This letter proposes a distributed path selection algorithm based on the meta multi-agent proximal policy optimization (Meta-MAPPO). The algorithm leverages transferable knowledge to achieve faster and more stable policy optimization in dynamic satellite networks. We integrate meta-learning into the MAPPO framework, equipping agents with rapid adaptation capabilities and enhancing convergence efficiency through experience sharing. Simulation results on a 96-satellite Walker–Delta constellation demonstrate that the proposed framework achieves at least a 5% reduction in average end-to-end delay, maintains zero packet loss, and converges faster, demonstrating its efficiency and robustness in dynamic satellite network environments.

To enhance the efficiency and quality of communications, it is crucial to employ digital pre-distortion (DPD) technology for linearizing the power amplifiers (PAs) in terahertz/mmWave transceivers. Previous studies have shown that training DPD models within the direct learning architecture (DLA) framework yields superior results, as the Transformer-based PA behavioral model in this framework can directly compute the inverse function. Owing to resource-constrained deployment environments, DPD models trained via DLA typically rely on alternative lightweight models—such as long short-term memory (LSTM) networks. However, in scenarios where only DLA is applicable, lightweight DPD models often suffer from limited linearization performance. To mitigate this limitation, drawing inspiration from related work on iterative learning control (ILC), we propose a simple yet effective cross-architecture knowledge distillation method in the DLA framework (dubbed CAKDDLA). In this method, the lightweight DPD model is trained using two sources: the PA behavioral model and input-output knowledge distilled from a teacher model. To fully verify the proposed method’s effectiveness, extensive experiments conducted on a 1-GHz dataset show that our approach outperforms baseline methods in terms of error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR), while also raising the upper bound of model performance.

To design reliable and efficient terahertz/mmWave transceivers, accurate power amplifier (PA) behavioral modeling is essential. In terahertz/millimeter wireless communication, the bandwidth will be 1 GHz or even more than 1 GHz, where PAs exhibit strong non-linearity and strong memory effects. This necessitates a powerful model capable of capturing long-term signal dependencies while managing strong non-linearity. To this end, we propose multi-scale augmented Transformer (MSAformer), which combines the ability of long short-term memory (LSTM) to capture complex sequence patterns with the self-attention mechanism’s dynamic attention adjustment, allowing it to effectively capture the intricate relationships between PA input and output signals. To fully validate the methods’ effectiveness, we collected and analyzed signals from the physical platform of the D-band system and set the bandwidth from 1 GHz to 4 GHz. Extensive behavioral modeling experiments on the collected datasets demonstrate that our method outperforms the existing methods in terms of normalized mean square error (NMSE). Further application in DPD scenarios proves that our method can effectively help improve the linearization performance of lightweight DPD models in terms of error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR).

Unmanned aerial vehicles (UAVs) have emerged as an important tool for communication research in recent years. However, they introduce new challenges for modern urban management. Conventional UAV detection methods, which rely on either vision-based systems or dedicated sensor networks, incur significant deployment complexity and high maintenance costs. To address these challenges, this paper proposes a communication signal-based UAV detection system. Given the poor signal quality passively scattered by the UAV, we employ signal processing techniques to enhance the feature extraction, while implementing customized modifications to the model architecture to accommodate the characteristics of complex-valued inputs. To validate our approach, we conducted comprehensive tests using a software-defined radio transceiver system constructed by USRP-2974 devices. Experimental results demonstrate that the proposed method outperforms other methods and achieves a detection accuracy surpassing 99% in real-world environments.

Due to the widespread applications and potential security risks, unmanned aerial vehicle (UAV) detection has received increasing attention in recent years. Among the various methods, one promising method is wireless sensing with channel state information (CSI). However, most CSI-based approaches are supervised and cannot fully utilize unlabeled data. To address this issue, this paper proposes a self-supervised learning method based on CSI. Utilizing a Transformer architecture, the method employs two decoders for prediction and reconstruction tasks to learn the features of the CSI. The pretrained model can be fine-tuned for UAV detection tasks with a small number of training samples to leverage unlabeled data. In experiments across multiple task scenarios, the pretrained model has achieved up to 100% accuracy when fine-tuned with the entire dataset, and approximately 80% accuracy when fine-tuned with only 1% of the data, which has surpassed those models without pretrained parameters. These results demonstrate that our pretraining method can enhance the accuracy of UAV detection while reducing dependency on labeled data.

