Accurate Channel State Information Research Articles

Predicted position-driven deep learning channel estimation for massive MIMO systems

Multiple-input multiple-output (MIMO) can significantly improve the energy efficiency and spectral efficiency of wireless communication systems, and obtaining accurate channel state information is a key prerequisite. However, traditional channel estimation techniques often have high computational complexity, poor channel estimation performance, and do not fully utilize the information in the communication scene. In this paper, a channel estimator (PrePD-CNN) with convolutional neural network (CNN) driven by predicted position information is proposed based on high-precision localization technology for 6G. The channel estimation performance is improved by rationally transforming the sensing information and accurately reconstructing the quasi-channel state as auxiliary information. Simulation results show that the BER performance and MSE performance of PrePD-CNN are significantly lower than that of traditional channel estimation algorithms. Therefore, the proposed scheme combines stronger robustness and good generalization, which not only reduces the complexity of the algorithm but also improves the performance.

Image Super Resolution-Based Channel Estimation for Orthogonal Chirp Division Multiplexing on Shallow Water Underwater Acoustic Communications.

Orthogonal chirp division multiplexing (OCDM) offers a promising modulation technology for shallow water underwater acoustic (UWA) communication systems due to multipath fading resistance and Doppler resistance. To handle the various channel distortions and interferences, obtaining accurate channel state information is vital for robust and efficient shallow water UWA communication. In recent years, deep learning has attracted widespread attention in the communication field, providing a new way to improve the performance of physical layer communication systems. In this paper, the pilot-based channel estimation is transformed into a matrix completion problem, which is mathematically equivalent to the image super-resolution problem arising in the field of image processing. Simulation results show that the deep learning-based method can improve the channel distortion, outperforming the equalization performed by traditional estimator, the performance of Bit Error Rate is improved by 2.5 dB compared to the MMSE method in OCDM system. At the 7.5 to 20 dB region, it achieves better bit error rate performance than OFDM systems, and the bit error rate is reduced by approximately 53% compared to OFDM when the SNR value is 20, which is very useful in shallow water UWA channels with multipath extension and severe time-varying characteristics.

A robust beamforming with antenna selection approach for dense antenna arrays under limited CSI

This paper introduces a novel approach for dense antenna arrays, which integrates beamforming and antenna selection. The proposed method aims to optimize the number of transmit antennas to reduce power consumption and enhance spectral efficiency at the transmitter. By employing optimal beamforming weights, our approach steers signals toward the intended receiver to maximize the transmit power of the desired signal while mitigating side-lobe interference under channel uncertainty. To adapt beamforming parameters dynamically to changing channel conditions, we develop a channel estimation approach that obtains accurate channel state information (CSI). In contrast to prior studies, our channel estimation method utilizes a generalized Gaussian distribution (GGD) model, capable of accommodating both matched and mismatched CSI features by modeling heavy-tailed and light-tailed distributions. This allows our method to capture diverse channel statistical behaviors resulting from phenomena such as fading and interference under various propagation conditions. Furthermore, we introduce an angle entropy metric, which measures angle of departure (AoD) variations, indicating the degree of uncertainty regarding the directionality of transmitted signals. Simulation results validate the accuracy of our analytical findings and demonstrate the superior performance of the proposed approach in terms of robustness and bit-error-rate (BER) performance.

On the Performance of MMSE Channel Estimation in Massive MIMO Systems over Spatially Correlated Rician Fading Channels

Massive multiple-input-multiple-output (M-MIMO) offers remarkable advantages in terms of spectral, energy, and hardware efficiency for future wireless systems. However, its performance relies on the accuracy of channel state information (CSI) available at the transceivers. This makes channel estimation pivotal in the context of M-MIMO systems. Prior research has focused on evaluating channel estimation methods under the assumption of spatially uncorrelated fading channel models. In this study, we evaluate the performance of the minimum-mean-square-error (MMSE) estimator in terms of the normalized mean square error (NMSE) in the uplink of M-MIMO systems over spatially correlated Rician fading. The NMSE allows for easy comparison of different M-MIMO configurations, serving as a relative performance indicator. Besides, it is an advantageous metric due to its normalization, scale invariance, and consistent performance indication across diverse scenarios. In the system model, we assume imperfections in channel estimation and that the random angles in the correlation model follow a Gaussian distribution. For this scenario, we derive an accurate closed-form expression for calculating the NMSE, which is validated via Monte-Carlo simulations. Our numerical results reveal that as the Rician K-factor decreases, approaching Rayleigh fading conditions, the NMSE improves. Additionally, spatial correlation and a reduction in the antenna array interelement spacing lead to a reduction in NMSE, further enhancing the overall system performance.

