arXiv subjects

Mugen Peng

Publications and source records attributed to Mugen Peng.

At least 37 records · Page 2Linked to original sources

arXiv2025

Turbulent Multiple-Scattering Channel Modeling for Ultraviolet Communications: A Monte-Carlo Integration Approach

Modeling of multiple-scattering channels in atmospheric turbulence is essential for the performance analysis of long-distance non-line-of-sight (NLOS) ultraviolet (UV) communications. Existing works on the turbulent channel modeling for NLOS UV communications either focused on single-scattering cases or estimate the turbulent fluctuation effect in an unreliable way based on Monte-Carlo simulation (MCS) approach. In this paper, we establish a comprehensive turbulent multiple-scattering channel model by using a more efficient Monte-Carlo integration (MCI) approach for NLOS UV communications, where both the scattering, absorption, and turbulence effects are considered. Compared with the MCS approach, the MCI approach is more interpretable for estimating the turbulent fluctuation. To achieve this, we first introduce the scattering, absorption, and turbulence effects for NLOS UV communications in turbulent channels. Then we propose the estimation methods based on MCI approach for estimating both the turbulent fluctuation and the distribution of turbulent fading coefficient. Numerical results demonstrate that the turbulence-induced scattering effect can always be ignored for typical UV communication scenarios. Besides, the turbulent fluctuation will increase as either the communication distance increases or the zenith angle decreases, which is compatible with existing experimental results and also with our experimental results. Moreover, we demonstrate numerically that the distribution of the turbulent fading coefficient for UV multiple-scattering channels under all turbulent conditions can be approximated as log-normal distribution; and we also demonstrate both numerically and experimentally that the turbulent fading can be approximated as a Gaussian distribution under weak turbulence.

eess.SY↗

arXiv2025

Online Specific Emitter Identification via Collision-Alleviated Signal Hash

Specific Emitter Identification (SEI) has been widely studied, aiming to distinguish signals from different emitters given training samples from those emitters. However, real-world scenarios often require identifying signals from novel emitters previously unseen. Since these novel emitters only have a few or no prior samples, existing models struggle to identify signals from novel emitters online and tend to bias toward the distribution of seen emitters. To address these challenges, we propose the Online Specific Emitter Identification (OSEI) task, comprising both online \revise{few-shot and generalized zero-shot} learning tasks. It requires constructing models using signal samples from seen emitters and then identifying new samples from seen and novel emitters online during inference. We propose a novel hash-based model, Collision-Alleviated Signal Hash (CASH), providing a unified approach for addressing the OSEI task. The CASH operates in two steps: in the seen emitters identifying step, a signal encoder and a seen emitters identifier determine whether the signal sample is from seen emitters, mitigating the model from biasing toward seen emitters distribution. In the signal hash coding step, an online signal hasher assigns a hash code to each signal sample, identifying its specific emitter. Experimental results on real-world signal datasets (i.e., ADSB and ORACLE) demonstrate that our method accurately identifies signals from both seen and novel emitters online. This model outperforms existing methods by a minimum of 6.08\% and 8.55\% in accuracy for the few-shot and \revise{generalized zero-shot learning }tasks, respectively. The code will be open-sourced at \href{https://github.com/IntelliSensing/OSEI-CASH}{https://github.com/IntelliSensing/OSEI-CASH}.