The development of 5G and beyond puts forward higher requirements for indoor positioning. However, conventional angle of arrival (AoA) estimation algorithms still rely on the far-field assumption, which is not applicable and may lead to a loss of accuracy. In this paper, we investigate the near-field AoA estimation problem and propose the complex convolutional Kolmogorov-Arnold network (CCKAN). With complex convolution, our method enhances the intrinsic relationship of signal amplitude and phase by simulating complex-valued operation in the convolution. We also introduce Kolmogorov-Arnold network (KAN), a deep learning model with better interpretability, as a feature extraction block. Results show that CCKAN achieves higher accuracy than other baseline methods in the near-field AoA estimation task. Furthermore, the model also exhibits remarkable reliability in both near-field and far-field cases.

Low earth orbit (LEO) satellite communication system is vital due to its global coverage and low latency. To meet higher data rates, orthogonal frequency division multiplexing (OFDM) technology is recommended for adoption. In this paper, we analyze the nonlinear behavior of high-power amplifier (HPA) at Ka and Q/V frequency bands in wideband OFDM-based satellite communication system. A real satellite PA testing platform is constructed. Experimental results reveal that in wideband OFDM-based systems with high peak-to-average power ratio (PAPR), the conventional power back-off (PBO) method is no longer effective, particularly in mitigating in-band imbalance. Furthermore, we propose a low-complexity digital predistortion (DPD) scheme for satellite communication system. Experimental results demonstrate the robust performance of the proposed DPD.

To ensure high-quality communication, it’s of great value to use digital pre-distortion (DPD) to linearize the core component power amplifier (PA) of terahertz/mmWave transceiver. In this work, we propose transfer learning with Transformer and LSTM for DPD of terahertz/mmWave transceiver, which uses Transformer based PA behavioral model to train effective and lightweight LSTM model for DPD. To collect and analyze the signal data of terahertz/mmWave transceiver, we set up a physical platform for the D-band system. Through experiments, we show that the proposed method is capable of significantly reducing in-band and out-band distortion while avoiding the excessive complexity of DPD models.

Accurate near-real-time precipitation retrieval has been enhanced by satellite-based technologies.However, infrared-based algorithms have low accuracy due to weak relations with surface precipitation, whereas passive microwave and radar-based methods are more accurate but limited in range.This challenge motivates the Precipitation Retrieval Expansion (PRE) task, which aims to enable accurate, infrared-based full-disc precipitation retrievals beyond the scanning swath.We introduce Multimodal Knowledge Expansion, a two-stage pipeline with the proposed PRE-Net model.In the Swath-Distilling stage, PRE-Net transfers knowledge from a multimodal data integration model to an infrared-based model within the scanning swath via Coordinated Masking and Wavelet Enhancement (CoMWE).In the Full-Disc Adaptation stage, Self-MaskTune refines predictions across the full disc by balancing multimodal and full-disc infrared knowledge.Experiments on the introduced PRE benchmark demonstrate that PRE-Net significantly advanced precipitation retrieval performance, outperforming leading products like PERSIANN-CCS, PDIR, and IMERG.The code will be available at https://github.com/Zjut-MultimediaPlus/PRE-Net.

With their high mobility and ease of deployment, unmanned aerial vehicle (UAV)-assisted communication systems have emerged as a prominent area of academic research and a cornerstone technology for Sixth-Generation (6G) mobile communication networks. This paper investigates a multi-UAV downlink wireless communication system in which users exhibit random movement on the ground. To maximize the sum-rate of all users over the observation period, we propose a joint optimization framework that integrates user association, UAV 3D trajectory design, and power allocation, while addressing channel estimation across different timescales. In the long timescale, we model the UAV-user connections as a graph and utilize a graph neural network to jointly optimize user association and UAV trajectories. In the short timescale, we deploy a deep unfolding network for efficient channel estimation and power allocation. Simulation results validate the effectiveness of the proposed approach, showcasing significant performance improvements.