Attention-based deep learning approach for CSI feedback under 5G TDL channel

In 5G communication systems, accurate channel state information (CSI) is indispensable for signal detection and regulation at the base station side. However, frequent CSI feedback from users leads to excessive system overhead. To tackle this challenge, this paper puts forward a novel deep learning framework - HCNet based on attention mechanism and autoencoder, aiming to efficiently compress and reconstruct the high-dimensional time-varying CSI matrices in an end-to-end approach. The proposed framework incorporates a self-attention module in the encoder to explicitly capture global dependencies within the CSI matrix. Meanwhile, the decoder adopts gated recurrent unit networks to fully exploit inter-feature correlations and redundancy. To evaluate the performance, simulations are conducted using datasets conforming to current 5G time-varying TDL channel models. Results demonstrate superior performance over existing deep learning based feedback networks. Specifically, the proposed framework can reduce the normalized mean square error of CSI reconstruction by 4 dB under various compression ratios which confirms the effectiveness of the attention-enhanced autoencoder structure for compressive CSI sensing and feedback in practical dynamic communication systems.

Power Optimization of Unmanned Aerial Vehicle-Assisted Future Wireless Communication Using Hybrid Beamforming Technique in Disaster Management

Several established procedures are required to be followed when responding to disasters. Although most of them have so far been successful, they pose their own set of difficulties. The primary factor is response time, which is crucial for disaster management. The use of unmanned aerial vehicles (UAVs) for wireless communication not only increases response time but also is energy efficient. However, UAVs have limited power capacities, which makes their use in wireless communication challenging. In wireless communication, hybrid beamforming is a technique for optimizing the battery consumption of unmanned aerial vehicles (UAVs). Massive MIMO technology facilitates hybrid beamforming by deploying antenna arrays, efficiently directing transmitted energy to intended receivers, and minimizing power waste. Beamforming relies heavily on accurate Channel State Information (CSI). Advanced channel estimation and tracking techniques optimize power allocation based on real-time channel conditions, thereby minimizing energy consumption and ensuring connection reliability. Hybrid beamforming combines the advantages of digital and analog beamforming to reduce power consumption while maintaining communication performance. This paper proposes a power optimization technique for UAV-based wireless communication using hybrid beamforming to maximize spectral efficiency and increase the battery life of UAVs.

PERFORMANCE ANALYSIS OF ACHIEVABLE BIT RATES IN RIS-ASSISTED MASSIVE MIMO NETWORKS AT 28 GHz BAND

Reconfigurable intelligent surface (RIS) technology is a smart way of controlling the radio signal propagation to improve the capacity and coverage of wireless networks. RIS tunes the phase shifts of the incident signals in a dynamic fashion. Channel modeling is an important aspect in RIS-based mmWave communication for the next-generation wireless networks. However, to achieve maximum benefit from RIS-assisted wireless systems, it is essential to provide accurate channel state information (CSI). But, it is very challenging to get accurate CSI because of large number of RIS elements, their passive nature, and the training overhead involved during the channel estimation. To overcome the higher training overhead, in this article we aimed to take advantage of the correlation and sparsity of channels in RIS-assisted channel estimation. The objective of this work is to propose a simplified channel model for the RIS-assisted physical channel of a massive multi-input-multi-output (mMIMO) wireless communication system and analyze its performance in terms of transmitted powers, MIMO configurations, and achievable bit rates. The simulated results proved that the strategic placement of RIS with optimal phase shifts and optimal MIMO configuration can enhance the maximum achievable rate. The achievable rates of the proposed channel modeling are compared with the existing state-of-the-art methods to prove its efficiency. Also, the combination of mMIMO technology along with RIS-assisted communication provides degrees of freedom in terms of signal coverage, energy consumption, and system complexity.

2D Beam Domain Statistical CSI Estimation for Massive MIMO Uplink

In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two-dimensional (2D) beam-based statistical channel model (BSCM) in massive multi-input multi-output (MIMO) systems. The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple received pilot signals. A received signal model showing the relation between the statistical properties of the received pilot signals and the BDCPMs is derived. On the basis of the received signal model, we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without the estimates of instantaneous CSI is proposed. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., estimation of the angle domain channel power matrix. Simulation results show that the proposed method has good convergence and can obtain sparse BDCPMs. Compared with the regularized multiple measurement vector focal underdetermined system solver (RM-FOCUSS) algorithm, the proposed algorithm obtains overall more accurate statistical CSI with much lower complexity and brings significant performance gains when used in channel estimation.

