Search

The expected development of the future generation of wireless communications systems such as 6G aims to achieve an ultrareliable and low-latency communications (URLLCs) while maximizing the data rates. These requirements push research into developing new advanced technologies. To this end, massive multiple input multiple output (MMIMO) is introduced as a promising transmission approach to fulfill these requirements. However, maximizing the downlink-achievable sum rate (DASR) in MMIMO with a frequency division duplex (FDD) transmission mode and limited coherence time (LCT) is very challenging. To address this challenge, this paper proposes a DASR maximization approach using a feasible power allocation optimization method. The proposed approach is based on smartly allocating the total transmit power between the data transmission and training sequence transmission for channel estimation. This can be achieved by allocating more energy to the training signal than the data transmission during the channel estimation process to improve the quality of channel estimation without compromising more training sequence length, thus maximizing the DASR. Additionally, the theory of random matrix approach is exploited to derive an asymptotic closed-form expression for the DASR with a regularized zero-forcing precoder (RZFP), which allows the power optimization process to be achieved without the need for computationally complex Monte Carlo simulations. The results provided in this paper indicate that a considerable enhancement in the DASR performance is achieved using the proposed power allocation method in comparison with the conventional uniform power allocation method. [ABSTRACT FROM AUTHOR]

This paper investigates single-user uplink and two-user downlink channel estimation in reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. Because of the difficulty associated with the estimation of channels in RIS-aided wireless communication systems, channel state information (CSI) is assumed to be known at the receiver in some previous works in the literature. By assuming that prior knowledge of the line-of-sight (LoS) channel between the RIS and the base station (BS) is known, two compressive sensing-based channel estimation schemes that are based on simultaneous orthogonal matching pursuit and structured matching pursuit (StrMP) algorithms are proposed for estimation of uplink channel between RIS and user equipment (UE), and joint estimations of downlink channels between BS and a UE, and between RIS and another UE, respectively. The proposed channel estimation schemes exploit the inherent common sparsity shared by the angular domain mmWave channels at different subcarriers. The superiority of one of the proposed channel estimation techniques, the StrMP-based channel estimation technique, with negligibly higher computational complexity cost compared with other channel estimators, is documented through extensive computer simulation. Specifically, with a reduced pilot overhead, the proposed StrMP-based channel estimation scheme exhibits better performance than other channel estimation schemes considered in this paper for signal-to-noise ratio (SNR) between 0 dB and 5 dB upward at different instances for both uplink and downlink scenarios, respectively. However, below these values of SNR the proposed StrMP-based channel estimation scheme will require higher pilot overhead to perform optimally. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle's movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle's starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle's position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance. [ABSTRACT FROM AUTHOR]

In vehicular communications, channel estimation is a complex problem due to the joint time–frequency selectivity of wireless propagation channels. To this end, several signal processing techniques as well as approaches based on neural networks have been proposed to address this issue. Due to the highly dynamic and random nature of vehicular communication environments, precise characterization of temporal correlation across a received data sequence can enable more accurate channel estimation. This paper proposes a new pilot constellation scheme in combination with a small feed‐forward neural network to improve the accuracy of channel estimation in V2X systems while keeping low the implementation complexity. The performance is evaluated in typical vehicular channels using simulated BER curves, and it is found superior to traditional channel estimation methods and state‐of‐the‐art neural‐network‐based implementations such as feed‐forward and super‐resolution. It is illustrated that the improvement becomes pronounced for small subcarrier spacings (or low 5G numerologies); hence, this paper contributes to the development of more reliable mobile services across rapidly varying vehicular communication channels with rich multi‐path interference. [ABSTRACT FROM AUTHOR]

Network load in mobile networks is continuously growing, putting pressure on mobile operators to deliver target network performance and user experience. This paper focuses on network capacity improvement through increased spectral efficiency, which can be achieved with overhead reduction by optimizing the demodulation reference signal (DM-RS) configuration, which is one of the channels/reference signals with the largest contribution to overhead. In a high-speed scenario, the Doppler effect noticeably influences the temporal nature of the channel such that a channel interpolation process is required within a slot. However, increasing the number of symbols used for DM-RS has a negative impact on capacity. The Doppler effect was analyzed for various 5G NR configurations of operating frequency and subcarrier spacing (SCS), and various use cases were considered using user equipment (UE) speed as the main parameter. For suitable use cases, the DM-RS configuration was optimized in networks with live traffic. The impact of the configuration change on 5G/NR spectral efficiency, user experience and link adaptation performance was assessed through a deep-dive analysis of active measurements, available performance Management (PM) counters and key performance indicators. An optimized DM-RS configuration is proposed, and it is demonstrated to achieve gains of 5–15%, depending on the metric used, use case analyzed, network load, traffic mix and other relevant network characteristics such as topology and clutter type. [ABSTRACT FROM AUTHOR]

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Channel estimation accuracy significantly affects the performance of orthogonal frequency-division multiplexing (OFDM) systems. In the literature, there are quite a few channel estimation methods. However, the performances of these methods deteriorate considerably when the wireless channels suffer from nonlinear distortions and interferences. Machine learning (ML) shows great potential for solving nonparametric problems. This paper proposes ML-based channel estimation methods for systems with comb-type pilot patterns and random pilot symbols, such as ATSC 3.0. We compare their performances with conventional channel estimations in ATSC 3.0 systems for linear and nonlinear channel models. We also evaluate the robustness of the ML-based methods against channel model mismatch and signal-to-noise ratio (SNR) mismatch. The results show that the ML-based channel estimations achieve good mean squared error (MSE) performance for linear and nonlinear channels if the channel statistics used for the training stage match those of the deployment stage. Otherwise, the ML estimation models may overfit the training channel, leading to poor deployment performance. Furthermore, the deep neural network (DNN)-based method does not outperform the linear channel estimation methods in nonlinear channels. [ABSTRACT FROM AUTHOR]

Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm. [ABSTRACT FROM AUTHOR]

With the advancement of information technology construction, the integration of radar and communication represents a crucial technological evolution. Driven by the research boom of integrated sensing and communications (ISACs), some scholars have proposed utilizing orthogonal frequency-division multiplexing (OFDM) to separately modulate radar and communication signals. However, the OFDM symbols in this paper incorporate a cyclic prefix (CP) and a virtual carrier (VC) instead of zero padding (ZP). This approach mitigates out-of-band power caused by ZP, in addition to reducing adjacent channel interference (ACI). In addition, we introduce low-density parity-check (LDPC) and use an improved normalized min-sum algorithm (NMSA) in decoding. The enhanced decoding efficiency and minimized system errors render the proposed waveform more suitable for complex environments. In terms of signal processing methods, this paper continues to use radar signals as a priori information to participate in channel estimation. Further, we consider the symbol timing offset (STO) and carrier frequency offset (CFO) issues. In order to obtain more reliable data, we use the minimum mean-square error (MMSE) estimation based on the discrete Fourier transform (DFT) to evaluate the channel. Simulation experiments verify that the system we propose not only realizes the transmission and detection functions but also improves the performance index of the integrated signal, such as the bit error rate (BER) of 7 × 10−5, the peak side lobe ratio (PSLR) of −13.81 dB, and the integrated side lobe ratio (ISLR) of −8.98 dB at a signal-to-noise ratio (SNR) of 10 dB. [ABSTRACT FROM AUTHOR]

In multi-antenna systems, advanced techniques such as massive multiple-input multiple-output (MIMO), beamforming, and beam selection depend heavily on the accurate acquisition of the channel state. However, pilot contamination (PC) can be a major source of interference which degrades they are performance. Moreover, the severity of PC increases as more pilots are reused between users in the wireless systems. Researchers have shown that PC can be mitigated by using deep learning (DL) approaches. Nevertheless, when minimizing PC, the examination that identifies the applications and factors that distinguish these DL approaches is still limited. This paper reviews these DL approaches and the improvements needed to enhance their performance. Simulation results confirm that DL networks that learn to predict the channels directly have superior performance under PC. [ABSTRACT FROM AUTHOR]

Real-time DOA (direction of arrival) estimation of surface or underwater targets is of great significance to the research of marine environment and national security protection. When conducting real-time DOA estimation of underwater targets, it can be difficult to extract the prior characteristics of noise due to the complexity and variability of the marine environment. Therefore, the accuracy of target orientation in the absence of a known noise is significantly reduced, thereby presenting an additional challenge for the DOA estimation of the marine targets in real-time. Aiming at the problem of real-time DOA estimation of acoustic targets in complex environments, this paper applies the MEMS vector hydrophone with a small size and high sensitivity to sense the conditions of the ocean environment and change the structural parameters in the adaptive adjustments system itself to obtain the desired target signal, proposes a signal processing method when the prior characteristics of noise are unknown. Theoretical analysis and experimental verification show that the method can achieve accurate real-time DOA estimation of the target, achieve an error within 3.1° under the SNR (signal-to-noise ratio) of the X channel of −17 dB, and maintain a stable value when the SNR continues to decrease. The results show that this method has a very broad application prospect in the field of ocean monitoring. [ABSTRACT FROM AUTHOR]

Due to the spectrum and complexity efficiency, the integrated radar and communications (RadCom) systems have been widely favored, in which orthogonal frequency division multiplexing (OFDM) is the most popular signal to conduct the two functions simultaneously. However, an unoptimized pulse could suffer from severe inter-carrier interference (ICI) and high out-of-band emission (OOBE), which greatly degrades the system performance. In this paper, we introduce the pulse shaping scheme dedicated to RadCom systems, in which both transmitter and receiver can adaptively design pulses with the assistance of radar estimation. We first optimize the transmitting pulse with the weighted sum of signal-to-interference-plus-noise ratio (SINR) and OOBE by employing the popular genetic algorithm. Then, we design an improved-matched pulse at the receiver for maximizing the SINR with the fmincon solver. In this way, they both utilize the readily available radar information and keep the pulse optimal even in highly dynamic scenarios, which makes the most of RadCom systems while avoiding the overhead of channel estimation and feedback. Simulations prove the feasibility of proposed scheme and reveal that the radar image and communications SINR stay close to their optimum in most cases with much lower OOBE. An improved-matched pulse can further improve the communications performance when severe ICI occurs compared with a matched pulse. [ABSTRACT FROM AUTHOR]

Future beyond fifth-generation (B5G) and sixth-generation (6G) communication systems require a higher quality of service (QoS) along with meeting multiple objectives and traffic demands. Consequently, new multi-antenna technologies and massive multiple-input-multiple-output (mMIMO) architectures have been proposed in recent years. This paper delves into the foundational concepts that form the basis for the design of two potential future mMIMO network topologies: cell-free (CF) network and its successor, the radio stripe (RS) system. Key aspects of the mMIMO and CF network concepts are addressed, along with a practical sequential implementation based on RSs. This exploration encompasses intricate details of the channel estimation (CE) phase, as well as the uplink (UL) and downlink (DL) transmission and reception phases. We then focus on analyzing optimization concepts that underpin resource allocation (RA) algorithms, specifically those applied in UL power allocation and access point selection (APS) schemes in both CF and RS networks. This comprehensive understanding serves as a robust foundation for addressing the challenges inherent in achieving the conflicting B5G and 6G major key performance indicators (KPIs), such as enhancements on spectral efficiency (SE), power efficiency (PE), and computational complexity or load balance (LB). [ABSTRACT FROM AUTHOR]

