Channel estimation in OFDM systems requires minimal complexity with one-tap equalizers and it is generally performed based on pilot symbols (PS) using least squares (LS). However, in a practical environment where impulsive noise may be present, this method may not be effective; furthermore, the minimal complexity required depends on cyclic prefixes (CP), which must be sufficiently large to cover the channel impulse response. In contrast, the use of PS and CP decreases the useful information that can be conveyed in an OFDM frame, thereby degrading the spectral efficiency of the system. In this context, we propose the use of quantum machine learning algorithms for channel estimation in OFDM systems with a reduced number of PS, without PS, and without CP. Our approach involves adapting classical machine learning models, specifically support vector regression and K-means clustering, to their quantum counterparts by integrating quantum kernels and encoding strategies suitable for channel modeling. The performance of the resulting quantum models is evaluated in comparison to LS and classical learning-based estimators. The viability of our approach is substantiated by computational simulation results obtained in frequency-selective channel models with the presence of non-Gaussian impulsive noise interfering with the symbols.
Channel estimation in OFDM systems requires minimal complexity with one-tap equalizers and it is generally performed based on pilot symbols (PS) using least squares (LS). However, in a practical environment where impulsive noise may be present, this method may not be effective; furthermore, the minimal complexity required depends on cyclic prefixes (CP), which must be sufficiently large to cover the channel impulse response. In contrast, the use of PS and CP decreases the useful information that can be conveyed in an OFDM frame, thereby degrading the spectral efficiency of the system. In this context, we propose the use of quantum machine learning algorithms for channel estimation in OFDM systems with a reduced number of PS, without PS, and without CP. Our approach involves adapting classical machine learning models, specifically support vector regression and K-means clustering, to their quantum counterparts by integrating quantum kernels and encoding strategies suitable for channel modeling. The performance of the resulting quantum models is evaluated in comparison to LS and classical learning-based estimators. The viability of our approach is substantiated by computational simulation results obtained in frequency-selective channel models with the presence of non-Gaussian impulsive noise interfering with the symbols.
The integration of Orthogonal Frequency-Division Multiplexing (OFDM) with contemporary communication systems faces challenges, notably the requirement of a cyclic prefix (CP), which affects spectral efficiency. This paper proposes a novel quantum OFDM transmission method that utilizes quantum mechanisms for encoding, decoding, and channel processes, replacing the need for Discrete Fourier Transforms (DFT) and Inverse Discrete Fourier Transform (IDFT). This advanced quantum OFDM model eliminates the CP requirement and, when compared to a standard quantum transmission scheme, shows improved accuracy in quantum state measurement, especially in channels with higher resistivity. The results have been validated using simulations on IBM’s Qiskit platform.
This work presents an over-the-air (OTA) OFDM software-defined radio (SDR) prototype using GNU Radio and two HackRF One devices to evaluate classical and quantumkernel channel estimation.LS, LMMSE, Support Vector Regression (SVR), and quantum-kernel SVR (QSVR) are compared on identical received IQ samples under controlled Gaussian and impulsive noise.BER, packet recovery, latency, and processing throughput are evaluated.Results show that the kernel-based estimators remain competitive with conventional approaches, demonstrating the feasibility of quantum-kernel channel estimation in a complete over-the-air SDR receiver.
Channel estimation is one of the most fundamental and performance-limiting operations in fifth-generation (5G) and emerging sixth-generation (6G) wireless networks, where ultra-massive MIMO arrays, millimetre-wave and terahertz carrier frequencies, and non-Gaussian hardware-induced noise combine to render classical least-squares and minimum mean squared error estimators inadequate. Deep learning has narrowed the performance gap, yet it offers no information-theoretic guarantee on how close any particular network architecture is to the fundamental noise floor. This paper introduces the AI-Quantum Channel Modeling and Estimation (AQCME) framework, which integrates variational quantum circuits, quantum signal processing, and a quantum eigenspace denoising operator into a unified trainable architecture for simultaneous channel estimation and noise suppression in MIMO-OFDM systems. The defining theoretical result is a quantum Cramer-Rao lower bound on normalised mean squared error, grounded in the quantum Fisher information matrix of the encoded channel state, together with a rigorous proof that the AQCME estimator approaches this bound as training data grows – to the best of our knowledge the first constructive achievability result of its kind in the wireless channel estimation literature. The framework is supported by formal analysis of quantum channel state preparation, quantum kernel universality with an explicit barren-plateau trainability discussion, ergodic spectral efficiency, Nakagami-m outage probability, and convergence of the hybrid quantum-classical gradient descent. Monte Carlo simulations over 50,000 channel realisations, reported with 90 percent confidence intervals throughout, validate every theoretical prediction. Ablation studies across circuit depths and qubit counts, a hardware-noise sweep over gate error rates from 1e-4 to 1e-2, and hyperparameter sensitivity analysis confirm robustness. At 20 dB signal-to-noise ratio, AQCME reduces normalised mean squared error by 4.8 dB over MMSE and by 2.1 dB over the best deep learning baseline; under emulated near-term noisy quantum hardware conditions it still outperforms MMSE by 2.6 dB. All quantum results in this work are obtained by exact classical statevector emulation of the variational circuit; no physical quantum processor was used, and reported hardware-noise behaviour is modelled by a depolarising channel. Spectral efficiency improves by up to 22.4 percent in massive MIMO scenarios, the URLLC outage target of 1e-5 is met at 6.2 dB lower signal-to-noise ratio than with MMSE, and inference complexity scales logarithmically with antenna count rather than quartically as with MMSE matrix inversion.
