Jul 2026· International Journal of Science, Strategic Management and Technology· Vol 02, pp. 1-10· 0 citations
TL;DR
Experimental evaluation demonstrates significant improvements in estimation accuracy, spectral efficiency, latency reduction, and communication reliability compared with traditional estimation methods, indicating that AI-based channel estimation will become a fundamental component of future intelligent communication systems and 6G wireless networks.
Abstract
The increasing demand for high-speed wireless communication services, coupled with the deployment of advanced technologies such as Massive Multiple-Input Multiple-Output (MIMO), millimeter-wave communications, Internet of Things (IoT), and Sixth Generation (6G) networks, has significantly increased the complexity of wireless channel environments. Accurate channel estimation plays a critical role in ensuring reliable communication, efficient resource utilization, and high-quality service delivery. Conventional channel estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) often struggle to provide optimal performance in highly dynamic and complex communication environments due to nonlinear channel characteristics, mobility, and interference. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving channel estimation accuracy through intelligent learning and adaptive optimization. This paper presents a comprehensive study of AI-based channel estimation techniques and proposes an Intelligent Deep Learning-Based Channel Estimation Framework (IDL-CEF) designed to enhance wireless communication performance. The proposed framework integrates deep neural networks, machine learning algorithms, adaptive signal processing, and real-time channel prediction mechanisms. Experimental evaluation demonstrates significant improvements in estimation accuracy, spectral efficiency, latency reduction, and communication reliability compared with traditional estimation methods. The findings indicate that AI-based channel estimation will become a fundamental component of future intelligent communication systems and 6G wireless networks.
The development of 5G and higher wireless communications systems has posed a great need on intelligent transmission techniques capable of supporting ultra-high data rates, low latency, high spectral efficiency, and dependable connectivity in dynamic networked environments. Beamforming is one of the emerging technologies that is critical in improving signal directionality and reducing interference, especially in millimeter-wave and massive multiple-input multiple-output (MIMO) communication systems. The traditional methods of beamforming are however usually challenged by issues associated with a high computational complexity, high energy usage and low adaptability to the fast-varying channel conditions. Through the research, a Hybrid Beaming Technique to enhance the aspect of performance in 5G and above communication networks using Machine Learning, combining Artificial Neural Networks (ANN) and Deep Convolutional Neural Networks (DNN) to optimize the beam selection, channel estimation, and adaptive resource allocation in the 5G and above communication networks. Within the framework proposed, ANN is used as a predictive analysis of channel state information and intelligent beam weight optimization, and CNN is used as a feature extraction of complex spatial channel patterns and interference mapping, in order to make accurate decisions of beam steering in real time. Incorporating the energy efficiency of analog beam control with the flexibility and precision of digital beam processing, the hybrid beamforming architecture forms a robust and scalable communication model. The results of the simulations have shown that the proposed ANN-DNN-based hybrid beaming technique is significantly better in terms of throughput, signal-to-interference-plus-noise ratio (SINR), spectral efficiency, and coverage reliability than traditional beamforming techniques. The framework also exhibits less beam misalignment and greater flexibility in case of user mobility and dense deployment. The proposed intelligent hybrid beaming model presents a bright solution to next-generation wireless systems, such as 5G networks, to support high-capacity, low-latency, and energy-efficient communication infrastructures to future smart and connected environments.
B Jaya, Ette Hari Krishna· International journal of com...· 0 citations
Orthogonal Frequency Division Multiplexing (OFDM) is the core physical-layer modulation technique used in modern wireless communication systems due to its high spectral efficiency and robustness against frequency-selective fading. In fifth-generation (5G) systems, channel estimation for OFDM is mainly performed using classical pilot-based methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) estimation. Although these techniques are computationally efficient and well standardized, their performance degrades in practical fading environments, particularly under high mobility, sparse pilot configurations, and higher-order modulation schemes. This survey reviews the OFDM physical layer and traditional channel estimation techniques in 5G systems and analyzes their limitations using performance results over AWGN and Rayleigh fading channels. The paper further examines recent artificial intelligence and machine learning (AI/ML)-based channel estimation approaches proposed for sixth-generation (6G) systems, highlighting their improved robustness and adaptability in dynamic channel conditions. A comparative analysis between classical 5G and AI/ML-based 6G channel estimation methods is presented in terms of bit error rate performance, adaptability, and complexity, followed by a discussion on the advantages and limitations of intelligent channel estimation for future wireless networks.