This paper investigates the design of integrated detection and communication waveforms with a particular focus on the exploitation of constructive interference (CI) at both the system and symbol levels. We formulate a waveform design problem with the objective of optimizing the detection performance which is characterized by the Kullback Leibler distance (KLD) between the probability density functions (PDFs) under two hypotheses, subject to the constraints of the CI condition and constant modulus. To address the challenging non-convex problem, a low-complexity recursive penalty-based Riemannian conjugate gradient (RE-PRCG) algorithm is proposed. The numerical results demonstrate the effectiveness of the proposed algorithm in improving the detection performance and reducing the computational burden over the benchmark.

Large-scale antenna arrays in millimeter wave (mmWave) systems are the cornerstone for next-generation Internet of Things (IoT) infrastructure. However, wideband mmWave systems suffer from frequency-dependent channel responses, known as beam squint. With beam squint effect, the far-field over-the-air (OTA) signal may exhibit frequency selective fading, rendering ineffective digital predistortion (DPD) design based on an OTA feedback structure. Therefore, existing mmWave DPD feedback architectures need to be examined carefully. In this article, we analyze the impact of beam squint on DPD and propose a proper DPD feedback architecture. To the best of our knowledge, this is the first work to address DPD in wideband mmWave systems with beam squint. Our results indicate that, with beam squint, the nonlinearity observed at the far-field OTA side differs from that at the power amplifier (PA) output side. We demonstrate that the far-field OTA received signal is no longer suitable as the feedback signal for DPD estimation. Moreover, we propose an effective DPD scheme for mmWave systems with beam squint. In this scheme, analog beamforming coefficients are fixed for DPD identification. Compared to existing solutions, hardware complexity of the proposed DPD scheme is much reduced. Numerical results validate the effectiveness of the proposed DPD scheme.

To meet the demand for high-speed and high-quality communication in next 6G satellite communication, it is very necessary and urgent to study the behavioral modeling of 6G satellite communication power amplifiers (PAs). In satellite communication, PAs face the situation of high dynamic and wide bandwidth and exhibit strong nonlinearity and strong memory effects. In this case, we need to study Transformer architectures that can better handle long sequence data and further explore the inherent characteristics of the PA signal data. In this article, we propose a behavioral modeling method of PAs named augmented real-valued time-delay transformer (ARVTDform). ARVTDform is an augmented transformer-based method, which can capture long-range dependencies between the PA signal data and has powerful nonlinear modeling capabilities. To simulate the working status of the satellite PAs, we set up two physical platforms and collect and analyze twelve datasets. To the best of our knowledge, this is the first time real satellite data has been used for behavioral modeling. Extensive experiments on the collected datasets further demonstrate that our transformer-based method is more suitable for handing PAs with strong nonlinearity and strong memory effects in terms of normalized mean square error (NMSE). Finally, we discuss the major challenge and list the potential future work that may contribute to the sustained development of high-performance transceivers.

With the sixth-generation mobile communication technology (6G) experiencing a shift of spatial expansion, the integration of terrestrial architecture and satellites has become one core feature of 6G. While the scarcity of spectrum resources has become a significant challenge hindering the advancement of integrated satellite-terrestrial networks, it is crucial to push the development of effective strategies to optimize spectrum utilization. This letter introduces a flexible spectrum sensing technique in the nonorthogonal multiple access (NOMA) uplink system for low-earth orbit (LEO) satellite-terrestrial communications, striking an optimal balance between under-sensing and over-sensing. Confronted with diverse complicated scenarios of multisatellite coverage, we conceive the operational principles and derive the sensing thresholds, exploiting spectrum holes and mitigating the fluctuation of false alarms. Notably, numerical outcomes showcase that the proposed technique outperforms the benchmarks, achieving a maximum throughput gain of 61.5% in dynamic communication environments.

This paper proposes a novel reconfigurable intelligent surface (RIS)-aided integrated communication and positioning design for orthogonal frequency division multiplexing systems in indoor scenarios. A non-geometric strategy is employed to realize accurate positioning. Specifically, location-related information is embedded into channel frequency responses (CFR) and estimated through regular pilot subcarriers. The coefficients of RIS are optimized to maximize the norm of the CFR vector differences among users, exclusively considering physically adjacent users. To enhance positioning accuracy, we propose a two-stage framework that incorporates the prior information about the user in physical space. A unique feature, named “correlation dispersion”, within this framework is leveraged to enhance performance compared to geometric-based methods. By transforming the geometric prior information into the frequency domain capitalizing on Gaussian kernel method, we derive the Cramer-Rao Lower Bound (CRLB) of the proposed framework. A notable gain in CRLB is observed, highlighting the efficacy. Theoretical comparison with the CRLB of conventional methods validates the correlation dispersion property. Simulation results demonstrate a significant improvement in positioning accuracy when meticulously combining prior information with a non-geometric positioning method. Furthermore, our results unveil that the incorporation of rough positioning methods yields exceptionally high positioning performance, provided that the location information depicts different aspects.

