The Need
Modern wireless networks, including 5G and future 6G systems, rely heavily on accurate channel prediction to ensure efficient and reliable communication. Frequency Domain Duplexing (FDD) systems face challenges in accurately predicting downlink channels from uplink channels, particularly in multipath-rich or previously unseen environments. Existing techniques often fail to generalize effectively across diverse scenarios, limiting their scalability and performance. A robust, adaptable solution is essential to optimize the performance of wireless systems in dynamic and heterogeneous conditions.

The Technology
HORCRUX is an innovative, physics-based machine learning system that enhances FDD wireless systems by accurately predicting downlink channels without explicit channel feedback. The system uses multiple neural networks to decompose uplink channels into sub-channels and generate coarse variable estimates, followed by a fast optimization framework to fine-tune these estimates for precise downlink channel prediction. HORCRUX is highly scalable, performing well across diverse wireless environments without prior knowledge of specific conditions. It applies to both single-antenna and MIMO systems and is implemented using software-defined radios. Evaluation shows that HORCRUX delivers a ~8 dB improvement in downlink channel prediction accuracy over existing methods.

Commercial Applications

• Telecommunications: Enhances downlink channel prediction for 5G and 6G networks, ensuring high performance and reliability.
• IoT Networks: Optimizes communication in dense IoT ecosystems, improving connectivity and data transfer efficiency.
• Autonomous Vehicles: Supports robust V2X communication in dynamic, multipath-rich environments.
• Smart Cities: Facilitates efficient wireless communication for smart infrastructure and urban applications.
• Wireless Research and Development: Serves as a foundational tool for exploring and testing advanced wireless communication systems.

Benefits/Advantages

• High Accuracy: Provides ~8 dB improvement in downlink channel prediction accuracy compared to state-of-the-art methods.
• Generalizability: Operates effectively across diverse wireless environments without prior training or adaptation.
• Scalability: Applicable to both single-antenna and MIMO systems, supporting next-generation network demands.
• Efficiency: Combines machine learning and optimization frameworks for fast and accurate predictions.
• Future-Ready: Essential for the success and optimization of emerging 5G, 6G, and beyond wireless networks.