cs.LGOct 14, 2026

Comparative Evaluation of Deep-Learning based DoA Estimators for Real-Time ISAC Beamforming

Authors: Anjali K. S., Manoj Kumar Panda, Chandramani Singh

Organizations: Department of Electronics and Communication Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India · Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India

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Abstract

Integrated Sensing and Communication (ISAC) systems rely on accurate Direction-of-Arrival (DoA) estimation for effective beamforming and spectral-efficiency enhancement. While numerous classical and deep-learning-based DoA estimation methods have been reported in the literature, their suitability for real-time ISAC operation remains insufficiently understood because practical deployment requires consideration of both estimation accuracy and computational latency. This paper presents a unified evaluation framework for systematically comparing classical and learning-based DoA estimators within an ISAC beamforming pipeline. Representative classical and learning-based DoA estimators, together with an ensemble architecture, are integrated with Singular Value Decomposition (SVD)-based beamforming and evaluated under identical operating conditions. The impact of DoA estimation accuracy, inference latency, beamforming-weight generation time, and resulting effective spectral efficiency is investigated. Results show that different estimators occupy distinct operating points in the accuracy-latency-performance design space. Although learning-based approaches substantially reduce estimation time compared with MUSIC, the overall processing latency increasingly becomes dominated by the computational cost of SVD-based beamforming. Among the evaluated methods, the Regression Convolutional Neural Network (RCNN) exhibits a favorable balance between estimation accuracy, computational efficiency, and beamforming performance, making it a promising candidate for real-time ISAC deployment. The findings further suggest that future low-latency ISAC systems may benefit from beamforming strategies that reduce or avoid computationally intensive SVD-based beamforming-weight generation.

Figures & tables

MethodsReferencesLimitations
Traditional (e.g., MUSIC, ESPRIT)[ 2 , 3 , 4 , 5 , 6 ]• High execution time. • Less accurate in noisy conditions.
Deep Learning (Classification)[ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]• Limited to predefined angle grids • Inaccurate for off-grid DoAs. • High execution time and training time. • Requires large labeled datasets.
Deep Learning (Regression)[ 16 , 17 , 18 , 19 , 20 , 21 , 22 ]• Sensitive to training data distribution and outliers. • Vulnerable to unstable predictions in noisy conditions.
TABLE I: DoA Estimation Methods and Their Limitations.
Fig. 1: Hybrid beamforming ISAC Scenario
Model NameCovariance matrix inputInput SizeLayer type
CLASS [ 8 ]Full matrix (real, imaginary, phase)16×16×316\times 16\times 34 Convolutional + FC
RDNN [ 20 ]UT elements (Real and imaginary)1×2401\times 240FC only
LDNN [ 21 ]Average of UT elements along super-diagonals (Real and imaginary)1×301\times 30FC only
RCNN [ 22 ]Full Covariance matrix16×1616\times 161 Convolutional +FC
TABLE II: Model Architectures and Configurations.
Fig. 2: RMSE vs. SNR performance with sinusoidal signals.
Fig. 3: RMSE vs. SNR performance with LFM signals.
Fig. 4: Transmitter beam pattern for BS transmitting sine waves
Fig. 5: Transmitter beam pattern for BS transmitting LFM signals.
Fig. 6: Prediction time needed for each method of DoA estimation
Fig. 7: Beamforming weight generation time for each method
Fig. 8: Beamforming weight generation time Vs. SVD time analysis
Fig. 9: Spectral efficiency for DL communication for sine wave
Fig. 10: Spectral efficiency for DL communication for LFM signal
MethodsDoA RMSEDoA Pred.Time(s)ESE (bps/Hz)
CLASS0.10512.55×10−042.55\times 10^{-04}9.9738
RDNN0.28094.6×10−54.6\times 10^{-5}10.2694
LDNN17.70311.22×10−41.22\times 10^{-4}2.2426
MUSIC0.80110.0018.8898
RCNN0.18872.2805×10−5\bf{2.2805\times 10^{-5}}10.4252
Ensemble0.17445.4×10−55.4\times 10^{-5}10.2738
TABLE III: Performance comparison at 10 dB using LFM signals.
Fig. 11: Performance evaluation based on distance from ideal operating point (LFM signals)

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