This paper advocates for spatial simultaneous functioning (SSF) over time division multiple access (TDMA) in joint communication and sensing (JCAS) scenarios for improved resource utilization and reduced interference. SSF enables the concurrent operation of communication and sensing systems, enhancing flexibility and efficiency, especially in dynamic environments. The study introduces joint design communication and sensing scenarios for single input single output (SISO) and multiple input multiple output (MIMO) JCAS receivers. An MIMO-JCAS base station (BS) is proposed to process downlink communication signals and echo signals from targets simultaneously using interference cancellation techniques. We evaluate the communication performance and sensing estimation across both Rayleigh and measured realistic channels. Additionally, a deep neural network (DNN)-based approach for channel estimation and signal detection in JCAS systems is presented. The DNN outperforms the traditional methods in the bit error rate (BER) versus signal-to-noise ratio (SNR) curves, leveraging its ability to learn complex patterns autonomously. The DNN's training process fine-tunes the performance based on specific problem characteristics, capturing the nuanced relationships within data and adapting to varying SNR conditions for consistently superior performance compared to the traditional approaches. [ABSTRACT FROM AUTHOR]

Joint source–channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network's self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder–decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model's performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim. [ABSTRACT FROM AUTHOR]

Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) and visible light positioning (VLP) systems are usually designed separately to prevent mutual interference, while terminal mobility often introduces challenges that can degrade their performance. In this paper, we propose an integrated visible light communication and positioning (VLCP) scheme designed for mobile scenarios, encompassing both multiple-input–single-output (MISO) and multiple-input–multiple-output (MIMO) configurations. This scheme integrates both functionalities into a unified system. Utilizing decision feedback channel estimation (DFCE), we effectively estimate the dynamic channel state information (CSI) for both VLC and VLP, thereby potentially enhancing both communication and positioning performance. Simulation results across various routes verify the effectiveness of the proposed scheme. It is observed that when the terminal moves at 1 m/s, the VLCP scheme with DFCE can maintain reliable transmission quality, ensuring bit error rates (BERs) consistently below 1.3 × 10−2. Additionally, the mean positioning errors remain within the centimeter range in different routes, not exceeding 4.3 cm and 15.5 cm in the MISO and MIMO scenarios, respectively. [ABSTRACT FROM AUTHOR]

Rate splitting multiple access (RSMA) is a powerful and upcoming multiple access technique that can serve as a promising technology for 5G and beyond applications. Due to the increase in the number of indoor communication devices, the internet of everything (IoE) demands higher bandwidth. Visible light communication (VLC) would be a viable solution for an indoor environment that offers high bandwidth, cost-free spectrum, and low power, and if employed with RSMA, the sum rate can be maximized. In this paper, we derive the closedform outage probability (OP) for the downlink RSMA-based VLC scheme over Rayleigh and Rician channels. We perform outage analyses for perfect as well as imperfect channel state information (CSI) and successive interference cancellation (SIC), respectively to study the system characteristics. Also, we show the superiority of RSMA over non-orthogonal multiple access (NOMA) for VLC-based system in terms of OP over Rayleigh and Rician channels. From the simulations, we show that the RSMA-based VLC system outperforms conventional NOMAbased VLC and the quality of services improves with the line of sight communication i.e., the Rician fading channel. [ABSTRACT FROM AUTHOR]

In this paper, in order to overcome the channel distortion due to doubly selective channel, a new deep learning-based channel estimation scheme is proposed. In the proposed scheme, two neural networks are combined to learn both the temporal and spectral characteristics of the channel. One is the AE (autoencoder) which is trained to track the channel characteristic in the time domain and the other is the LSTM (long short term memory) which is trained to track the channel characteristic in the frequency domain. Firstly, the AE is trained and then the LSTM is sequentially trained based on the AE output. After that, the trained neural network is used to the conventional DPA (data pilot aided) process to estimate the channel values in frequency domain. It is noted that the proposed LSTM network consists of fixed number of LSTM units, so it enables to track the temporal variation of the channel reliably, regardless of the position of the OFDM (orthogonal frequency division multiplexing) symbol in a frame. Therefore, the proposed scheme can enhance the PER (packet error rate) performance more by reducing the error propagation due to the DPA process. Through numerical results, it is confirmed that the proposed scheme shows the best PER performance of the conventional schemes in doubly selective channel environment [ABSTRACT FROM AUTHOR]

The dim lighting and excessive dust in underground mines often result in uneven illumination, blurriness, and loss of detail in surveillance images, which hinders subsequent intelligent image recognition. To address the limitations of the existing image enhancement algorithms in terms of generalization and accuracy, this paper proposes an unsupervised method for enhancing mine images in the hue–saturation–value (HSV) color space. Inspired by the HSV color space, the method first converts RGB images to the HSV space and integrates Retinex theory into the brightness (V channel). Additionally, a random perturbation technique is designed for the brightness. Within the same scene, a U-Net-based reflectance estimation network is constructed by enforcing consistency between the original reflectance and the perturbed reflectance, incorporating ResNeSt blocks and a multi-scale channel pixel attention module to improve accuracy. Finally, an enhanced image is obtained by recombining the original hue (H channel), brightness, and saturation (S channel), and converting back to the RGB space. Importantly, this image enhancement algorithm does not require any normally illuminated images during training. Extensive experiments demonstrated that the proposed method outperformed most existing unsupervised low-light image enhancement methods, qualitatively and quantitatively, achieving a competitive performance comparable to many supervised methods. Specifically, our method achieved the highest PSNR value of 22.18, indicating significant improvements compared to the other methods, and surpassing the second-best WCDM method by 10.3%. In terms of SSIM, our method also performed exceptionally well, achieving a value of 0.807, surpassing all other methods, and improving upon the second-place WCDM method by 19.5%. These results demonstrate that our proposed method significantly enhanced image quality and similarity, far exceeding the performance of the other algorithms. [ABSTRACT FROM AUTHOR]

