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Efficient Estimation Approach for Carrier Frequency Offset in Multiuser OFDMA Uplink Systems

International Journal of Electronics and Communication Engineering
© 2025 by SSRG - IJECE Journal
Volume 12 Issue 11
Year of Publication : 2025
Authors : Nisarg K. Bhatt, Ravi C. Butani, Rajat G. Pandey, Neetirajsinh J. Chhasatia
10.14445/23488549/IJECE-V12I11P109
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How to Cite?

Nisarg K. Bhatt, Ravi C. Butani, Rajat G. Pandey, Neetirajsinh J. Chhasatia, "Efficient Estimation Approach for Carrier Frequency Offset in Multiuser OFDMA Uplink Systems," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 11, pp. 115-122, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I11P109

Abstract:

The performance of uplink OFDMA systems can be severely impacted by Carrier Frequency Offset (CFO), making it a critical impairment to address. While the Maximum Likelihood (ML) estimator, often implemented with an Alternating Projection (AP) algorithm, provides optimal accuracy, its computational burden, specifically the repeated matrix inversions, hinders practical deployment. The novelty of this work lies in leveraging the Woodbury matrix identity to avoid repeated full inversions, thereby cutting computational load by >90% while retaining ML-level estimation accuracy. Unlike prior studies limited to 4 users, this work extends the evaluation up to 16 users to show scalability. This approach avoids re-computing the entire matrix inverse for each trial CFO value, leading to significant computational savings. Simulation results for a 16-user system demonstrate that our proposed method achieves a Mean Squared Error (MSE) performance nearly identical to that of the ML-AP method, while reducing the required complex multiplications for matrix inversion by over 90%. This work provides a scalable and low-latency solution for CFO estimation, making the optimal ML-based approach more feasible for next-generation wireless systems.

Keywords:

Carrier Frequency Offset, OFDMA Uplink, Multiuser Interference, Maximum Likelihood Estimation, Low-Complexity Estimator, Sparse Signal Processing, 5G Uplink Systems.

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