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Communication-Assisted Sensing in 6G Networks
Exploring the mutual benefit and reciprocity of sensing and communication (S\&C) functions is fundamental to realizing deeper integration for integrated sensing and communication (ISAC) systems.
Sensing With Random Signals
Radar systems typically employ well-designed deterministic signals for target sensing.
Sensing as a Service in 6G Perceptive Mobile Networks: Architecture, Advances, and the Road Ahead
Sensing-as-a-service is anticipated to be the core feature of 6G perceptive mobile networks (PMN), where high-precision real-time sensing will become an inherent capability rather than being an auxiliary function as before.
Symbol-Level Precoding for MU-MIMO System with RIRC Receiver
The problem is solved using the alternating optimization (AO) method, and the optimal solution structures for transmit precoding and receive combining matrices are derived by using Lagrangian and Karush-Kuhn-Tucker (KKT) conditions, based on which, the original problem is transformed into an equivalent quadratic programming problem, enabling more efficient solutions.
Waveform Design for Communication-Assisted Sensing in 6G Perceptive Networks
In particular, a pair of transmission schemes, namely, separated S&C and dual-functional waveform designs, are proposed to optimize the sensing QoS under the constraints of the rate-distortion and power budget.
Rethinking Estimation Rate for Wireless Sensing: A Rate-Distortion Perspective
Wireless sensing has been recognized as a key enabling technology for numerous emerging applications.
Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security
The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation.
Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation
Then, we establish a unified framework for ISAC resource allocation, where the fairness and the comprehensiveness optimization criteria are considered for the aforementioned sensing services.