The reconfiguration of wireless channels with reconfigurable reflecting surface (RIS) technology offers new design options for future wireless networks. Due to its high altitude and increased probability of establishing line-of-sight linkages with ground source/destination nodes, aerial RIS (ARIS) has greater deployment flexibility than traditional terrestrial RIS. It also provides a wider-view signal reflection. To leverage the advantages of ARIS-enabled systems, this paper defines air-to-ground linkages via Nakagami-m small-scale fading and inverse-Gamma large-scale shadowing, considering realistic composite fading channels. To construct a tight approximate closed-form formula for the outage probability (OP), a new mathematical framework is proposed. Additionally, a deep-learning-based system called the BiLSTM model is deployed to evaluate OP performance in the 3D spatial movement of the ARIS system. In the offline phase, the proposed model is trained with real-value channel state estimation sets and enhances OP performance in the online phase by learning channel information in a bidirectional manner. Simulation results demonstrate that the proposed BiLSTM model outperforms all other models in analyzing OP for the ARIS system. [ABSTRACT FROM AUTHOR]

Integrated Sensing and Communications (ISAC) need to process data streams in high‐speed sensor data acquisition or high‐speed wireless communications. To process the data can require more computing and communication resources, resulting in higher power consumption. Halved‐Phase Only Multiple Input Multiple Output (HPO‐MIMO) communication technology can solve this problem by using low‐power nonlinear detection devices. In ISAC, Channel Estimation (CE) technology can provide key channel characteristics and state information for sensing and collaborative work of perception and communication tasks. However, HPO‐MIMO system cannot realize CE using traditional receiver schemes because of the missing amplitude. In order to solve this problem, two HPO‐MIMO CE schemes based on model‐driven deep learning are proposed in this paper. The proposed schemes include a Densely Residual Network (DRN) and a Inception‐Resnet (IR), which is suitable for the case of sufficient data and insufficient data, respectively. The simulation results show that the performance of DRN based scheme is better than that of IR based scheme when the data amount is sufficient, and the performance of IR based scheme is better when the dataset is small. In addition, the proposed CE schemes work well with a range of antenna sizes and distances. [ABSTRACT FROM AUTHOR]

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Compressive sampling matching pursuit (CoSaMP), as a conventional algorithm requiring system sparsity and sensitive to step size, was improved in this paper by approximating the sparsity with adaptive variable step size. In the proposed algorithm (CoSaMP with variable step size abbreviated as Vss-CoSaMP), the idea of approximating sparsity with adaptive step size was borrowed from the sparsity adaptive matching pursuit (SAMP) algorithm to determine the sparsity for the CoSaMP algorithm. The applicability of the CoSaMP algorithm was therefore expanded considerably. On this basis, a step size reduction was added as the iteration termination condition of an orthogonal frequency division multiplexing (OFDM) system. An adaptive variable step size algorithm was then put forward to address the CoSaMP algorithm's sensitivity to step size. It could realize the required precision at different initial step sizes. A simulation was carried out to analyze the influence of pilot number and step size in an OFDM system on the algorithm. The algorithms, including SAMP, CoSaMP, and Vss-CoSaMP, were compared with two sparse channels, revealing that the Vss-CoSaMP algorithm overcame the problem of the CoSaMP algorithm, that is, the impossibility to forecast the channel sparsity. With the adaptive step size, the proposed algorithm could reach and achieve better accuracy than the CoSaMP algorithm. Additionally, the proposed algorithm was superior over the SAMP algorithm in terms of reconstruction, mean square error (MSE), and bit error ratio (BER). [ABSTRACT FROM AUTHOR]

Recent years have witnessed the remarkable progress in wireless communication systems due to the escalating demand for higher data rates, improved reliability, and increased energy efficiency. In this regard, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technology, enhancing spectral efficiency and accommodating multiple users concurrently within the same time and frequency resources. Simultaneously, the energy harvesting has surfaced as a sustainable solution, converting ambient environmental energy into usable electrical power for operating communication nodes. This paper proposes a cooperative NOMA transmission scheme integrating energy harvesting and utilizing Least Squares (LS) channel estimation for precise Channel State Information (CSI) acquisition. The objective is to establish an optimal communication path from source to destination. Relay selection methods: Optimal Relay Selection (ORS) and Max-Min Relay Selection (MMRS), are compared, focusing on their impact on the system performance. The analysis considers the influence of the number of relays and power allocation factor on the system, with a specific emphasis on the outage probability expressions. Comparative analysis between the cooperative-NOMA and the traditional cooperative relaying without NOMA reveals the superior performance of the cooperative-NOMA. Additionally, the ORS scheme outperforms MMRS in terms of the outage performance. [ABSTRACT FROM AUTHOR]

