The Need
Ultra-wideband communication systems, including mmWave and optical wireless networks, demand highly accurate and low-latency channel estimation to maintain throughput and reliability. Existing methods, such as hybrid correlation and least squares estimators, suffer from high computational complexity and degraded performance in noisy environments. These limitations hinder efficient resource allocation and adaptive modulation, especially in 5G/6G and free-space optical systems where environmental perturbations and feedback delays are common.

The Technology
This invention, developed by OSU researchers, introduces a novel channel estimation method that leverages vectorized least squares estimation and subspace noise filtering to enhance accuracy and reduce computational load. By transforming matrix operations into element-wise computations and applying LDL matrix factorization, the method significantly accelerates processing. Additionally, it dynamically adjusts SINR and channel capacity using precomputed error-based modifications, enabling robust CSI determination under varying noise conditions. This approach ensures stable, high-throughput communication even in challenging environments.

Potential Commercial Applications
• 5G/6G wireless infrastructure and user equipment
• Free-space optical communication systems
• Satellite-ground communication links
• High-speed industrial IoT networks
• Defense and aerospace communication platforms

Benefits/Advantages
• Substantial reduction in computational complexity via vectorization and matrix factorization
• Enhanced estimation accuracy in noisy and turbulent environments
• Real-time SINR and channel capacity adjustment for adaptive transmission
• Improved throughput in both TDLA and TDLC fading channel scenarios
• Scalable to both RF and optical communication systems