Enhanced Channel Estimation in RIS-Aided OFDM Systems Using DFT-Based Pilot Optimization under Practical Hardware Constraints

Résumé

Reconfigurable Intelligent Surface (RIS) promises large spectral-efficiency gains for Orthogonal Frequency-Division Multiplexing (OFDM) systems but introduces significant channel-training challenges when the number of RIS elements grows. This paper proposes a practical, Discrete Fourier Transform (DFT)-based pilot optimization framework to reduce pilot overhead for channel estimation in RIS-aided OFDM links under realistic constraints, including finite RIS element counts and quantized phase shifts. Subsampled DFT pilots and sparse recovery reconstruct a truncated sparse delay-domain channel from a few measurements, greatly reducing pilot overhead while preserving estimation accuracy. Numerical results for a representative system size (N = 128) demonstrate significant reductions in pilot-symbol requirements compared to conventional full-dimensional training while maintaining estimation accuracy. Furthermore, conventional pilot overhead is observed to scale approximately linearly with RIS size, whereas the proposed scheme scales with the number of dominant channel coefficients. This paper analyzes trade-offs among Pilot Ratio (PR), measurement budget, and recovery performance, and provides guidelines for practical pilot-budget selection. The proposed approach is robust to quantized RIS phase shifts and offers improved spectral efficiency and latency benefits for large-scale RIS deployments. The study further highlights existing limitations, the need for ensemble-level validation, and directions for joint delay–angle sparsity exploitation and adaptive pilot allocation. This work introduces a low-complexity estimation method using DFT pilots and Least Squares (LS), accounting for quantized RIS phase shifts and frequency-selective fading. Evaluated through Normalized Mean Square Error (NMSE) and pilot overhead, the approach provides a scalable, practical solution for RIS-aided OFDM in next-generation networks.

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