eess.SP↗

arXiv2025

Multi-Stage CD-Kennedy Receiver for QPSK Modulated CV-QKD in Turbulent Channels

Continuous variable-quantum key distribution (CV-QKD) protocols attract increasing attentions in recent years because they enjoy high secret key rate (SKR) and good compatibility with existing optical communication infrastructure. Classical coherent receivers are widely employed in coherent states based CV-QKD protocols, whose detection performance is bounded by the standard quantum limit (SQL). Recently, quantum receivers based on displacement operators are experimentally demonstrated with detection performance outperforming the SQL in various practical conditions. However, potential applications of quantum receivers in CV-QKD protocols under turbulent channels are still not well explored, while practical CV-QKD protocols must survive from the atmospheric turbulence in satellite-to-ground optical communication links. In this paper, we consider the possibility of using a quantum receiver called multi-stage CD-Kennedy receiver to enhance the SKR performance of a quadrature phase shift keying (QPSK) modulated CV-QKD protocol in turbulent channels. We first derive the error probability of the multi-stage CD-Kennedy receiver for detecting QPSK signals in turbulent channels and further propose three types of multi-stage CD-Kennedy receiver with different displacement choices, i.e., the Type-I, Type-II, and Type-III receivers. Then we derive the SKR of a QPSK modulated CV-QKD protocol using the multi-stage CD-Kennedy receiver and post-selection strategy in turbulent channels. Numerical results show that the multi-stage CD-Kennedy receiver can outperform the classical coherent receiver in turbulent channels in terms of both error probability and SKR performance and the Type-II receiver can tolerate worse channel conditions compared with Type-I and Type-III receivers in terms of error probability performance.

eess.SP↗

arXiv2025

Channel Modeling of Satellite-to-Underwater Laser Communication Links: An Analytical-Monte Carlo Hybrid Approach

Channel modeling for satellite-to-underwater laser communication (StULC) links remains challenging due to long distances and the diversity of the channel constituents. The StULC channel is typically segmented into three isolated channels: the atmospheric channel, the air-water interface channel, and the underwater channel. Previous studies involving StULC channel modeling either focused on separated channels or neglected the combined effects of particles and turbulence on laser propagation. In this paper, we established a comprehensive StULC channel model by an analytical-Monte Carlo hybrid approach, taking into account the effects of both particles and turbulence. We first obtained the intensity distribution of the transmitted laser beam after passing through the turbulent atmosphere based on the extended Huygens-Fresnel principle. Then we derived a closed-form probability density function of the photon propagating direction after passing through the air-water interface, which greatly simplified the modeling of StULC links. At last, we employed a Monte Carlo method to model the underwater links and obtained the power distribution at the receiving plane. Based on the proposed StULC channel model, we analyzed the bit error rate and the outage probability under different environmental conditions. Numerical results demonstrated that, the influence of underwater particle concentration on the communication performance is much pronounced than those of both the atmospheric turbulence and the underwater turbulence. Notably, increasing the wind speed at the air-water interface does not significantly worsen the communication performance of the StULC links.

eess.SP↗

arXiv2025

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial

The rapid advancement toward sixth-generation (6G) wireless networks has significantly intensified the complexity and scale of optimization problems, including resource allocation and trajectory design, often formulated as combinatorial problems in large discrete decision spaces. However, traditional optimization methods, such as heuristics and deep reinforcement learning (DRL), struggle to meet the demanding requirements of real-time adaptability, scalability, and dynamic handling of user intents in increasingly heterogeneous and resource-constrained network environments. Large language models (LLMs) present a transformative paradigm by enabling natural language-driven problem formulation, context-aware reasoning, and adaptive solution refinement through advanced semantic understanding and structured reasoning capabilities. This paper provides a systematic and comprehensive survey of LLM-enabled optimization frameworks tailored for wireless networks. We first introduce foundational design concepts and distinguish LLM-enabled methods from conventional optimization paradigms. Subsequently, we critically analyze key enabling methodologies, including natural language modeling, solver collaboration, and solution verification processes. Moreover, we explore representative case studies to demonstrate LLMs' transformative potential in practical scenarios such as optimization formulation, low-altitude economy networking, and intent networking. Finally, we discuss current research challenges, examine prominent open-source frameworks and datasets, and identify promising future directions to facilitate robust, scalable, and trustworthy LLM-enabled optimization solutions for next-generation wireless networks.

cs.NI↗

arXiv2025

Uninformed-to-Informed Estimation: A Ping-Pong Positioning Method for Multi-user Wideband mmWave Systems