This paper investigates an integrated communication and positioning (ICAP) system facilitated by rate-splitting multiple access (RSMA). We propose encoding part of users’ messages into a common stream using a public codebook, which simultaneously facilitates fingerprint-based positioning. To enhance positioning accuracy, we investigate the interplay between geometric and non-geometric spatial consistency, which intriguingly leads to improved quality of imperfect channel state information (ICSI). In particular, we establish a novel strategy to significantly reduce the minimum mean square error of channel estimation via the Bayes pooling principle. We also demonstrate the theoretical equivalence between ICSI enhancement and positioning accuracy. Moreover, we develop a progressive transmission protocol that minimizes training overhead alongside ICSI enhancement strategy while allowing for the reallocation of spare resources without compromising designed performance. Our cooperative ICAP system leverages both reconfigurable intelligent surface and transmit precoding techniques to reconfigure the spatial consistency of the propagation space. Numerical results underscore advantages of proposed system in achieving the broadest Pareto boundary among existing ICAP designs. Furthermore, we reveal that RSMA bolsters communication capabilities while the ICSI enhancement strategy predominantly augments positioning.

This paper investigates the cooperative integrated communication and positioning (ICAP) within multiple-input single-output (MISO) systems. A novel strategy that leverages positioning outcomes is presented to enhance the quality of imperfect channel state information (ICSI), thereby bridging these two functionalities. Initially, a fingerprint-based method is developed to establish positioning function with the channel frequency responses serving as identification features. We focus on the interplay of geometric and non-geometric spatial consistency to improve positioning accuracy. To this end, reconfigurable intelligent surface (RIS) and transmit precoding techniques are employed to promote the non-geometric spatial consistency while ensuring communication quality. Moreover, we refine the distribution of random channel uncertainty through the Bayes pooling principle by leveraging the reshaped spatial consistency, resulting in an updated ICSI covariance matrix. This refinement is proven to significantly enhance the quality of ICSI, as evidenced by a reduction in the minimum mean square error. Furthermore, the study introduces a progressive transmission protocol that reduces training overhead in line with the channel enhancement strategy. Numerical results validate that the proposed protocol enhances the accuracy positioning by adjusting the power allocation without compromising the performance of communication. Moreover, opting for overhead reduction rather than directly utilizing enhanced ICSI quality demonstrates superior communication performance.

The deployment of low earth orbit (LEO) satellites megaconstellations presents a promising way for achieving global coverage and service, attributed to their comparatively low round-trip latency and launch costs. However, this surge in LEO satellite launches exacerbates the scarcity of the limited spectrum resources. Spectrum sharing between satellite constellations and terrestrial networks and beam hopping (BH) technology emerge as viable strategies to mitigate this spectrum shortage. To enhance spectrum efficiency and avoid serious inter-system interference, we investigate the beam hopping scheduling of satellites for interference avoidance. The beam hopping scheduling of the integrated satellite-terrestrial wireless networks system is formulated as throughput-driven beam hopping (TDBH) problem and satisfaction-rate-driven beam hopping (SDBH) problem, respectively. In particular, we decompose the TDBH problem into two sub-problems by relaxation, and a genetic algorithm (GA) is introduced to handle the SDBH problem. The impact of channel conditions and traffic load intensity on the satellite system throughput is analyzed in TDBH simulation. As for SDBH optimization problem, the simulation results show that the proposed GA algorithm improves the average traffic satisfaction rate by 16.96% at least, compared with other benchmarks and suits to scenarios with different traffic demands and fading channel conditions.

