Abstract—This paper evaluates classical, end-to-end deep learning, and hybrid Orthogonal Frequency-Division Multiplexing (OFDM) receivers under a 60 GHz Third-Generation Partnership Project Technical Report 38.901 Clustered Delay Line Model A (3GPP TR 38.901 CDL-A) channel with explicit synchronization impairments. Four receivers are compared: no equalization, pilot-assisted least-squares channel estimation with Least‑Squares Zero‑Forcing (LS-ZF), an end-to-end regression Deep Neural Network (DNN), and a hybrid Least‑Squares‑aided Deep Neural Network (LS‑DNN) detector. The main contribution is a Carrier-Frequency-Offset (CFO)-aware comparison of whether a simple fully connected DNN is more effective as a direct receiver replacement or as a post-equalization refinement stage. The impairment model includes frame-wise common phase error and deterministic normalized CFO. Performance is evaluated from 0−50 dB using Bit Error Rate (BER), symbol Mean Squared Error (MSE), Error Vector Magnitude (EVM), receiver runtime, and Wilson confidence intervals over {0, 0.01, 0.02, 0.05, 0.06, 0.10}. Results indicate that LS-ZF remains the strongest BER baseline, while the hybrid detector is BER-competitive at low-to-moderate CFO and improves symbol-fidelity metrics in several CFO-impaired cases. At , all receivers degrade sharply, showing limited robustness outside the DNN CFO training range. Overall, the results suggest that deep learning is more useful as a structured post-equalization refinement stage than as a universal end-to-end OFDM receiver replacement.
 
Keywords—Orthogonal Frequency Division Multiplexing (OFDM), deep learning, carrier frequency offset, clustered delay line channel, hybrid receiver, millimeter wave, signal detection

Cite: Noever Q. Saile and Lawrence Materum, "Carrier-Frequency-Offset (CFO)-aware Deep Learning-based Orthogonal Frequency-Division Multiplexing (OFDM) Detection under a 60 GHz Clustered Delay Line Channel," Journal of Communications, vol. 21, no. 5, pp. 631-643, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).