To enhance the positioning and tracking performance of dynamic user equipment (UE) in wideband millimeter-wave (mmWave) systems, we propose a novel positioning error lower bound (PELB)-driven ping-pong positioning framework, where the base station (BS) and UE alternately transmit and receive adaptive beamforming signals for positioning. All beam-formers are scheduled based on the locally evaluated PELB. In this framework, we exploit multi-dimensional information fusion to assist in positioning. Firstly, a multi-subcarrier collaborative positioning error lower bound (MSCPEB) is proposed to evaluate the positioning error limits of wideband mmWave systems, which quantifies the contribution of all subcarriers to positioning accuracy. Moreover, we prove that the MSCPEB does not exceed the arithmetic mean of the PELBs of the individual subcarriers. Subsequently, we develop an alternating optimization (AO) algorithm to optimize the hybrid beamformers targeted for MSCPEB minimization. By convexifying this problem, closed-form solutions of beamformers are derived. Finally, we develop a multipath collaborative positioning method that quantifies the impact of path reliability on positioning accuracy, with a closed-form solution for user position derived. The proposed method does not rely on path resolution and traditional triangular relationships. Numerical results validate that the proposed method improves estimation accuracy by at least 16% compared to potential schemes without optimized beam configurations, while requiring only approximately one-quarter of the slot resources.

eess.SP↗

arXiv2025

Generalized Kennedy Receivers Enhanced CV-QKD in Turbulent Channels for Endogenous Security of Space-Air-Ground Integrated Network

Endogenous security in next-generation wireless communication systems attracts increasing attentions in recent years. A typical solution to endogenous security problems is the quantum key distribution (QKD), where unconditional security can be achieved thanks to the inherent properties of quantum mechanics. Continuous variable-quantum key distribution (CV-QKD) enjoys high secret key rate (SKR) and good compatibility with existing optical communication infrastructure. Traditional CV-QKD usually employ coherent receivers to detect coherent states, whose detection performance is restricted to the standard quantum limit. In this paper, we employ a generalized Kennedy receiver called CD-Kennedy receiver to enhance the detection performance of coherent states in turbulent channels, where equal-gain combining (EGC) method is used to combine the output of CD-Kennedy receivers. Besides, we derive the SKR of a post-selection based CV-QKD protocol using both CD-Kennedy receiver and homodyne receiver with EGC in turbulent channels. We further propose an equivalent transmittance method to facilitate the calculation of both the bit-error rate (BER) and SKR. Numerical results show that the CD-Kennedy receiver can outperform the homodyne receiver in turbulent channels in terms of both BER and SKR performance. We find that BER and SKR performance advantage of CD-Kennedy receiver over homodyne receiver demonstrate opposite trends as the average transmittance increases, which indicates that two separate system settings should be employed for communication and key distribution purposes. Besides, we also demonstrate that the SKR performance of a CD-Kennedy receiver is much robust than that of a homodyne receiver in turbulent channels.

quant-ph↗

arXiv2025

LAURA: LLM-Assisted UAV Routing for AoI Minimization

With the rapid growth of the low-altitude economy, there is increasing demand for real-time data collection using UAV-assisted wireless sensor networks. This paper investigates the problem of minimizing the age of information (AoI) in UAV-assisted wireless sensor networks by optimizing the UAV flight routing. We formulate the AoI minimization task and propose a large language model (LLM)-assisted UAV routing algorithm (LAURA). LAURA employs an LLM as intelligent crossover operators within an evolutionary optimization framework to efficiently explore the solution space. Simulation results show that LAURA outperforms benchmark methods in reducing the maximum AoI, especially in scenarios with a large number of sensor nodes.

cs.NI↗

arXiv2025

Satellite-Terrestrial Integrated Fog Networks: Architecture, Technologies, and Challenges