The performance of wireless communication systems is fundamentally constrained by random and uncontrollable wireless channels. Recently, reconfigurable intelligent surfaces (RIS) has emerged as a promising solution to enhance wireless network performance by smartly reconfiguring the radio propagation environment. While significant research has been conducted on RIS-assisted wireless systems, this paper focuses specifically on the deployment of RIS in a wideband millimeter wave (mmWave) multiple-input-multiple-output (MIMO) system to achieve maximum sum-rate. First, we derive the average user rate as well as the lower bound rate when the covariance of the channel follows the Wishart distribution. Based on the lower bound of users’ rate, we propose a heuristic method that transforms the problem of optimizing the RIS’s orientation into maximizing the number of users served by the RIS. Simulation results show that the proposed RIS deployment strategy can effectively improve the sum-rate. Furthermore, the performance of the proposed RIS deployment algorithm is only approximately 7.6% lower on average than that of the exhaustive search algorithm.

In this paper, we optimize the resource allocation for channel estimation and data transmission and the packet size to maximize the resource utilization efficiency subject to the constraints of ultra-reliable low-latency communications (URLLC). With practical channel estimation, the packet error probability (PEP) does not have a closed-form expression. To solve the problem, we develop novel model-based and model-free unsupervised deep learning algorithms to train a deep neural network for resource allocation and data transmission. Two types of reliability constraints are considered over a wireless link: 1) average PEP constraint; 2) constraint on the probability that PEP is higher than a threshold. The simulation results show that the learning algorithms can guarantee both types of reliability constraints. Compared with a benchmark that maximizes the number of symbols for data transmission and uses the maximum ratio transmission precoding, the learning method with the codebook-based precoding achieves a lower average signal-to-interference-plus-noise ratio (SINR), but improves the resource utilization efficiency by three times. It is because the resource utilization efficiency of URLLC is dominated by the tail distribution of SINR, not the average SINR, and the SINR of the benchmark has a much longer tail distribution than the learning method.

Next-generation sequencing (NGS) has revolutionized the detection of structural variation in genome. Among NGS strategies, read depth is widely used and paramorphism information contained inside is generally ignored. We develop an algorithm that can fully exploit both read depth and paramorphism information. We embed mutation procedure in our system model for estimating prior likelihood of single nucleotide base. Hidden Markov model (HMM) is used to connect single base into segments and belief propagation algorithm is performed for the optimal solution of the HMM model. Simulations show promising results in detecting important types of structural variation. We have applied the algorithm on the maize B73 and MO17 genome data and compared the results with those obtained from array CGH method based micro-array data. Inconsistency between the two sets of data is discussed.

Visible light communications (VLC) rely on white light emitting diodes (LEDs) to provide communication and illumination simultaneously. Orthogonal frequency division multiplexing (OFDM) enables bit loading in VLC to make the best use of available modulation bandwidth of white LEDs. However, OFDM signals in VLC exhibit high peak-to-average power ratio (PAPR) as in radio frequency (RF) communications. VLC-OFDM differs from RF-OFDM in that the baseband signals are real-valued and there are two PAPRs to be reduced, namely upper PAPR and lower PAPR. Moreover, upper PAPR and lower PAPR are not independently distributed, and are often subject to asymmetric constraints. In this paper, we propose a distortion-based PAPR reduction scheme to minimize the weighted upper PAPR and lower PAPR in VLC-OFDM. The proposed method takes bit loading into consideration that ensures allocating less distortions to subcarriers with more bits loaded (higher order constellations).

Recently, visible light communication (VLC) has attracted much attention as a possible candidate technology to meet the ever growing demand in wireless data. However, current low-cost white LED has limited modulation bandwidth, which limits the throughput of the VLC. Optical MIMO can provide spatial diversity and thus achieve high data rate. Traditional multiple-input multiple-output (MIMO) techniques used in wireless communications cannot be directly applied to VLC. This paper studies the precoder and equalizer design of optical wireless MIMO system for VLC. First, we propose a MIMO VLC system, which can effectively support the flickering/dimming control and other VLC-specific requirements. Second, besides the transceiver design with perfect channel state information, we also take into account channel uncertainties for joint optimization in the MIMO VLC system. Numerical results show that the proposed MIMO solution for VLC is robust to combat the influence caused by the channel estimation imperfection. By taking into account the channel estimation errors, the proposed joint optimization method demonstrates the bit error rate (BER) improvements in the scenario of imperfect channel estimation.