In the era of 6G, the rapid increase in communication data volume poses higher demands on traditional channel estimation techniques and those based on deep learning, especially when processing large-scale data as their computational load and real-time performance often fail to meet practical requirements. To overcome this bottleneck, this paper introduces quantum computing techniques, exploring for the first time the application of Quantum Generative Adversarial Networks (QGAN) to broadband channel estimation challenges. Although generative adversarial technology has been applied to channel estimation, obtaining instantaneous channel information remains a significant challenge. To address the issue of instantaneous channel estimation, this paper proposes an innovative QGAN with a dualmodule design in the generator. The adversarial loss function and the Mean Squared Error (MSE) loss function are separately applied for the parameter updates of these two modules, facilitating the learning of statistical channel information and the generation of instantaneous channel details. Experimental results demonstrate the efficiency and accuracy of the proposed dual-module QGAN technique in channel estimation on the Pennylane quantum computing simulation platform. This research opens a new direction for physical layer techniques in wireless communication and offers expanded possibilities for the future development of wireless communication technologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Moving microphones allow for the fast acquisition of sound-field data that encode acoustic impulse responses in time-invariant environments. Corresponding decoding algorithms use the knowledge of instantaneous microphone positions for relating the dynamic samples to a positional context and solving the involved spatio-temporal channel estimation problem subject to the particular parameterization model. Usually, the resulting parameter estimates are supposed to remain widely unaffected by the Doppler effect despite the continuously moving sensor. However, this assumption raises issues from the physical point of view. So far, mathematical investigations into the actual meaning of the Doppler effect for such dynamic sampling procedures have been barely provided. Therefore, in this paper, we propose a new generic concept for the dynamic sampling model, introducing a channel representation that is explicitly based on the instantaneous Doppler shift according to the microphone trajectory. Within this model, it can be clearly seen that exact trajectory tracking implies exact Doppler-shift rendering and, thus, enables unbiased parameter recovery. Further, we investigate the impact of non-perfect trajectory data and the resulting Doppler-shift mismatches. Also, we derive a general analysis scheme that decomposes the microphone signal along with the encoded parameters into particular subbands of Doppler-shifted frequency components. Finally, for periodic excitation, we exactly characterize the Doppler-shift influences in the sampled signal by convolution operations in the frequency domain with trajectory-dependent filters. [ABSTRACT FROM AUTHOR]

In the paper, we investigate a massive machine-type communication (mMTC), where numerous single-antenna users communicate with a single-antenna base station while being active. However, the status of user can undergoes multiple transitions between active and inactive states across whole consecutive intervals. Then, we formulate the problem of joint user activity detection and channel estimation within the dynamic compressed sensing (DCS) framework, considering the temporally-correlated user activity across the entire consecutive intervals. To be specific, we introduce a new hybrid vector approximate message passing algorithm for DCS (HyVAMP-DCS). The proposed algorithm comprises a VAMP block for estimating channel and a loopy belief propagation (LBP) block for detecting user activity. Moreover, these two blocks can exchange messages, enhancing the performance of both channel estimation and user activity detection. Importantly, compared to the fragile GAMP algorithm, VAMP is robust and applicable to a much broader class of large random matrices. Furthermore, the fixed points of VAMP's state evolution align with the replica prediction of the minimum mean-squared error. The simulation results illustrate the superiority of HyVAMP-DCS, demonstrating its significant outperformance over HyGAMP-DCS. [ABSTRACT FROM AUTHOR]

This study investigates receiver design solutions for distributed Massive Multiple Input Multiple Output (D-m MIMO) systems, taking into account parameters such as number of access points as well as concerns related to channel estimates that use single-carrier frequency-domain equalization (SC-FDE). A significant contribution of this research is the integration of Low-Density Parity-Check (LDPC) codes to simplify coding complexity and enhance communication efficiency. The research examines different receiver designs, such as spatial antenna correlation and sophisticated channel estimation methods. The authors propose integrating LDPC codes into the receiver architecture to simplify computations and enhance error correction and decoding. Moreover, the paper examines performance evaluation measures and approaches, highlighting the trade-offs among complexity, spectral efficiency, and error performance. The comparative analysis indicates the benefits, in terms of performance, of incorporating LDPC codes and improving system throughput and dependability. We examine four distinct receiver algorithms: zero-forcing (ZF), minimum mean square error (MMSE), maximum ratio combining (MRC), and equal gain combining (EGC). The study shows that MRC and EGC receivers work well in D-m MIMO because they make the receiver system less computationally demanding. [ABSTRACT FROM AUTHOR]

Simultaneous transmit and receive wireless communications have been highlighted for their potential to double the spectral efficiency. However, it is necessary to mitigate self-interference (SI). Considering both the SI channel and remote transmission (RT) channel need to be estimated before equalizing the received signal, we propose two adaptive algorithms for linear and nonlinear self-interference cancellation (SIC), based on a multi-layered joint channel estimator structure. The proposed algorithms estimate the RT channel while performing SIC, and the multi-layered structure ensures improved performance across various interference-to-signal ratios. The M-estimate function enhances the robustness of the algorithm, allowing it to converge even when affected by impulsive noise. For nonlinear SIC, this paper introduces an adaptive algorithm based on generalized Hammerstein polynomial basis functions. The simulation results indicate that this approach achieves a better convergence speed and normalized mean squared difference compared to existing SIC methods, leading to a lower system bit error rate. [ABSTRACT FROM AUTHOR]

Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l 1 − l 1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

[Objective] Power Line Carrier(PLC)communication adopts the frame burst transmission mode. Due to the carrier frequency offset between transceivers, various noise and time-varying characteristics of PLC channel and the system has no dedicated reference signal. The traditional channel estimation has no tracking and prediction ability for the channel, which leads to the deterioration of the PLC system performance. [Methods] Aiming at the existing problems, this paper proposes a Denoising Long Short Term Memory (DnLSTM) neural network based on Long Short Term Memory (LSTM) neural network and Denoising Convolutional Neural Network (DnCNN), which is used for PLC channel estimation. First, offline training is performed on DnLSTM and the parameters are saved after training. Then the trained parameters are deployed in PLC system. After loading parameters, online prediction is performed to obtain the predicted PLC system channel estimation. In the simulation of PLC system, this paper uses Least Squares (LS) algorithm, Minimum Mean Square Error (MMSE) algorithm and DnLSTM to estimate the channel response, and gives the simulation results under the conditions of Additive White Gaussian Noise (AWGN), combined noise, impulsive noise and colored noise. Meanwhile, simulations for different number of preamble symbols for channel estimation are performed. [Results] The results show that there is a relationship between the accuracy of DnLSTM channel estimation and the number of preamble symbols. Using four preamble symbols for channel estimation, its estimation accuracy is better than LS and close to MMSE algorithm. DnLSTM has a good ability to resist carrier frequency offset and channel time-varying. When the number of preamble symbols for channel estimation increases, PLC system performance with low Singnal to Noise Ratio (SNR) gets better and PLC system performance is similar with high SNR. [Conclusion] According to simulation results above, it can be concluded that DnLSTM, which is based on DnCNN and LSTM, can predict PLC system channel response with frequency offset very well and it can track the varying PLC system channel response in real time [ABSTRACT FROM AUTHOR]

Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method utilizing superimposed training sequences. Compared with conventional schemes, this method offers higher spectral efficiency and better adaptability to time-varying channels owing to its temporal traversal. To ensure success in this scheme, it is crucial to obtain time-varying channel estimation and data detection at low SNRs given that superimposed training sequences consume power resources. To achieve this goal, we initially employ coarse channel estimation utilizing superimposed training sequences. Subsequently, we employ approximate message passing algorithms based on the estimated channels for data detection, followed by iterative channel estimation and equalization based on estimated symbols. We devise an approximate message passing channel estimation method grounded on a Gaussian mixture model and refine its hyperparameters through the expectation maximization algorithm. Then, we refine the channel information based on time correlation by employing an autoregressive hidden Markov model. Lastly, we perform numerical simulations of communication systems by utilizing a time-varying channel toolbox to simulate time-varying channels, and we validate the feasibility of the proposed communication system using experimental field data. [ABSTRACT FROM AUTHOR]

This paper investigates methods for noise-canceling channel estimation (NC-CE) to track rapid time-varying channels in IEEE 802.11p/orthogonal frequency division multiplexing (OFDM) systems. To this end, we introduce a novel three-step channel estimation technique based on the estimated length of the channel impulse response (CIR). This approach aims to surpass the performance of conventional designs that rely on constructed data pilots (CDPs). In the first step, we not only eliminate noise components but also estimate the channel frequency responses (CFRs) of virtual subcarriers for long preamble parts. Moving on to the second step, we incorporate a modified CDP method without a frequency-domain reliability test and interpolation, taking into account the CFRs of virtual subcarriers obtained at the previous OFDM symbol time. The final step can be implemented as the operation of the inverse fast Fourier transform (IFFT)/nulling/FFT to reduce noise components from the CFRs obtained in the second step and generate CFRs for virtual subcarriers to be used in the next symbol time. The results of our simulations validate the effectiveness of our proposed channel estimation schemes. [ABSTRACT FROM AUTHOR]

A physical model of a wireless transmission channel in the time domain usually consists of the main propagation path and only a few reflections. The reasonable assumptions made about the channel model can improve its parameters' estimation by a greedy OFDM (Orthogonal Frequency Division Multiplexing) equalizer. The equalizer works flawlessly if delays between propagation paths are in the sampling grid. Otherwise, the channel impulse response loses its compressible characteristic and the number of coefficients to find increases. It is possible to get back to the simple channel model by data oversampling. The paper describes how the above idea helps the OMP (Orthogonal Matching Pursuit) algorithm estimate channel coefficients. The authors analyze the oversampling algorithm on the one hand to assess the influence of filtering function and signal resolution on the quality of the channel impulse response reconstruction. On the other hand, the abilities of the OMP algorithm are analyzed to distinguish components of the oversampled signal. Based on these analyses, we proposed modifications to the compressible channel's impulse response reconstruction algorithm to minimize the number of transmission errors. A distinction was made between the filters used in the OMP search and channel reconstruction stages before calculating equalizer coefficients. Additionally, the results of the search stage were considered as elements within the groups. [ABSTRACT FROM AUTHOR]

In recent years, the global surge of High-speed Railway (HSR) revolutionized ground transportation, providing secure, comfortable, and punctual services. The next-gen HSR, fueled by emerging services like video surveillance, emergency communication, and real-time scheduling, demands advanced capabilities in real-time perception, automated driving, and digitized services, which accelerate the integration and application of Artificial Intelligence (AI) in the HSR system. This paper first provides a brief overview of AI, covering its origin, evolution, and breakthrough applications. A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system: mechanical manufacturing and electrical control, communication and signal control, and transportation management. The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components, forecast of railroad maintenance, optimization of energy consumption in railroads and trains, communication security, communication dependability, channel modeling and estimation, passenger scheduling, traffic flow forecasting, high-speed railway smart platform. Finally, challenges associated with the application of AI are discussed, offering insights for future research directions. [ABSTRACT FROM AUTHOR]

In UnderWater-Acoustic-Orthogonal-Frequency-Division-Multiplexing-(UWA-OFDM) communication, the traditional interpolated channel estimation method produces error codes, due to the small number of user pilots, uneven distribution, and complex channel characteristics. In this paper, we propose a novel UWA-channel-estimation method based on Deep Learning (DL). First, based on a small number of channel samples, we used the CWGAN-GP model to generate enhanced classified underwater-acoustic channel samples to have semantic similarity to the real samples and also to present the diversity of the samples. After obtaining the channel sample, the pilot estimation matrix was processed in a similar image way. Here, we extracted the channel features by constructing a convolutional network structure similar to U-Net, weakening the impact of feature information loss. A Channel-Attention-Denoising-(CAD) module was also designed, to further optimize the reconstructed channel information. The simulation results verified the superiority of the proposed algorithm, in terms of Mean Square Error (MSE) and Bit-Error Rate (BER) compared to the existing Least-Squares-(LS), Deep-Neural-Network-(DNN), and ChannelNet algorithms. [ABSTRACT FROM AUTHOR]

