Circular orthogonal sequences based channel estimation for MIMO SC-FDE systems

SummaryIn this paper, the channel estimation and signal‐to‐noise ratio (SNR) estimation technique of single‐carrier frequency domain equalization (SC‐FDE) system under low SNR in aeronautical multipath channel are studied, a SNR estimation algorithm which is easy to implement in engineering and an improved LS channel estimation algorithm based on Kalman filter using minimum error entropy (MEE‐KF) are proposed. This paper first introduces the SC‐FDE system and introduces the principle of MEE‐KF, and then, the channel estimation flow based on MEE‐KF is obtained by combining it with the traditional LS channel estimation algorithm, which makes the estimation results perform better. Simulation results show that after getting more accurate noise variance, the channel estimation results can better follow the changes of the channel after MEE‐KF processing, so as to resist the doppler frequency offset effect and make the channel estimation results more accurate, that is the channel response results of the data part can be closer to the real situation, so that the communication performance of SC‐FDE system has also been greatly improved.

Channel estimation is critical for coherent detection in OFDM systems. In pilot symbol aided channel estimation methods, the LS channel estimation can be viewed as a noisy observation of the true channel frequency response, so the noise component can be compressed through signal subspace projecting. In this paper, we focus on parametric channel estimation method combined with subspace projecting. First, we estimate the signal space of the OFDM system, and then we can use ESPRIT method to estimate the multipath delay of wireless channel, finally we use subspace projecting to get more accurate channel estimation. When the channel multipath delays vary with time, the signal subspace should be tracked to keep the efficiency of channel estimation. Computer simulations show that the performance of our method is much better than traditional channel estimation methods and its nonparametric counterpart.

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.

In order to solve the problem that noise and signal interference have an effect on the channel estimation of receiver, a channel estimation method based on integrated neural network has been proposed in this paper. Firstly, improved neural network would be used to extract nonlinear relationship between pilot signal with noise and original pilot signal. Secondly, the integrated neural network model would be obtained by selective integrating according to diversity factor, then channel estimation results could be obtained by model. Compared with the traditional methods, not only this method could reduce the computational complexity and obtain more accurate channel estimation results to reduce the bit error rate of communication system, but also reduce the cost of pilot and increase the bandwidth utilization rate. The simulation results show that the mean square error of channel estimation and bit error rate of communication are reduced by about 5% and 3% respectively, when compared with the traditional channel estimation method under the same signal-to-noise ratio.

A novel asymmetrical massive multiple-input multiple-output (MIMO) system has recently emerged as a crucial solution for reducing hardware complexity and alleviating the data processing pressure. However, the absence of channel reciprocity in this system presents unique challenges when directly applying traditional channel estimation methods, inevitably leading to performance loss. Deep learning approaches hold promise for achieving improved channel estimation performance by implicitly learning channel features. Unfortunately, deep learning approaches are often designed empirically. It is critical to utilize prior knowledge to develop an efficient deep neural network (DNN), especially for wireless communication systems. This paper explores a knowledge-driven DNN design approach and introduces a deep learning-based channel estimation framework for asymmetrical transceivers. The channel estimation problem is decoupled into channel denoising and information inference problems. Two novel DNNs are proposed to eliminate noise and exploit correlative features for reconstructing the missing channel information, respectively. Extensive simulations demonstrate that our proposed channel estimation framework can significantly eliminate noise effects, even in low signal-to-noise ratio regimes, and outperform traditional estimators and other deep learning-based methods. Moreover, ablation studies also validate the effectiveness of our knowledge-driven network structure design approach.

In this paper, we investigate channel estimation and robust detection for uplink massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with in-phase and quadrature-phase imbalances (IQI). We represent the received signal with a real-valued model, where the combination of the IQI and the wireless channel is treated as the effective channel. Based on the real-valued model, we then perform the minimum mean square error (MMSE) criterion based estimation for the effective channel. We use adjustable phase shift pilots (APSPs) to reduce the pilot overhead and verify that the MSE of the effective channel estimation can be minimized by scheduling the pilot phase shifts properly. We propose a pilot phase shift scheduling algorithm based on the covariance matrices of the effective channels. Since the channel estimation performance might be degraded due to using APSPs, we develop an MMSE criterion based data detection scheme which is robust to the channel estimation error. Using operator-valued free probability theory, we derive an asymptotic deterministic equivalent for the ergodic achievable sum rate of the proposed detection scheme. The performance of the channel estimation with APSPs and the robust data detection is demonstrated via extensive numerical results.

Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep Learning (DL) based OFDM channel estimators are only applicable to Signal-to-Noise Ratios (SNRs) in a small range, and the estimation performance of the existing algorithms is greatly limited when the channel model or the mobile speed at the receiver does not match. To solve this problem, this paper proposes a novel network model NDR-Net that can be used for channel estimation under unknown noise levels. NDR-Net consists of a Noise Level Estimate subnet (NLE), a Denoising Convolutional Neural Network subnet (DnCNN), and a Residual Learning cascade. Firstly, a rough channel estimation matrix value is obtained using the conventional channel estimation algorithm. Then it is modeled as an image and input to the NLE subnet for noise level estimation to obtain the noise interval. Then it is input to the DnCNN subnet together with the initial noisy channel image for noise reduction to obtain the pure noisy image. Finally, the residual learning is added to obtain the noiseless channel image. The simulation results show that NDR-Net can obtain better estimation results than traditional channel estimation, and it can be well adapted when the SNR, channel model, and movement speed do not match, which indicates its superior engineering practicability.