Joint Beamforming Design in Reconfigurable Intelligent Surface-Assisted Rate Splitting Networks

Reconfigurable Intelligent Surface (RIS) is an emerging technology that can improve the spectrum and energy efficiency of next-generation wireless networks. However, attaining accurate channel state information (CSI) for the cascaded RIS channel is particularly challenging. Imperfect CSI is a major bottleneck to achieving the spectral efficiency benefit of RIS-assisted networks. Rate splitting (RS), a promising multiple access technology, has been shown to be able to achieve an improved spectrum efficiency and be robust to channel uncertainties. This paper investigates the interplay between RIS and RS by considering a RIS-assisted RS beamforming problem. Active beamforming at the base station (BS) and passive beamforming at the RIS are jointly considered to maximize the minimum user rate under both the perfect CSI case and the imperfect CSI case. A block coordinate descent (BCD) algorithm is developed to solve this non-convex problem. Compared with the conventional semi-definite relaxation (SDR) approach, the proposed method does not require that the covariance matrix of the common beamforming vector be rank-one. Moreover, we present theoretical results to help reveal the impact of the system parameters and explain the performance gain resulting from the integration of RIS and RS. Extensive simulation results are also provided to show that RIS-assisted RS can improve the max-min rate performance significantly compared with conventional multiple access technologies, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) with/without RIS, especially in overloaded systems. With the proposed method, RS shows great potential in combating realistic CSI errors in RIS-assisted networks.

Joint channel training and passive beamforming design for intelligent reflecting surface-aided LoRa systems

&lt;abstract&gt;&lt;p&gt;In order to examine the potential and synergetic aspects of intelligent reflecting surface (IRS) techniques for Internet-of-Things (IoT), we study an IRS-aided Long Range (LoRa) system in this paper. Specifically, to facilitate the acquisition of accurate channel state information (CSI) for effective reflection of LoRa signals, we first propose an optimal training design for the least squares channel estimation with LoRa modulation, and then, by utilizing the acquired CSI, we develop a high-performing passive beamforming scheme based on a signal-to-ratio (SNR) criterion. Numerical results show that the proposed training design considerably outperforms the baseline schemes, and the proposed passive beamforming design results in a significant improvement in performance over that of the conventional LoRa system, thereby demonstrating the feasibility of extending coverage areas of LoRa systems with the aid of IRS.&lt;/p&gt;&lt;/abstract&gt;

On the Doppler Squint Effect in OTFS Systems Over Doubly-Dispersive Channels: Modeling and Evaluation

Extensive work has demonstrated the excellent performance of orthogonal time frequency space (OTFS) modulation in high-mobility scenarios. Time-variant wideband channel estimation serves as one of the key compositions of OTFS receivers since the data detection requires accurate channel state information (CSI). In practical wideband OTFS systems, the Doppler shift brought by the high mobility is frequency-dependent, which is referred to as the Doppler Squint Effect (DSE). Unfortunately, DSE was ignored in overall prior estimation schemes employed in OTFS systems, which leads to severe performance loss in channel estimation and the consequent data detection. In this paper, we investigate DSE of wideband time-variant channel in delay-Doppler domain and concentrate on the characterization of OTFS channel coefficients considering DSE. The formulation and evaluation of OTFS input-output relationship are provided for both ideal and rectangular waveforms considering DSE. The channel estimation is therefore formulated as a sparse signal recovery problem and an orthogonal matching pursuit (OMP)-based scheme is adopted to solve it. Simulation results confirm the significance of DSE and the performance superiority compared with traditional channel estimation approaches ignoring DSE.

Toward RIS-Aided Non-Coherent Communications: A Joint Index Keying M-ary Differential Chaos Shift Keying System

In reconfigurable intelligent surface (RIS)-aided coherent communications, channel state information (CSI) is often assumed to be perfectly estimated at the receiver. However, perfect CSI cannot be available in practice. Furthermore, the complex and ever-changing channel makes the acquisition of accurate CSI unaffordable because of the large overhead in transmitting pilot signals. Motivated by these considerations, a novel non-coherent RIS-aided joint index keying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -ary differential chaos shift keying (RIS-JIK-MDCSK) scheme is proposed in this paper, where the receiver can retrieve information bits by performing non-coherent correlation demodulation without requiring CSI, thereby reducing the system complexity. In RIS-JIK-MDCSK, the states of the reference signal, RIS elements, and information-bearing subcarriers are jointly optimized to devise a joint index keying mechanism, where additional information bits are implicitly transmitted by these state indices, thus increasing the throughput and spectral efficiency. Furthermore, an effective joint index keying detection algorithm is proposed to recover information bits. The analytical bit error rate (BER) of RIS-JIK-MDCSK is derived over a Rayleigh fading channel. Other evaluation metrics, including the throughput, spectral efficiency, and system complexity are also analyzed and compared against benchmark schemes. Numerical simulations are performed to evaluate the superiority of RIS-JIK-MDCSK compared to existing schemes.