In the evolution of sixth-generation (6G) mobile communication networks, satellite-terrestrial integrated networks emerge as a promising paradigm, characterized by their wide coverage and reliable transmission capabilities. By integrating with cloud-based terrestrial mobile communication networks, the limitations of low Earth orbit (LEO) satellites, such as insufficient onboard computing capabilities and limited inter-satellite link capacity, can be addressed. In addition, to efficiently respond to the diverse integrated tasks of communication, remote sensing, and navigation, LEO constellations need to be capable of autonomous networking. To this end, this article presents a satellite-terrestrial integrated fog network for 6G. Its system architecture and key technologies are introduced to achieve flexible collaboration between fog satellites and terrestrial cloud computing centers. In particular, key techniques with diverse challenges and their corresponding solutions are discussed, including integrated waveform design and resource management based on fog satellite onboard processing, as well as mobility management and native artificial intelligence based on cloud-fog collaboration. Finally, future challenges and open issues are outlined.

cs.NI↗

arXiv2025

Cache-Aware Cooperative Multicast Beamforming in Dynamic Satellite-Terrestrial Networks

With the burgeoning demand for data-intensive services, satellite-terrestrial networks (STNs) face increasing backhaul link congestion, deteriorating user quality of service (QoS), and escalating power consumption. Cache-aided STNs are acknowledged as a promising paradigm for accelerating content delivery to users and alleviating the load of backhaul links. However, the dynamic nature of low earth orbit (LEO) satellites and the complex interference among satellite beams and terrestrial base stations pose challenges in effectively managing limited edge resources. To address these issues, this paper proposes a method for dynamically scheduling caching and communication resources, aiming to reduce network costs in terms of transmission power consumption and backhaul traffic, while meeting user QoS demands and resource constraints. We formulate a mixed timescale problem to jointly optimize cache placement, LEO satellite beam direction, and cooperative multicast beamforming among satellite beams and base stations. To tackle this intricate problem, we propose a two-stage solution framework, where the primary problem is decoupled into a short-term content delivery subproblem and a long-term cache placement subproblem. The former subproblem is solved by designing an alternating optimization approach with whale optimization and successive convex approximation methods according to the cache placement state, while cache content in STNs is updated using an iterative algorithm that utilizes historical information. Simulation results demonstrate the effectiveness of our proposed algorithms, showcasing their convergence and significantly reducing transmission power consumption and backhaul traffic by up to 52%.

cs.NI↗

arXiv2025

Joint Communication and Radar Sensing for Terahertz Space-Air-Ground Integrated Networks (SAGIN)

The transition from isolated systems to integrated solutions has driven the development of space-air-ground integrated networks (SAGIN) as well as the integration of communication and radar sensing functionalities. By leveraging the unique properties of the Terahertz (THz) band, THz joint communication and radar sensing (JCRS) supports high-speed communication and precise sensing, addressing the growing demands of SAGIN for connectivity and environmental awareness. However, most existing THz studies focus on terrestrial and static scenarios, with limited consideration for the dynamic and non-terrestrial environments of SAGIN. In this paper, the THz JCRS techniques for SAGIN are comprehensively investigated. Specifically, propagation characteristics and channel models of THz waves in non-terrestrial environments are analyzed. A link capacity comparison with millimeter-wave, THz, and free-space optical frequency bands is conducted to highlight the advantages of THz frequencies. Moreover, novel JCRS waveform design strategies are presented to achieve mutual merit of communication and radar sensing, while networking strategies are developed to overcome challenges in SAGIN such as high mobility. Furthermore, advancements in THz device technologies, including antennas and amplifiers, are reviewed to assess their roles in enabling practical JCRS implementations.

eess.SP↗

arXiv2024

UniRS: Unifying Multi-temporal Remote Sensing Tasks through Vision Language Models