Quantum communication systems are essential for secure information transmission, but their performance is significantly impeded by complex hybrid quantum noise (HQN) in Gaussian quantum channels. Accurately modeling this noise is crucial for optimizing channel achievable rates, a problem compounded by the complex nature of hybrid noise, which combines quantum shot noise and classical additive-white-Gaussian noise (AWGN) and is typically represented as an infinite mixture of Gaussian distributions. This work focuses on optimizing the HQN model to improve the achievable rate of Gaussian quantum channels using Machine Learning (ML) optimized clusters. The work specifically leverages Gaussian mixture model (GMM) and the Expectation-Maximization (EM) algorithm to model the complex noise characteristics of quantum channels. The study proposes modeling of hybrid noise as an infinite mixture of Gaussian distributions weighted by Poissonian parameters, with a novel cluster reduction technique that minimizes the number of Gaussian components while maintaining accuracy within acceptable error tolerances. Simulation results demonstrate that the GMM-EM enhanced clustering method significantly improves channel achievable rates in general quantum communication and satellite-based quantum communication systems. The GMMEM method substantially outperforms conventional clustering techniques, including K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms, in terms of maximizing achievable rates and characterizing noise accuracy. This advancement provides a practical and efficient solution for real-time quantum noise modeling, representing a significant improvement over existing state-of-the-art approaches in quantum communication network optimization
Recently, extensive research attention has been dedicated to enabling Orthogonal Frequency-Division Multiplexing (OFDM) waveforms to be compatible with a modern communication system. Encoding data as OFDM wavelengths still has a lot of problems, like the peak-to-average power ratio (PAPR) and the cyclic prefix (CP), which are important factors that affect how efficiently the spectrum is used. To meet the quality-of-service requirements imposed by communication system applications, this paper proposes to replace the classical encoding and decoding schemes, classical channel, discrete Fourier transform (DFT), and inverse discrete Fourier transform (IDFT) with their classical counterparts. This new quantum OFDM transmission scheme allows for the preparation of a quantum OFDM symbol without the need to incorporate a CP. To validate the accuracy of the suggested quantum OFDM transmission scheme, we compared it with the most widely recognised reference quantum transmission scheme. We have demonstrated that increasing the channel resistivity results in a higher probability of correctly measuring the quantum state in the quantum OFDM transmission scheme compared to the reference quantum transmission scheme. The results are verified by IBM's Qiskit.
Accurate channel estimation is a fundamental requirement for high-performance wireless communication systems, where conventional deep learning approaches often suffer from excessive trainable parameters and high memory cost as antenna and subcarrier dimensions scale up. This paper proposes a quantum machine learning (QML)-based channel estimation framework that addresses this scalability challenge through a compact hybrid quantum-classical architecture. An 8-qubit quantum neural network (QNN) based on a parameterized quantum circuit (PQC) encodes received pilot-domain features using both Cartesian and polar components through Hadamard initialization and rotation-based quantum encoding, and learns channel representations using trainable variational rotations and controlled-Z (CZ) entanglement. The Pauli-Z measurement outputs are then processed by a lightweight classical dense head to produce the final channel estimate. The proposed QNN is compared with a classical deep neural network (DNN) and a QNN without hidden dense layers over a 2 × 1 Rayleigh fading multiple-input single-output (MISO) channel across an SNR range of 10 dB to 30 dB. Simulation results show that the proposed hybrid QNN achieves the lowest normalized mean square error (NMSE) across the entire evaluated SNR range while using only 1,012 trainable parameters, compared with 3,356 for the DNN. At 30 dB SNR, the proposed QNN achieves the lowest NMSE of approximately − 20.24 dB, compared with approximately − 17.54 dB for the DNN baseline. An ablation study further confirms that the dense post-processing head is essential for accurate regression from raw quantum measurement outputs. These results suggest that compact hybrid quantum-classical estimators offer a promising direction for accurate and parameter-efficient channel estimation in next-generation wireless communication systems.