Due to the relative motion between transmitters and receivers and the multipath characteristic of wideband underwater acoustic channels, Doppler and channel estimations are of great significance for an underwater acoustic (UWA) communication system. In this paper, a preamble signal based on superimposed linear frequency modulation (LFM) signals is first designed. Based on the designed preamble signal, a real-time Doppler factor estimation algorithm is proposed. The relative correlation peak shift of two LFM signals in the designed preamble signal is utilized to estimate the Doppler factor. Moreover, an enhanced channel estimation algorithm, the correlation-peak-search-based improved orthogonal matching pursuit (CPS-IOMP) algorithm, is also proposed. In the CPS-IOMP algorithm, the excellent autocorrelation characteristic of the designed preamble signal is used to estimate the channel sparsity and multipath delays, which are utilized to construct the simplified dictionary matrix. The simulation and sea trial data analysis results validated the designed preamble, the proposed Doppler estimation algorithm, and the channel estimation algorithm. The performance of the proposed Doppler factor estimation is better than that of the block estimation algorithm. Compared with the original OMP algorithm with known channel sparsity, the proposed CPS-IOMP algorithm achieves a similar estimation accuracy with a smaller computational complexity, as well as requiring no prior knowledge about the channel sparsity. [ABSTRACT FROM AUTHOR]

This paper analyzes the fundamental trade-offs that occur in the co-design of pilot resource allocations in orthogonal frequency-division multiplexing signals for both ranging (via time-of-arrival estimation) and communications. These trade-offs are quantified through the Shannon capacity bound, probability of outage, and the Ziv–Zakai bound on range estimation variance. Bounds are derived for signals experiencing frequency-selective Rayleigh block fading, accounting for the impact of limited channel knowledge and multi-antenna reception. Uncompensated carrier frequency offset and phase errors are also factored into the capacity bounds. Analysis based on the derived bounds demonstrates how Pareto-optimal design choices can be made to optimize the communication throughput, probability of outage, and ranging variance. Different pilot resource allocation strategies are then analyzed, showing how Pareto-optimal design choices change depending on the channel. [ABSTRACT FROM AUTHOR]

Two-stage precoding schemes have been developed to reduce the channel estimation overhead in FDD systems. By integrating user scheduling into these schemes, it becomes possible to meet the quality-of-service requirements of high-density wireless communication systems, despite the limitations on spatial resources and transmit power budget. In this paper, we propose a user scheduling and dynamic beam allocation method for neighbor-based joint spatial division multiplexing (N-JSDM) transmission. The user scheduling problem is formulated as a 0–1 quadratic programming problem to maximize effective spectral efficiency (ESE) using directional channel properties. To address the complexity issue, convex relaxation and linearization methods are employed to transform the problem into a 0–1 mixed integer linear programming, and a dimensionality reduction method is introduced. The proposed user scheduling-aided N-JSDM scheme reduces downlink training length and feedback of channel state information. Additionally, a dynamic configuration form is used for pre-beamforming matrix design based on user distribution, outperforming conventional approaches. Simulation results demonstrate higher ESE achieved by the proposed scheme compared to previous methods. [ABSTRACT FROM AUTHOR]

In recent years, the study of deep learning techniques for underwater acoustic channel estimation has gained widespread attention. However, existing neural network channel estimation methods often overfit to training dataset noise levels, leading to diminished performance when confronted with new noise levels. In this research, a "bias-free" denoising convolutional neural network (DnCNN) method is proposed for robust underwater acoustic channel estimation. The paper offers a theoretical justification for bias removal and customizes the fundamental DnCNN framework to give a specialized design for channel estimation, referred to as the bias-free complex DnCNN (BF-CDN). It uses least squares channel estimation results as input and employs a CNN model to learn channel characteristics and noise distribution. The proposed method effectively utilizes the temporal correlation inherent in underwater acoustic channels to further enhance estimation performance and robustness. This method adapts to varying noise levels in underwater environments. Experimental results show the robustness of the method under different noise conditions, indicating its potential to improve the accuracy and reliability of channel estimation. [ABSTRACT FROM AUTHOR]

This paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER. [ABSTRACT FROM AUTHOR]

With the capability for single-receiver AR (Ambiguity Resolution), real-time GNSS (Global Navigation Satellite System) satellite integer clock has been the research hotspot of GNSS community in the past decade. Robust ambiguity datum is one prerequisite for high-quality integer clock. In this paper, with undifferenced AR model derived, a robust ambiguity datum definition method is proposed to isolate the signal biases from the integer ambiguity. Firstly, based on the ambiguity graph, the ambiguity datum is selected with the minimum spanning tree algorithm. Secondly, the ambiguity datum is transferred to the subsequent epochs through signal bias connection. Moreover, additional datum ambiguities are introduced to accommodate the arising of receiver and satellite in real-time circumstance. With the combination of ambiguity graph and signal bias, the efficient definition and smooth transition of ambiguity datum are assured for the real-time processing. To validate the approach, simulated real-time integer clock estimation for GPS/Galileo/BDS-3 satellites are conducted with one-month (January 2022) data from about 100 global IGS (International GNSS Service) network stations. Experiments show that the proposed robust ambiguity datum can produce accurate real-time integer clock. The average WL (Wide-Lane) and NL (Narrow-Lane) fixed rates exceed 99 % and 95 %, respectively, for most of the GPS/Galileo satellites. While comparable WL fixed rate is achieved for the BDS-3 satellites, the NL fixed rate is about 5 % lower as a result of poor orbit quality and imperfect measurement model. With eclipsing satellites excluded, the standard deviations between real-time integer clock and post-processed reference clock reach 0.018 ns and 0.016 ns for the GPS and Galileo constellations, respectively, which are 28 % and 24 % smaller than those of corresponding float clocks. In contrast, the standard deviations of float and integer clocks are similar for the BDS-3 constellation. Further improvements on the background models, including unconservative force modeling, satellite attitude modeling and antenna calibration are required to achieve ultimate accuracy, especially for the BDS-3 satellites. [ABSTRACT FROM AUTHOR]