The domain gap between remote sensing imagery and natural images has recently received widespread attention and Vision-Language Models (VLMs) have demonstrated excellent generalization performance in remote sensing multimodal tasks. However, current research is still limited in exploring how remote sensing VLMs handle different types of visual inputs. To bridge this gap, we introduce \textbf{UniRS}, the first vision-language model \textbf{uni}fying multi-temporal \textbf{r}emote \textbf{s}ensing tasks across various types of visual input. UniRS supports single images, dual-time image pairs, and videos as input, enabling comprehensive remote sensing temporal analysis within a unified framework. We adopt a unified visual representation approach, enabling the model to accept various visual inputs. For dual-time image pair tasks, we customize a change extraction module to further enhance the extraction of spatiotemporal features. Additionally, we design a prompt augmentation mechanism tailored to the model's reasoning process, utilizing the prior knowledge of the general-purpose VLM to provide clues for UniRS. To promote multi-task knowledge sharing, the model is jointly fine-tuned on a mixed dataset. Experimental results show that UniRS achieves state-of-the-art performance across diverse tasks, including visual question answering, change captioning, and video scene classification, highlighting its versatility and effectiveness in unifying these multi-temporal remote sensing tasks. Our code and dataset will be released soon.

cs.CV↗

arXiv2024

Sensing-Assisted Beam Tracking with Real-Time Beamwidth Adaptation for THz Communications

Terahertz (THz) communications, with their substantial bandwidth, are essential for meeting the ultra-high data rate demands of emerging high-mobility scenarios such as vehicular-to-everything (V2X) networks. In these contexts, beamwidth adaptation has been explored to address the problem that high-mobility targets frequently move out of the narrow THz beam range. However, existing approaches cannot effectively track targets due to a lack of real-time motion awareness. Consequently, we propose a sensing-assisted beam tracking scheme with real-time beamwidth adaptation. Specifically, the base station (BS) periodically collects prior sensing information to predict the target's motion path by applying a particular motion model. Then, we build a pre-calculated codebook by optimising precoders to align the beamwidth with various predicted target paths, thereby maximising the average achievable data rates within each sensing period. Finally, the BS selects the optimal precoder from the codebook to maintain stable and continuous connectivity. Simulation results show that the proposed scheme significantly improves the rate performance and reduces outage probability compared to existing approaches under various target mobility.

eess.SP↗

arXiv2024

Joint Beamforming Design and Satellite Selection for Integrated Communication and Navigation in LEO Satellite Networks

Relying on the powerful communication capabilities and rapidly changing geometric configuration, the Low Earth Orbit (LEO) satellites have the potential to offer integrated communication and navigation (ICAN) services. However, the isolated resource utilization in the traditional satellite communication and navigation systems has led to a compromised system performance. Against this backdrop, this paper formulates a joint beamforming design and satellite selection optimization problem for the LEO-ICAN network to maximize the sum rate, while simultaneously reconciling the positioning performance. A two-layer algorithm is proposed, where the beamforming design in the inner layer is solved by the difference-of-convex programming method to maximize the sum rate, and the satellite selection in the outer layer is modeled as a coalition formation game to simultaneously reconcile the positioning performance. Simulation results verify the superiority of our proposed algorithms by increasing the sum rate by 16.6% and 29.3% compared with the conventional beamforming and satellite selection schemes, respectively.

eess.SP↗

arXiv2024

Blockchain-Enabled Dynamic Spectrum Sharing for Satellite and Terrestrial Communication Networks

Dynamic spectrum sharing (DSS) between satellite and terrestrial networks has increasingly engaged the academic and industrial sectors. Nevertheless, facilitating secure, efficient and scalable sharing continues to pose a pivotal challenge. Emerging as a promising technology to bridge the trust gap among multiple participants, blockchain has been envisioned to enable DSS in a decentralized manner. However, satellites with limited resources may struggle to support the frequent interactions required by blockchain networks. Additionally,given the extensive coverage of satellites, spectrum sharing needs vary by regions, challenging traditional blockchain approaches to accommodate differences. In this work, a partitioned, self-governed, and customized dynamic spectrum sharing approach (PSC-DSS) is proposed for spectrum sharing between satellite access networks and terrestrial access networks. This approach establishes a sharded and tiered architecture which allows various regions to manage spectrum autonomously while jointly maintaining a single blockchain ledger. Moreover, a spectrum-consensus integrated mechanism, which decouples DSS process and couples it with blockchain consensus protocol, is designed to enable regions to conduct DSS transactions in parallel and dynamically innovate spectrum sharing schemes without affecting others. Furthermore, a theoretical framework is derived to justify the stability performance of PSC-DSS. Finally, simulations and experiments are conducted to validate the advantageous performance of PSC-DSS in terms of low-overhead, high efficiency, and robust stability.

