“Compressed Sensing-Based Channel Estimation for Intelligent Reflective Surface Aided mmWave Communication,” by Fitsum Zerfu et. al. tackles a significant challenge in next-generation wireless networks. Intelligent Reflective Surfaces (IRS) are an up-and-coming “enabling” technology for improving the performance of future wireless networks, but acquiring accurate channel state information (CSI) for IRS-aided communication systems remains a hurdle. Because IRS elements are passive (lacking signal processing capabilities), estimating the cascaded channel between the user equipment, the surface, and the base station requires measuring a very large number of parameters; this makes conventional channel estimation approaches both costly and inefficient.
The authors address this challenge by proposing a Multiple Measurement Vector based Compressive Sensing approach: they exploit structural sparsity inherent in mmWave channels, and by projecting the data onto the signal subspace they filter out noise more effectively. This multifaceted method achieved a lower complexity in comparison to similar existing estimation techniques.
To learn more, read the paper:
Zerfu, F., Gelmecha, D. J., Meko, S. F., Kassa, H. B., & Kornegay, K. T. (2025). Compressed Sensing-Based Channel Estimation for Intelligent Reflective Surface Aided mmWave Communication. IEEE Access, 13, 197599–197608. https://doi.org/10.1109/ACCESS.2025.3630088
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