Abstract In vehicular communications, channel estimation is a complex problem due to the joint time–frequency selectivity of wireless propagation channels. To this end, several signal processing techniques as well as approaches based on neural networks have been proposed to address this issue. Due to the highly dynamic and random nature of vehicular communication environments, precise characterization of temporal correlation across a received data sequence can enable more accurate channel estimation. This paper proposes a new pilot constellation scheme in combination with a small feed‐forward neural network to improve the accuracy of channel estimation in V2X systems while keeping low the implementation complexity. The performance is evaluated in typical vehicular channels using simulated BER curves, and it is found superior to traditional channel estimation methods and state‐of‐the‐art neural‐network‐based implementations such as feed‐forward and super‐resolution. It is illustrated that the improvement becomes pronounced for small subcarrier spacings (or low 5G numerologies); hence, this paper contributes to the development of more reliable mobile services across rapidly varying vehicular communication channels with rich multi‐path interference.

【Objective】With the development of the space-ground integrated information network, the Low Earth Orbit (LEO) satellite communication system is ushering in a development boom. The Paired Carrier Multiple Access (PCMA) technology is gradually developing to the low-orbit satellite communication due to its advantages of saving bandwidth resources. However, traditional PCMA technology is mostly used in high-orbit satellites, and cannot adapt to the highly dynamic fading channel character-istics of low-orbit satellite channels, which greatly degrades the bit error performance of the PCMA receiver. The bottleneck lies in the channel estimation and equalization technologies for overlapping signals.【Methods】Aiming at the channel characteristics of LEO satellites, this paper proposes a channel estimation scheme that combines training sequence estimation and Autoregressive (AR) model prediction. Based on the idea of superimposed training sequence channel estimation, an iterative method suitable for PCMA mixed signal channel estimation is introduced to improve the accuracy of training sequence channel estimation through iteration. The AR model is used to predict the Channel State Information (CSI) of the data sequence in real time. The use of AR model can also reduce the frequency of channel estimation in training sequences, so as to adapt to the dynamics of LEO satellite channels.【Results】The simulation results show that the idea of superimposed training sequence channel estimation can be applied to the PCMA signals, and accurate channel estimation can be obtained after iterations. The method proposed in this paper can effectively improve the accuracy of channel estimation. After signal separation and demodulation, the bit error rate can reach the order of 10-3when the signal-to-noise ratio is greater than 9 dB.【Conclusion】A channel estimation method for PCMA signal is proposed in this paper. The simulation results show that the bit error rate loss is within an acceptable range, which can support the application of PCMA technology in low-orbit satellite communication. The proposed algorithm has the advantages of simple structure, low complexity, and high practical value.

High-accuracy positioning plays an important role in future 5G-Advanced cellular networks. However, it is quite challenging to utilize 5G signals to estimate positioning parameters accurately under multipath environments. To establish a high-precision real-time location system (RTLS), which is suitable for indoor scenarios, advanced parameter estimation techniques with low computational complexity should be developed. This paper proposes a modified subspace-based time-of-arrival (TOA) estimation algorithm for 5G signals with ultra-high accuracy and reduced complexity under multipath environments. A novel smoothing scheme termed as grouped subcarrier smoothing (GSS) is proposed to reduce the dimension of the covariance matrix involved in typical subspace-based methods. Then, the computational complexity of the corresponding eigenspace calculation is reduced. The TOA estimation performance is enhanced by constructing multiple snapshots of the estimated channel frequency response vector in the frequency domain. Finally, the super-resolution TOA estimate can be obtained using only one orthogonal frequency division multiplexing (OFDM) symbol. Simulation results show that the proposed method achieves centimeter-level TOA estimation accuracy under multipath channels when the signal-to-noise ratio (SNR) is high. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-channel speech separation method based on spatial estimation via sparse Bayesian inference (SBI) and nonnegative matrix factorization (NMF). The spatial information estimated by traditional compressed sensing (CS) models is insufficient when two microphones receive limited columns of mixed signals. Considering the sparsity of peak values in the cross-correlation spectrum between two received signals, the proposed method builds a new CS model based on cross-correlation spectrum and applies SBI algorithm to solve this model to improve the estimation accuracy of spatial information. Combined the spatial information with the spectral features decomposed by NMF, NMF coefficient matrix masks belonging to individual source are generated for pre-separation. To mitigate retained potential interference components, a post-separation processing stage is designed using an expectation maximization (EM) algorithm based on a Gaussian mixture model (GMM). The estimated spatial information and binary time–frequency masks are used for parameter initialization of the EM algorithm. The experimental results using real-world speech data show that the proposed method can achieve better separation performance compared to various existing methods. [ABSTRACT FROM AUTHOR]

In this paper, channel estimation capabilities of a multiple-input multiple-output (MIMO) system using superimposed training sequences are investigated. A new expression for estimation-error variance is derived. It is shown that the training sequences must be balanced and must have specific correlation properties. The latter are required only in a specific zone. Sequences that satisfy these criteria exist and are referred to as zero-correlation zone (ZCZ) solutions. Consequently, by using balanced ZCZ sequences, harmful direct current (DC) offset can be removed. Owing to their zero-cross correlation, interference from other transmitting antennas may be eliminated. Furthermore, a closed-form expression of the estimation-error variance can be obtained due to their impulse-like autocorrelation. To increase the number of antennas in the MIMO system, a new construction of ZCZ sequence set is proposed, in which all sequences are balanced. [ABSTRACT FROM AUTHOR]