Advanced Propagation Channel Estimation Techniques for Sixth-Generation (6G) Wireless Communications

Topic Information

Dear Colleagues,

The evolution toward sixth-generation (6G) wireless networks is redefining the wireless propagation environment, driven by ambitious goals such as ultra-high data rates, ultra-low latency, massive connectivity, and integrated sensing and communication (ISAC). Enabling technologies—including terahertz (THz) communications, extremely large-scale MIMO (XL-MIMO), reconfigurable intelligent surfaces (RIS), near- and far-field hybrid transmission, and orthogonal time frequency space (OTFS) modulation—are introducing unprecedented complexity to wireless channel behavior.

In this context, robust channel estimation and accurate physical parameter extraction (e.g., delay, angle, Doppler, and polarization) are key to ensuring system reliability and efficiency. These techniques not only support reliable data transmission but also enable high-resolution localization, environmental sensing, and dynamic spectrum optimization—cornerstones of future 6G networks.

This Topic invites high-quality original research and review articles focusing on advanced estimation and simulation techniques for wireless propagation channels and parameters, especially in the context of 6G-enabling technologies and complex environments. We welcome interdisciplinary contributions from both academia and industry, covering theoretical analysis, algorithm design, simulation frameworks, and hardware feasibility studies.

Topics of interest include, but are not limited to, the following:

• Channel estimation and parameter extraction in THz/sub-THz and mmWave systems;
• Near-field and hybrid-field channel estimation for XL-MIMO systems;
• Delay-Doppler domain channel estimation for OTFS modulation;
• Sparse, structured, and compressive sensing-based estimation techniques;
• Deep learning and data-driven approaches to channel and parameter estimation;
• Channel estimation and beamforming for RIS-assisted communication;
• Wireless-based localization and sensing in ISAC systems;
• Dataset generation, channel simulation platforms, and reproducible frameworks;
• Trade-offs in estimation accuracy, complexity, and latency.

This Topic aims to foster collaboration across wireless communication, signal processing, and sensing communities and promote novel insights that will shape the future of channel modeling and estimation for 6G.

Dr. Han Wang
Prof. Dr. Fangqing Wen
Prof. Dr. Xianpeng Wang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Future Internet futureinternet	4.6	10.0	2009	15 Days	CHF 1800	Submit
Information information	4.3	8.2	2010	18.7 Days	CHF 1800	Submit
Microwave microwave	-	-	2025	15.0 days *	CHF 1000	Submit
Network network	3.7	8.0	2021	23.2 Days	CHF 1200	Submit
Signals signals	2.9	5.9	2020	28.6 Days	CHF 1200	Submit
Technologies technologies	5.2	6.7	2013	17 Days	CHF 1800	Submit
Telecom telecom	2.8	5.2	2020	22.8 Days	CHF 1400	Submit

* Median value for all MDPI journals in the first half of 2026.

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Published Papers (6 papers)

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All Electronics Future Internet Technologies Telecom Network Microwave Information Signals

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11 pages, 9969 KB  

Open AccessArticle

Semi-Blind Channel Estimation and Symbol Detection for Double RIS-Aided MIMO Communication System

by Mingkang Qu, Honggui Deng, Ni Li and Wanqing Fu

Electronics 2026, 15(9), 1781; https://doi.org/10.3390/electronics15091781 - 22 Apr 2026

Viewed by 309

Abstract

Reconfigurable intelligent surfaces (RISs) are regarded as a transformative technique for future wireless networks. Currently, the majority of research efforts have focused on channel estimation scenarios in communication systems assisted by a single passive RIS. However, single-RIS-assisted systems suffer from limited coverage performance, [...] Read more.

Reconfigurable intelligent surfaces (RISs) are regarded as a transformative technique for future wireless networks. Currently, the majority of research efforts have focused on channel estimation scenarios in communication systems assisted by a single passive RIS. However, single-RIS-assisted systems suffer from limited coverage performance, with significant performance degradation observed in dense obstacle environments. To mitigate the adverse impacts imposed by environmental factors, a dual-RIS-assisted communication system exhibits superior adaptability to practical scenarios. This work focuses on investigating such a system. It is worth noting that fully passive RISs lack the capability to process signals independently. Furthermore, when employing pilot-aided algorithms to acquire channel state information (CSI), wireless systems often encounter challenges arising from large channel matrix dimensions, thereby leading to substantial pilot overhead. To address the aforementioned issues, this paper proposes a novel semi-blind channel estimation method for multiple-input multiple-output (MIMO) systems aided by double reconfigurable intelligent surfaces (D-RISs). Specifically, we construct two tensor models, namely the Parallel Factor (PARAFAC) model and the Parallel Tucker2 model, for the received signal in two separate stages. By means of tensor decomposition, the joint channel estimation and symbol detection problem is reformulated as a least squares problem and solved using a two-stage algorithm. In the first stage, the ALS algorithm is adopted to estimate the transmitted symbols and provide initialization for the second stage. Then, in the second stage, the TALS algorithm is employed to obtain the final estimation results of the three sub-channels. Simulation results verify the effectiveness of the proposed receiver. Full article

