Cooperative ISAC Beamforming via Deep Learning
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Solution Overview
Problem
Existing ISAC technologies face challenges in securing a line-of-sight path and achieving high transmission rates in millimeter wave bands due to signal loss and path attenuation, particularly in non-line-of-sight conditions, and optimizing multi-base station beamforming is complex and difficult in real-time.
Innovation Solution
A cooperative ISAC method using deep learning-based beamforming, where a central apparatus processes channel and location data through a first network, and each base station processes a second network to optimize beamforming matrices, balancing sensing and communication performance using a weighted sum of Cramer-Rao lower bound and transmission rate as training criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate radar equipment is introduced to each base station, then sensing capability is improved, but installation and implementation cost increases
Solution Approach 1:
The patent combines sensing and communication functions into a unified ISAC system where base stations share radar signal processing capabilities through a centralized controller. Multiple base stations cooperate to perform joint sensing operations, eliminating the need for separate radar equipment at each site while maintaining sensing capability through resource sharing and coordinated beamforming.
Solution Approach 2:
The base stations are designed to perform dual functions: traditional wireless communication and radar sensing. The same hardware infrastructure (antennas, signal processors) is utilized for both purposes, with the centralized controller coordinating sensing operations across multiple base stations to provide universal service without requiring dedicated sensing equipment.
2Measurement precision
If sensing technology requires line-of-sight path, then sensing precision is improved, but adaptability to non-line-of-sight conditions deteriorates
Solution Approach 1:
The patent divides the sensing task across multiple base stations, with each station contributing localized measurements and channel information. The centralized controller aggregates data from multiple base stations to reconstruct target information, enabling non-line-of-sight sensing through cooperative processing of partial observations from different spatial locations.
Solution Approach 2:
The centralized controller acts as an intermediary that collects channel information and location data from multiple base stations, processes this information through neural networks, and generates coordinated beamforming matrices. This intermediary processing enables the system to overcome individual base station limitations and achieve accurate sensing in non-line-of-sight conditions through collaborative computation.
3Productivity
If multi-base station beamforming is optimized in real-time, then transmission rate is improved, but optimization complexity increases
Solution Approach 1:
The patent employs neural networks that are trained offline in advance to learn optimal beamforming strategies. During real-time operation, the trained models rapidly infer beamforming matrices from current channel and location information, avoiding the need for complex real-time optimization computations while maintaining high transmission rates through pre-learned optimal solutions.
Solution Approach 2:
The patent replaces traditional mechanical optimization algorithms with data-driven neural network models. Instead of solving complex optimization problems in real-time, the system uses trained neural networks to directly predict optimal beamforming matrices, substituting iterative computational mechanics with efficient pattern recognition based on previously learned relationships between channel conditions and optimal beamforming strategies.
Data Source
AI summary
Disclosed is a multi-base station-based cooperative integrated sensing and communication (ISAC) method and system using deep learning. A multi-base station-based cooperative ISAC method performed by a cooperative ISAC system may include inputting, to a second network, result data acquired from channel information acquired at each base station and location data of a target through a first network; and outputting a beamforming matrix for ISAC of each base station through the second network.