Rizwan H. Alad, Hardip K. Shah, Nirav Desai et al.· Discover Electronics· 0 citations
The increasing demand for high data rates and massive connectivity in next-generation wireless systems has led
to the adoption of non-orthogonal multiple access (NOMA) techniques. However, conventional successive interference
cancellation (SIC)-based receivers suffer from performance degradation due to error propagation and nonlinear
interference effects, particularly in multi-user MIMO environments. To address these limitations, this work proposes a
feedback-based deep neural network (FDNN) receiver for signal detection in MIMO-NOMA systems. The proposed model
integrates deep learning capabilities with iterative interference cancellation, enabling improved nonlinear signal
separation and robustness against noise and inter-user interference. A comprehensive simulation framework is developed
using Python to evaluate system performance under varying signal-to-noise ratios (SNR) and power allocation conditions.
The results demonstrate that the FDNN-based receiver achieves significantly lower bit error rates (BER) compared to
traditional SIC methods, especially in scenarios with small power differences between users. This study highlights the
effectiveness of deep learning in enhancing receiver design for future wireless communication systems.
D. Sharma, Nidhi Sisodiya· International Journal for Re...· 0 citations
Wireless communication systems are constantly evolving from 5th generation (5G) networks to beyond-5G (B5G) and 6th generation (6G) networks, presenting unprecedented requirements for highly reliable and efficient reception of signals. Advanced applications like autonomous vehicles, industrial automation, remote healthcare, extended reality and integrated sensing demand ultra-reliable communications with low latency (URLLC), massively connected devices and quality of service. The multipath fading and co-channel interference, dynamic user mobility, blockage and penetration losses of millimeter-wave and terahertz frequencies, and rapidly changing propagation environments, however, severely affect the reception of signals in modern wireless systems. These impairments affect signal to noise ratio (SNR), signal to interference-plus-noise ratio (SINR), bit error rate (BER), throughput, and overall communication reliability. Artificial Intelligence (AI) has become a game-changer that can tackle such challenges with data-driven and adaptive optimization. In this review, the authors comprehensively examine the roles that Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable Artificial Intelligence (XAI) play in improving the reception of signals in 5G and future mobile networks. It is used in key applications such as channel estimation, equalization, beamforming, blockage prediction, interference mitigation, predictive handover, power control, and intelligent resource allocation. The literature provides ample evidence of the significant improvements of AI-based techniques over traditional methods in terms of SINR, BER, throughput and reliability in highly dynamic environments.
A. Jamil· International Journal of Mul...· 0 citations
In millimeter-wave Integrated sensing and communication (ISAC) systems, a high-resolution Doppler velocity estimation is crucial for localizing multiple tightly coupled mobile users in the environment for subsequent communication. Conventional subspace methods, such as the Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), provide high resolution but incur high complexity and latency, limiting their use in real-time hardware. This work proposes a lowcomplexity end-to-end multi-layer perceptron (MLP) for Doppler estimation for IEEE 802.11ad ISAC waveform. The proposed MLP achieves up to 59.8% lower RMSE than classical ESPRIT while using fewer slow-time packets and up to $20.4 \times$ lower latency on the system-on-chip (SoC). A multi-model extension further improves robustness across signal-to-noise ratio (SNR), providing an additional 56% gain at low SNR by adaptively selecting model parameters. These results demonstrate that MLPbased estimation provides a scalable, efficient alternative to classical subspace methods for real-time ISAC systems.
Samarth Bhardwaj, Aakanksha Tewari, Syed Asrar ul Haq et al.· International Conference on...· 0 citations
Integrated sensing and communication (ISAC) technology enables the joint integration of communication and sensing functions through the efficient utilization of spectrum/energy resources. By further incorporating with the reconfigurable intelligent surfaces (RISs), the wireless propagation environment of ISAC systems can be dynamically controlled, thereby enhancing the overall sensing and communication (SAC) performance. In this context, accurate channel estimation is a fundamental prerequisite for the efficient operation of the RIS-assisted ISAC systems. However, the strong coupling between SAC signals from direct and reflected channel links, as well as severe noise interference, limits the SAC channel estimation accuracy. This paper proposes a novel two-stage channel estimation scheme for RIS-assisted ISAC systems, where the direct and reflected SAC channels are respectively estimated in the first and second stages. To mitigate the negative effects of noise interference in each estimation stage, an extreme learning machine-based feature purification module is precisely designed, improving the quality of received SAC signals and generating purified channel features. Then, the dedicated deep neural network adopts the purified channel features to estimate SAC channels. Simulation results demonstrate that, under different signal-to-noise ratio conditions and channel dimensions, the proposed scheme achieves superior estimation accuracy and strong robustness compared to the benchmark methods.
Gang Liu, Yu Liu, Zelin Zheng· Electronics· 0 citations
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