimeter wave MIMO communications but is challenging in dynamic environments. In this work, we exploit the emerging integrated sensing and communication (ISAC) beamforming technique for concurrent CSI sensing and dat...

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Veröffentlicht in:IEEE journal on selected areas in communications Jg. 43; H. 4; S. 1354 - 1370
Hauptverfasser: Li, Lei, Zhang, Jiawei, Chang, Tsung-Hui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation algorithms
Array signal processing
Beamforming
Communication
Complexity
Convexity
Cramer-Rao bounds
CSI acquisition
Data transmission
Design optimization
Errors
Integrated sensing and communication
ISAC
low-complexity design
Millimeter wave communication
Millimeter waves
MIMO communication
Minimax techniques
Optimization
Optimization techniques
Prediction algorithms
robust beamforming
Robustness
Sensors
Signal processing algorithms
ISSN:0733-8716, 1558-0008
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Zusammenfassung:Acquiring accurate channel state information (CSI) at low overhead is crucial for millimeter wave MIMO communications but is challenging in dynamic environments. In this work, we exploit the emerging integrated sensing and communication (ISAC) beamforming technique for concurrent CSI sensing and data transmission. Despite its low overhead, the corresponding ISAC transmit beamforming design faces a complex trade-off between CSI sensing accuracy and communication interference management. To address this, we formulate the beamforming design as an optimization problem minimizing the maximum Cramér-Rao bound (CRB) of CSI sensing errors subject to the users' worst-case communication rates under CSI errors. To efficiently solve the problem, we step-by-step propose three algorithms. The first algorithm is based on the semidefinite relaxation and successive convex optimization techniques, which can serve as a benchmark algorithm but suffers high computational complexity. To efficiently handle the worst-case objective and rate constraints, we propose a complexity-reduced algorithm based on the primal-dual optimization method and first-order min-max algorithm. Furthermore, we dismiss SDR and employ the block coordinate descent method combined with cheap gradient descent steps to achieve a low-complexity algorithm. Extensive simulations show the proposed ISAC beamforming design and low-complexity algorithms can provide robust communication performance and significantly outperform existing schemes.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2025.3531545