Millimeter Wave Wireless Channel Knowledge Map Construction Based on Path Matching and Environment Partitioning

Key technologies in 5G and future 6G, such as millimeter wave massive multiple-input multiple-output (MIMO), relies accurate channel state information (CSI). However, when the number of base station (BS) antenna increases or the number of users is large, it is rather resource-consuming to obtain the CSI. Channel knowledge map (CKM) is an emerging environment-aware wireless communication technology, which stores the physical coordinates of BS and reference locations together with the corresponding channel path information. This makes it possible to obtain CSI with light or even without pilots, which can significantly reduce the overhead of channel estimation and improve system performance, especially suitable for quasi-static wireless environments with relatively stable channels and communication systems using millimeter waves, terahertz waves, visible light, and so on. The main challenge for CKM is how to construct an accurate CKM based on finite measurement data points at limited reference locations. In this work, we proposed a novel CKM construction method based on path matching and environmental partitioning (PMEP-CC) to address the above issues. Specifically, we first sort the propagation paths between reference locations, map them to a high-dimensional space to establish the path correlation coefficient between two reference locations. Then, the communication region are divided into different subregions based on its spatial correlation. Finally, the path information at locations where no measurements are available are estimated based on the known path information within the subregion to construct CKM. Numerical results are provided to show the performance of the proposed method over related studies.

Common structured sparsity based multi-user channel estimation for double-RIS assisted millimeter wave systems

Compared to a single reconfigurable intelligent surface (RIS) system, a double RIS system can achieve better passive beamforming gain, while requiring more accurate channel state information (CSI). However, due to the presence of double-reflection links, channel estimation with low pilot overhead is quite challenging. In this paper, we investigate channel estimation of double-RIS assisted multi-user millimeter wave (mmWave) communication systems. Firstly, the channel estimation problem for double-reflection links is formulated as a sparse signal recovery problem. Then, we analyze and exploit the common structured sparsity of the cascaded channel among multiple users, i.e., the common row and column sparsity in the angular channel matrix, and the corresponding common sparsity after being transformed into the channel vector. Inspired by this, we further propose a novel common structured sparsity based orthogonal matching pursuit (CSS-OMP) algorithm for multi-user joint channel estimation. Simulation results show that CSS-OMP algorithm can provide more accurate channel estimation with reduced pilot overhead compared to the conventional OMP algorithm.

Pilot-Partitioning Protocol and Anti-Jamming Methods in Distributed Massive MIMO Systems

Accurate channel state information (CSI) is crucial for effective beamforming in wireless communication systems. Unfortunately, the estimation of CSI might not be accurate due to pilot contamination, which happens when the transmission of pilots from the users to access points (APs) is interfered. In systems with a large number of users, non-orthogonal pilot training can increase pilot contamination resulting in incorrect CSI estimation. To address this issue, we propose a pilot-partitioning protocol for distributed massive multiple-input multiple-output (MIMO) systems. The protocol decomposes long-length orthogonal pilots into short-length sub-pilots, transmits them in consecutive slots, and uses the channel correlation among consecutive slots to regenerate orthogonal pilots. In addition, we consider the impact of adversarial transmissions, such as jamming attacks, on CSI estimation. To protect the system against jamming attacks, we propose an AP selection framework to eliminate ineffective APs where the estimated CSI is strongly distorted by jammers. Furthermore, we develop power control strategies for achieving the net throughput maximization and user fairness. Our numerical results show that the proposed designs improve system performance by up to 6.47 times compared to non-optimized scenarios.

Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks

The communication between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In addition, road and traffic safety have very strict requirements for low-latency, high-reliability communication, and information transmission is only valuable within a certain range, and requires timeliness. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep-Q network reinforcement learning (RL) with low-latitude fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. These agents work in concert, interact with the environment, receive appropriate observations, rewards, and ultimately learn to improve power and spectrum allocation to provide a better entertainment experience and safer driving environment for users. We also address the shortcomings in existing literature studies. Firstly, the low upper limit of transmitted data volume; Secondly, the artificial assumption that the spectrum is consistent with the number of V2V links, which is insufficient for future applications in spectrum shortage sections. We accordingly extend the amount of transmitted data while adding a scenario where the spectrum resources are relatively short, i.e., the number of V2V links is significantly larger than the amount of spectrum. Experimental results show that with proper training mechanism and reward function construction, multiple intelligence can cooperate effectively. Therefore, the total link capacity of the V2I link and the success rate of periodic security information transmission are further improved.