BACKGROUND: A copy number variation (CNV) is a difference between genotypes in the number of copies of a genomic region. Next generation sequencing (NGS) technologies provide sensitive and accurate tools for detecting genomic variations that include CNVs. However, statistical approaches for CNV identification using NGS are limited. We propose a new methodology for detecting CNVs using NGS data. This method (henceforth denoted by m-HMM) is based on a hidden Markov model with emission probabilities that are governed by mixture distributions. We use the Expectation-Maximization (EM) algorithm to estimate the parameters in the model. RESULTS: A simulation study demonstrates that our proposed m-HMM approach has greater power for detecting copy number gains and losses relative to existing methods. Furthermore, application of our m-HMM to DNA sequencing data from the two maize inbred lines B73 and Mo17 to identify CNVs that may play a role in creating phenotypic differences between these inbred lines provides results concordant with previous array-based efforts to identify CNVs. CONCLUSIONS: The new m-HMM method is a powerful and practical approach for identifying CNVs from NGS data.

Intra-cell interference management is one of the technical challenges for device-to-device (D2D) communication underlaying cellular networks. In this paper, we propose two superposition coding-based cooperative relaying schemes to exploit the transmission opportunities for the D2D users without deteriorating the performance of the cellular users. In the first scheme, the D2D transmitter (DT) is enabled to decode and regenerate the cellular signal, and transmit the cellular signal by superposing it with its own signal. In this way, the interference from the D2D pair to the cellular pair can be canceled by properly allocating time and power. To further exploit the transmission opportunity for the D2D pair, in the second scheme the cellular transmitter splits its signal into two parts and broadcasts these two parts in a superposition signal. DT relays only one part of the cellular signal. Analytic and numerical results confirm the efficiency of the proposed schemes.

Mobile and wireless communication has been evolved from the basic model for providing point-to-point voice centric services into more complicated service provision technologies with point-to-multipoint transmission mode, such as MBMS in 3GPP and BCMCS in 3GPP2. However, the group member with the worst channel condition becomes the bottleneck since the base station or access point is supposed to take care of all the users in a multicast group whose members would like to receive a same service. On the other hand, delay is one of the most important QoS criteria along with throughput especially for realtime services such as mobile TV and IP conference. In this paper, we propose a framework for performance analysis of multicast service with delay constraints. Additionally, an admission control policy for real-time multicast scheduling is demonstrated.

The heterogeneous multicast networks are the networks with several wireless technologies that provide multicast service. Due to the high data rate of the service, point-to-point (P2P) transmission mode, which is employed by unicast channels, is no longer the best mode because of its inefficiency in power consumption and spectrum utilization. Compared with P2P, point-to-multipoint (P2M) transmission mode is recognized as a more promising mode for multicast service. But since P2M mode is provided by mulitcast channels who have turn-on thresholds, the P2M link of a transmission point (TP) is not always available for User Equipments (UEs). To deal with the UEs' cell/transmission mode selection problem in heterogeneous multicast networks, this paper proposes a novel auction mechanism. In the auction process, TPs act as auctioneers who own channel resource and UEs act as bidders who wish to buy these channels for multicast service. Different from many auction based mechanism, we design two kinds of prices, one for unicast channels providing P2P links and the other for multicast channels providing P2M links. In auction, the equilibrium is defined as the state that no UE wishes to change it decision. Through simulation, we show that the auction process converges to a equilibrium state, and the mechanism achieves about 40%-50% power efficiency compared with that of random access.

Mobile and wireless communication has been evolved from the basic model for providing point-to-point (PtP) voice centric services into more complicated service provision technologies with point-to- multipoint (PtM) transmission mode, such as MBMS in 3GPP and BCMCS in 3GPP2. On the other hand, the evolution of mobile and wireless communication has also resulted in a large number of wireless technologies such as UMTS cellular network, WiMAX and WLAN. As a result, issues related to multicast/broadcast service over heterogeneous networks will be more complex, interesting and valuable. In this paper, we propose a solution aimed to provide seamless and ubiquitous support for multicast/broadcast service on heterogeneous accesses. Additionally, a vertical handover policy named PtM First for multicast resource management in heterogeneous networks is demonstrated.