cs.DC↗

arXiv2024

Wideband Beamforming with RIS: A Unified Framework via Space-Frequency Transformation

The spectrum shift from the sub-6G band to the high-frequency band has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is particularly challenging in reconfigurable intelligent surface (RIS)-assisted systems, due to that RIS is not capable of performing frequency-dependent phase shift, therefore inducing high signal processing complexity. In this paper, we propose a simple-yet-efficient wideband beamforming design for RIS-assisted systems, in which a transmitter sends wideband signals to a desired target, through the aid of the RIS. In our proposed design, we exploit space-frequency Fourier transformation and stationary phase method to yield an approximate closed-form solution of the RIS phase shifts which significantly reduces the signal processing complexity, compared to the existing approaches. The obtained solution is then used to generate a large and flat beampattern over the desired frequency band. Through numerical results, we validate the effectiveness of our proposed beamforming design and demonstrate how it can improve system performances in terms of communication rate and sensing resolution. Beyond generating the flat beampattern, we highlight that our proposed design is capable of mimicking any desired beampattern by matching the RIS phase shift with the amplitude modulation function, thus providing valuable insights into the design of novel wideband beamforming for RIS-assisted systems.

cs.CE↗

arXiv2024

Detection and Multi-Parameter Estimation for NLOS Targets: An IRS-assisted Framework

Intelligent reflecting surface (IRS) has the potential to enhance sensing performance, due to its capability of reshaping the echo signals. Different from the existing literature, which has commonly focused on IRS beamforming optimization, in this paper, we pay special attention to designing effective signal processing approaches to extract sensing information from IRS-reshaped echo signals. To this end, we investigate an IRS-assisted non-line-of-sight (NLOS) target detection and multi-parameter estimation problem in orthogonal frequency division multiplexing (OFDM) systems. To address this problem, we first propose a novel detection and direction estimation framework, including a low-overhead hierarchical codebook that allows the IRS to generate three-dimensional beams with adjustable beam direction and width, a delay spectrum peak-based beam training scheme for detection and direction estimation, and a beam refinement scheme for further enhancing the accuracy of the direction estimation. Then, we propose a target range and velocity estimation scheme by extracting the delay-Doppler information from the IRS-reshaped echo signals. Numerical results demonstrate that the proposed schemes can achieve 99.7% target detection rate, a 10^{-3}-rad level direction estimation accuracy, and a 10^{-6}-m/10^{-5}-m/s level range/velocity estimation accuracy.

eess.SP↗

arXiv2024

Rethinking the fundamental performance limits of integrated sensing and communication systems

Integrated sensing and communication (ISAC) has been recognized as a key enabler and feature of future wireless networks. In the existing works analyzing the performances of ISAC, discrete-time systems were commonly assumed, which, however, overlooked the impacts of temporal, spectral, and spatial properties. To address this issue, we establish a unified information model for the band-limited continuous-time ISAC systems. In the established information model, we employ a novel sensing performance metric, called the sensing mutual information (SMI). Through analysis, we show how the SMI can be utilized as a bridge between the mutual information domain and the mean squared error (MSE) domain. In addition, we illustrate the communication mutual information (CMI)-SMI and CMI-MSE regions to identify the performance bounds of ISAC systems in practical settings and reveal the trade-off between communication and sensing performances. Moreover, via analysis and numerical results, we provide two valuable insights into the design of novel ISAC-enabled systems: i) communication prefers the waveforms of random amplitude, sensing prefers the waveforms of constant amplitude, both communication and sensing favor the waveforms of low correlations with random phases; ii) There exists a linear positive proportional relationship between the allocated time-frequency resource and the achieved communication rate/sensing MSE.

cs.IT↗