Machine learning based low-complexity channel state information estimation

In 5G communications, the acquisition of accurate channel state information (CSI) is of great importance to the hybrid beamforming of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. In classical mmWave MIMO channel estimation methods, the exploitation of inherent sparse or low-rank structures has demonstrated to improve the performance. However, most high-accurate CSI estimators incur a high computational complexity and require the prior channel information, which hence present the major challenges in the practical deployment. In this work, we leverage machine learning to design the low-complexity and high-performance channel estimator. To be specific, we first formulate the CSI estimation, in the case of sparse structure, as one classical least absolute shrinkage and selection operator problem. In order to reduce the time complexity of existing compressed sensing (CS) methods, we then approximate the original optimization problem to another one, by imposing the other low-rank constraint that was barely considered by CS. We thus solve this new approximated problem and attain the near-optimal solution of the original problem. One new method excludes any prior channel information, and greatly improves the estimation performance, which only incurs a low time complexity. Simulation results demonstrate the superiority of our proposed method both in the estimation accuracy and time complexity.

CSI Sensing From Heterogeneous User Feedbacks: A Constrained Phase Retrieval Approach

This paper investigates the downlink channel state information (CSI) sensing in 5G heterogeneous networks composed of user equipments (UEs) with different feedback capabilities. We aim to enhance the CSI accuracy of UEs only affording the low-resolution Type-I codebook. While existing works have demonstrated that the task can be accomplished by solving a phase retrieval (PR) formulation based on the feedback of precoding matrix indicator (PMI) and channel quality indicator (CQI), they need many feedback rounds. In this paper, we propose a novel CSI sensing scheme that can significantly reduce the feedback overhead. Our scheme involves a novel parameter dimension reduction design by exploiting the spatial consistency of wireless channels among nearby UEs, and a constrained PR (CPR) formulation that characterizes the feasible region of CSI by the PMI information. To address the computational challenge due to the non-convexity and the large number of constraints of CPR, we develop a two-stage algorithm that firstly identifies and removes inactive constraints, followed by a fast first-order algorithm. The study is further extended to multi-carrier systems. Extensive tests over DeepMIMO and QuaDriGa datasets showcase that our designs greatly outperform existing methods and achieve the high-resolution Type-II codebook performance with a few rounds of feedback.

An improved deep learning-based end-to-end autoencoder for UAV-to-ground free space optical communication

In this paper, an improved deep learning-based end-to-end autoencoder is proposed for unmanned aerial vehicle (UAV) to ground free space optical communication to mitigate atmospheric turbulence. Deep neural network (DNN) is applied to the intensity modulation/direct detection (IM/DD) autoencoder, including transmitter, receiver as well as channel model. The performance is improved by two-stage deep learning training because the minimum Hamming distance between the codewords is increased through pre-training. Simulation results show that the bit error rate of our proposed scheme can reach the 7% hard-decision forward error correction (HD-FEC) threshold at signal-to-noise ratio of approximately 22 dB and in strong atmospheric turbulence where the maximum Rytov variance is 3.5. Our proposed scheme can outperform the state-of-the-art IM/DD system with PPM transmitter and maximum likelihood receiver by achieving approximately 12 dB improvement and reducing ∼51.3% of the decoder's running time without the need for accurate channel state information.

Channel Estimation for XL-RIS-Aided Millimeter-Wave Systems

Reconfigurable intelligent surface (RIS) is able to enhance the capacity of wireless communication systems with low overhead. Extremely large (XL)-RIS-aided millimeter-wave (mmWave) communication has become a promising key technique for future 6-th Generation (6G) systems. The performance gain brought in by XL-RIS relies on the accurate channel state information (CSI). However, channel estimation requires huge training overhead and high computational complexity due to the XL number of passive elements at RIS. Moreover, the unknown visual region (VR) infomation caused by the sensitivity of mmWave signal to random blockages makes the channel estimation more difficult. In this paper, we consider the channel estmation for XL-RIS-aided mmWave uplink system. We firstly model the XL-RIS-aided channel as a hybrid one composed of near-field RIS-to-user channel and far-field RIS-to-base station (BS) channel, where the VR issue of XL-RIS has been taken into consideration. Then we formulate the channel estimation problem as a sparse recovery problem. To solve this problem, we propose a two-stage algorithm for joint channel estimation and VR detection. Finally numerical results show that the proposed algorithms outperform the existing benchmark schemes in terms of normalized mean-squared error (NMSE) due to the VR detection and the utilization of shift common-support property among sub-channels.