проведено исследованию методов оценки состояния канала связи (CSI) в ортогональном частотно-разделённом мультиплексировании (OFDM), применяемом в современных стандартах беспроводной связи, таких как LTE, Wi-Fi и 5G. Рассматриваются традиционные методы оценки канала, такие как Least Squares (LS) и Minimum Mean Square Error (MMSE), отмечаются их недостатки в сложных условиях, связанных с многолучевым распространением сигнала и высокой мобильностью абонентов. Рассмотрено перспективное направление оценки канала связи — глубокое обучение (DL). Доказано, что применение нейронных сетей позволяет существенно повысить точность оценки канала. Приводятся примеры исследований, использующих свёрточные нейронные сети (CNN) и другие архитектуры нейронных сетей для улучшения качества оценки CSI. Отмечено, что ряд аспектов применения нейронных сетей требует дальнейшего изучения, включая оптимизацию моделей под изменяющиеся условия среды и снижение вычислительной сложности. Предлагаются новые подходы для повышения эффективности оценки канала, среди которых использование рекуррентных нейронных сетей (RNN) и методов сжатия моделей (квантование и прунинг). Результаты экспериментов продемонстрировали потенциал использования RNN для эффективной оценки канала связи и восстановления сигналов в OFDM-системах. Моделирование подтвердило преимущество рекуррентных нейронных сетей над CNN в плане потребления вычислительных ресурсов, при сохранении сопоставимой производительности. Описана важность внедрения глубоких нейронных сетей для совершенствования технологии OFDM и предложены направления дальнейших исследований и разработок we conducted a study of methods for assessing the state of a communication channel (CSI) in orthogonal frequency-divided multiplexing (OFDM), used in modern wireless communication standards such as LTE, Wi-Fi and 5G. We considered traditional channel estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE), and their disadvantages are noted in difficult conditions associated with multipath signal propagation and high subscriber mobility. We considered a promising area of communication channel assessment, deep learning (DL). We proved that the use of neural networks can significantly improve the accuracy of channel estimation. We give examples of studies using convolutional neural networks (CNNs) and other neural network architectures to improve the quality of CSI assessment. We note that a number of aspects of the use of neural networks require further study, including optimizing models for changing environmental conditions and reducing computational complexity. We proposed new approaches to improve the efficiency of channel estimation, including the use of recurrent neural networks (RNNs) and model compression methods (quantization and pruning). The simulation confirmed the advantage of recurrent neural networks over CNNs in terms of computing resource consumption, while maintaining comparable performance. The experimental results demonstrated the potential of using RNN for effective assessment of communication quality and signal recovery in OFDM systems. We proposed the importance of the introduction of deep neural networks for the improvement of OFDM technology is described and the directions of further research and development

Multiuser multiple-input multiple-output (MU-MIMO) wireless systems that employ single-carrier frequency-domain equalization (SC-FDE) for uplink transmissions can provide high data rates with increased spectral efficiency under severe channel conditions. However, the availability of accurate channel estimates at the base station (BS) receiver is crucial for achieving peak performance. In this paper, we investigate the use of subspace decomposition and derive a novel algorithm for the blind estimation of MU-MIMO channels in SC-FDE systems, as specified by the 3GPP LTE. By exploiting the long data blocks available in LTE standards, our proposed blind algorithm can obtain accurate estimates of the MU-MIMO channels over every block of transmitted data. This provides for a bandwidth-efficient solution in SC-FDE systems by eliminating (reducing) the need to allocate an entire block of pilot sequences for each active transmitter. Furthermore, since the channel estimation is deployed at the BS, the computational complexity is not considered to be a significant burden for future systems in exchange for the increased spectral efficiency. The results of simulations over fading channels, using realistic system parameters representative of LTE-Advanced, demonstrate the advantages of our proposed blind subspace-based channel estimation algorithm and support the feasibility of the resulting MU-MIMO SC-FDE scheme with reduced training.

Accurate channel estimation is a necessary condition for data demodulation in wireless communication systems. Compared to traditional channel estimation method, compressed sensing theory can take full use of the sparse characteristics of the channel. The contradiction between channel estimation error and message rate can be effectively solved. Based on a system model of Single-antenna orthogonal frequency division multiplexing transmission, the performance of compressed sensing to channel estimation is studied. When Orthogonal Matching Pursuit Algorithm in frequency domain is used to sparse channel estimation, the accuracy of the channel estimation is greatly improved although the complexity of the algorithm is increased.