(This article belongs to the Topic Advanced Propagation Channel Estimation Techniques for Sixth-Generation (6G) Wireless Communications)

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18 pages, 5735 KB  

Open AccessArticle

Joint Channel Estimation for RIS-Aided mmWave Massive MIMO with Low-Resolution Quantization

by Wanqing Fu, Honggui Deng, Mingkang Qu and Nanqing Zhou

Electronics 2026, 15(7), 1497; https://doi.org/10.3390/electronics15071497 - 2 Apr 2026

Viewed by 623

Abstract

Reconfigurable intelligent surface (RIS) technology is a promising enabler for 6G communication systems due to its ability to reconfigure wireless propagation environments. However, as a passive device, RIS requires significant pilot overhead for accurate channel estimation. Moreover, the integration of RIS with multiple-input [...] Read more.

Reconfigurable intelligent surface (RIS) technology is a promising enabler for 6G communication systems due to its ability to reconfigure wireless propagation environments. However, as a passive device, RIS requires significant pilot overhead for accurate channel estimation. Moreover, the integration of RIS with multiple-input multiple-output (MIMO) systems further exacerbates power consumption and hardware costs. To address these challenges, this paper investigates RIS-assisted millimeter-wave (mmWave) MIMO systems with low-resolution analog-to-digital converters (ADCs). Exploiting the inherent sparsity of mmWave channels and considering the distortion introduced by low-resolution quantization, we propose a compressive sensing (CS)-based channel estimation scheme. Furthermore, to mitigate the effects of angular leakage, we introduce an energy capture orthogonal matching pursuit (ECOMP) algorithm. Simulation results demonstrate that the proposed scheme not only improves channel estimation accuracy but also reduces pilot overhead and power consumption, while maintaining enhanced stability in high signal-to-noise ratio (SNR) regimes. Full article

(This article belongs to the Topic Advanced Propagation Channel Estimation Techniques for Sixth-Generation (6G) Wireless Communications)

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19 pages, 3020 KB  

Open AccessArticle

Channel Estimation for RIS-Assisted Multi-User mmWave MIMO Systems via Joint Correlation

by Nanqing Zhou, Honggui Deng and Ni Li

Electronics 2026, 15(3), 594; https://doi.org/10.3390/electronics15030594 - 29 Jan 2026

Viewed by 1137

Abstract

Reconfigurable intelligent surface (RIS) demonstrates significant potential in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) wireless communication systems. However, the introduction of RIS leads to a substantial number of parameters in the channel matrix, making channel estimation highly challenging. By exploiting the sparsity of mmWave [...] Read more.

Reconfigurable intelligent surface (RIS) demonstrates significant potential in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) wireless communication systems. However, the introduction of RIS leads to a substantial number of parameters in the channel matrix, making channel estimation highly challenging. By exploiting the sparsity of mmWave channels, compressed sensing algorithms, such as the orthogonal matching pursuit (OMP) algorithm, can significantly reduce the pilot overhead. Nevertheless, traditional OMP algorithms typically require extensive prior knowledge about the number of effective paths, which is often difficult to obtain. To address this problem, we propose a novel multi-user joint correlation allocation (MUJCA) algorithm, which requires only minimal and easily measurable prior information. Our key idea is to divide the RIS coverage area into multiple sub-regions, each associated with a known number of scatterers, which is a pre-measured quantity, with users distributed within these sub-regions. Then, the MUJCA algorithm exploits joint correlation of multiple users to facilitate sparse channel recovery and transforms it back into the spatial channel. Simulation results show that the proposed MUJCA achieves higher channel estimation accuracy than existing benchmark algorithms. Full article

(This article belongs to the Topic Advanced Propagation Channel Estimation Techniques for Sixth-Generation (6G) Wireless Communications)

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17 pages, 581 KB  

Open AccessCommunication

3D Localization of Near-Field Sources with Symmetric Enhanced Nested Arrays

by Linke Yu, Huayue Wu, Haifen Meng, Zheng Zhou and Hua Chen

Technologies 2025, 13(9), 415; https://doi.org/10.3390/technologies13090415 - 12 Sep 2025

Cited by 1 | Viewed by 1222

Abstract

Sparse arrays can effectively reduce antenna cost and implementation complexity. However, most existing research in sparse array design mainly focuses on far-field scenarios, which cannot be directly applied to near-field (NF) source localization, where the delay term and source incident parameters exhibit a [...] Read more.

Sparse arrays can effectively reduce antenna cost and implementation complexity. However, most existing research in sparse array design mainly focuses on far-field scenarios, which cannot be directly applied to near-field (NF) source localization, where the delay term and source incident parameters exhibit a nonlinear relationship. In this paper, employing a symmetric enhanced nested array, a high-precision underdetermined three-dimensional (3D) NF localization method is proposed. Firstly, the symmetry of the array and the fourth-order cumulant are utilized to construct the equivalent virtual far-field (FF) received data. Then, a gridless, sparse, and parametric approach combined with an l₁-singular value decomposition-based pairing procedure is employed to obtain estimates of two paired angles. Finally, a one-dimensional (1D) spectral estimator is applied to obtain the estimate of the range parameter. By analyzing the virtual aperture, the optimal parameter configuration for a given number of elements is obtained. As shown by simulation results, the proposed method can handle underdetermined estimation. Compared with the other algorithms, the proposed algorithm achieves significant improvements in both angular and distance accuracy, with enhancements of 65% and 61.7%, respectively. Full article

(This article belongs to the Topic Advanced Propagation Channel Estimation Techniques for Sixth-Generation (6G) Wireless Communications)

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17 pages, 2245 KB  

Open AccessArticle

An Energy-Efficient Scheme for Waking Co-Channel TDMA in LoRa Networks via the Integration of Bidirectional Timestamp Correction and Address Recognition

by Zongliang Xu, Guicai Yu, Yingcong Luo and Hao Jiang

Future Internet 2025, 17(8), 369; https://doi.org/10.3390/fi17080369 - 14 Aug 2025

Cited by 3 | Viewed by 1411

Abstract

To address the issues of high energy consumption, data collisions, and invalid wake-ups of nontarget nodes in large-scale node-deployment scenarios of long-range (LoRa) star networks, this paper proposes an energy-saving wake-up scheme that combines (i) time-division multiple access (TDMA) slot allocation based on [...] Read more.

To address the issues of high energy consumption, data collisions, and invalid wake-ups of nontarget nodes in large-scale node-deployment scenarios of long-range (LoRa) star networks, this paper proposes an energy-saving wake-up scheme that combines (i) time-division multiple access (TDMA) slot allocation based on bidirectional timestamp correction with (ii) a sensing and communication integrated (ISAC) scheme proposed for physical address identification of LoRa nodes operating on the same channel. The scheme incorporates parameter estimation of the LoRa channel, which effectively enhances the identification accuracy and improves the system’s robustness. The proposed scheme consists of two parts: First, in case nodes briefly lose power, a bidirectional timestamp calibration algorithm and GPS-assisted timing are used to synchronize the gateway and each node with high precision, ensuring the accurate scheduling of the TDMA mechanism. Second, based on time synchronization, a “slot–LoRa module address” mapping table is constructed to set the communication time points between the gateway and each node. The gateway can wake the target nodes at specific, precise communication time points. Experimental results show that the proposed method maintains the error range within ±1 ms. The significant decrease in the rate of unnecessary node wake-up decreases data collisions and energy waste in the same channel environment. Energy savings scale with network size, thereby significantly extending the network life cycle. Full article

(This article belongs to the Topic Advanced Propagation Channel Estimation Techniques for Sixth-Generation (6G) Wireless Communications)

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28 pages, 1293 KB  

Open AccessArticle

A Lightweight Double-Deep Q-Network for Energy Efficiency Optimization of Industrial IoT Devices in Thermal Power Plants

by Shuang Gao, Yuntao Zou and Li Feng

Electronics 2025, 14(13), 2569; https://doi.org/10.3390/electronics14132569 - 25 Jun 2025

Cited by 5 | Viewed by 1632

Abstract

Industrial Internet of Things (IIoT) deployments in thermal power plants face significant energy efficiency challenges due to harsh operating conditions and device resource constraints. This paper presents gradient memory double-deep Q-network (GM-DDQN), a lightweight reinforcement learning approach for energy optimization on resource-constrained IIoT [...] Read more.

Industrial Internet of Things (IIoT) deployments in thermal power plants face significant energy efficiency challenges due to harsh operating conditions and device resource constraints. This paper presents gradient memory double-deep Q-network (GM-DDQN), a lightweight reinforcement learning approach for energy optimization on resource-constrained IIoT devices. At its core, GM-DDQN introduces the gradient memory mechanism, a novel memory-efficient alternative to experience replay. This core innovation, combined with a simplified neural network architecture and efficient parameter quantization, collectively reduces memory requirements by 99% and computation time by 85–90% compared to standard methods. Experimental evaluations across three realistic simulated thermal power plant scenarios demonstrate that GM-DDQN improves energy efficiency by 42% compared to fixed policies and 27% compared to threshold-based approaches, extending battery lifetime from 8–9 months to 14–15 months while maintaining 96–97% PSR. The method enables sophisticated reinforcement learning directly on IIoT edge devices without requiring cloud connectivity, reducing maintenance costs and improving monitoring reliability in industrial environments. Full article

(This article belongs to the Topic Advanced Propagation Channel Estimation Techniques for Sixth-Generation (6G) Wireless Communications)

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