Distributed compressed sensing (DCS)-based channel estimation of multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing for relay communication is considered in this paper. Specifically, the pilot allocation is addressed to optimize the channel estimation performance. Pilot placement in all the existing works based on Compressed Sensing (CS), address the mean square error (MSE) probabilistically via mutual coherence. On the contrary, we try to address the MSE of the estimate directly and optimize the MSE directly and design a pilot pattern to maximize the performance of the estimation. By taking into account the optimization approach, a combinatorial stochastic algorithm has been presented. Simulation results represent that the DCS-based MIMO relay channel estimation using optimized pilot placements will increase the performance from 3 to 10 dB as compared with the conventional least squares (LS) method. Moreover, the DCS-based MIMO relay channel estimation shows 2.3% and 45% improvement in spectrum efficiency under the same bit error rate performance over the compressed sensing (CS)-based channel estimation and traditional LS-based channel estimation approach, respectively.

In this paper, while considering the impact of antenna correlation and the interference from neighboring users, we study the problem of channel estimation and training sequence design in multi-input multi-output (MIMO) two-way relaying (TWR) systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) and the broadcast (BC) phases. By deriving the optimal linear minimum mean-square-error estimators, the corresponding training design problems for the MAC and BC phases are formulated and solved. Subsequently, algorithms and, in some special cases, closed-form solutions for obtaining the optimal training sequences for channel estimation in TWR systems are derived. Moreover, to further reduce channel estimation overhead, the minimum required length of the training sequences are determined. Simulation results verify the effectiveness of the proposed training designs in improving channel estimation performance in TWR systems.

After a few decades’ evolution of wireless communication systems, to ensure reliable highspeed communication over unreliable wireless channels is still one of the major challenges facing researchers and engineers. The use of multiple antennas at transmitter and receiver, known as multiple-input multiple-output (MIMO) communications, is one promising technology delivering desired wireless services. The main goal of this thesis is to study two important issues in wireless MIMO communication systems: receiver design for coded MIMO systems, and diversity-multiplexing tradeoff analysis in general fading channels. In the first part of this thesis, we decompose the receiver design problem into two subproblems: MIMO channel estimation and MIMO detection. For the MIMO channel estimation, we develop an expectation-maximization (EM) based semi-blind channel and noise covariance matrix estimation algorithm for space-time coding systems under spatially correlated noise. By incorporating the proposed channel estimator into the iterative receiver structure, both the channel estimation and the error-control decoding are improved significantly. We also derive the modified Cramer-Rao bounds (MCRB) for the unknown parameters as the channel estimation performance metric, and demonstrate that the proposed channel estimation algorithm can achieve the MCRB after several iterations. For the MIMO detection, we propose a novel lowcomplexity MIMO detection algorithm, which has only cubic order computational complexity, but with near-optimal performance. For a 4×4 turbo-coded system, we show that the proposed detector had the same performance as the maximum a posteriori (MAP) detector for BPSK modulation, and 0.1 dB advantage over the approximated MAP detector (list sphere decoding algorithm) for 16-QAM modulation at BER = 10−4. In the second part of this thesis, we derive the optimal diversity-multiplexing tradeoff for

The traditional channel estimation methods for Filter Bank Multicarrier (FBMC) systems have some drawbacks, such as unreasonable pilot structure settings and poor estimation accuracy. Because FBMC/OQAM systems have inherent imaginary interference, this paper presents two new block pilots based on the traditional channel estimation methods. First, the system is briefly introduced. Because the traditional two-column pairs of pilot method (POP) has poor estimation accuracy and anti-noise performance, the structure of the pilot is reset to reduce noise interference and improve the estimation performance from the basic reason. Then aiming at the interference approximation method (IAM), because of the characteristics of its three-column pilot settings, the equivalent power of the pilot is high, which results in the excessive amplitude of the time-domain pilot signal and the pressure on the RF power amplifier. Therefore, the structure of IAM is modified to effectively suppress the amplitude. The simulation results show that the algorithm presented in this paper has a good bit error rate (BER) performance and small mean square error (MSE).

Orthogonal frequency division multiplexing (OFDM) technology has been increasingly applied to power line carrier communication (PLC). Discrete Fourier Transformation (DFT)‐based channel estimation algorithm is suitable for OFDM‐based PLC due to its low complexity. In view of the problem that the traditional DFT channel estimation does not consider the influence of noise inside cyclic prefix (CP), an improved DFT channel estimation based on signal‐to‐noise ratio (SNR) estimation is proposed. First, least‐square (LS) algorithm is performed and the frequency domain channel estimation is converted to the time domain through inverse DFT, and the average SNR of the system is estimated according to the pilot sequence. Second, the substitute SNR for each sample point inside CP is defined and used to filter impulse noise inside CP. Third, the average SNR is converted into the threshold of the useful signal energy inside CP, and the widespread background noise inside CP is filtered. The simulation results show that the proposed algorithm can obtain more accurate channel estimation than other DFT channel estimation algorithms because it can effectively filter out noise inside CP. In addition, compared with other similar algorithms, the proposed algorithm dose not result in a significant increase in complexity.