arXiv subjects

Bang Huang

Publications and source records attributed to Bang Huang.

17 recordsLinked to original sources

arXiv2026

Agentic AI-Enabled Solar-Powered High-Altitude Platforms for Sustainable SAGINs

Space-Air-Ground Integrated Networks (SAGINs) can extend connectivity, but their communication, computing, and platform operations create tightly coupled energy demands. Solar-powered High-Altitude Platforms (HAPs) offer a promising middle layer by combining persistent regional coverage, renewable-energy harvesting, and onboard computing. However, realizing this potential requires more than optimizing individual links or processors, as radio transmission, task execution, backhaul use, and battery preservation share a common energy budget. Therefore, we introduce a HAP-native Agentic AI framework. It continuously perceives communication, computing, energy, mobility, and mission states; invokes quantitative tools for prediction and verification; and coordinates executable actions through a closed control loop. Then, a multi-timescale design separates fast radio control from task orchestration and long-term energy planning. Furthermore, a disaster-recovery case study illustrates how the framework responds to backhaul congestion, traffic surges, and declining solar generation, improving energy efficiency, task completion, and latency over other baselines. We finally identify trustworthy control, collaborative multi-HAP orchestration, and digital-twin-assisted lifelong adaptation as key steps toward deployable, sustainable, and resilient SAGIN intelligence.

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arXiv2026

Joint Beamforming, Energy Management, and Trajectory Optimization for Figure-Eight Loitering in Solar-Powered HAPS-Enabled ISAC Systems

Solar-powered high-altitude platform stations (HAPSs) provide a promising platform for integrated sensing and communication (ISAC) owing to their wide-area coverage and long-endurance operation. This paper proposes a solar-powered HAPS-enabled ISAC framework for sustainable day-night operation, where a figure-eight loitering architecture is adopted to provide persistent ISAC services over geographically separated regions while harvesting solar energy. A unified communication-sensing-energy model is developed by jointly characterizing solar energy harvesting, battery dynamics, propulsion power consumption, communication transmission, and synthetic aperture radar (SAR) imaging. Based on this model, coupled optimization problems are formulated for daytime operation (DTO) and nighttime operation (NTO), where the battery state bridges the two operational phases through a long-term energy budget. The proposed framework jointly optimizes communication, sensing, mobility, and energy management to maximize daytime communication performance while minimizing nighttime propulsion energy consumption. Efficient iterative algorithms are developed to solve the resulting non-convex optimization problems. Simulation results verify the effectiveness of the proposed communication-sensing-energy co-design and demonstrate that the proposed framework effectively supports sustainable day-night ISAC operation.

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arXiv2026

Joint 3D Trajectory and Power Allocation for HAPs-UAV Bistatic ISARAC in Low-Altitude Networks

This paper investigates joint three-dimensional (3D) trajectory planning and resource allocation for a high-altitude platform (HAPs)-unmanned aerial vehicle (UAV) bistatic integrated synthetic aperture radar (SAR) and communication (ISARAC) system in low-altitude networks. In the proposed architecture, the HAPs provides persistent wide-area connectivity by transmitting ISARAC waveforms for ground-user communications, while a low-altitude UAV exploits its proximity and mobility to passively collect ground-target echoes for high-resolution SAR imaging. We formulate a sum-rate maximization problem for ground users subject to stringent SAR imaging signal-to-noise ratio (SNR) and resolution requirements, a total energy budget for ISARAC transmission, and UAV dynamic constraints. The resulting problem is inherently nonconvex. To tackle it, an alternating optimization (AO) framework is developed, where the power-allocation subproblem with fixed UAV states admits a closed-form water-filling solution, while the UAV trajectory optimization with fixed transmit powers is handled via successive convex approximation (SCA) and difference-of-convex (DC) programming. Simulation results verify the effectiveness of the proposed approach and demonstrate its capability to jointly support persistent communication coverage and high-resolution sensing in low-altitude network scenarios.

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arXiv2026

Frequency Diverse Arrays: Fundamentals, Key Insights, and Future Directions

Frequency diverse arrays (FDA) have attracted sustained interest as a promising architecture for introducing range-dependent responses into array systems. Unlike conventional phased arrays (PA), whose transmit behavior is primarily angle-dependent, FDA employs inter-element frequency offsets to generate time-and range-dependent phase structures, thereby producing a joint time-range-angle array response. Despite extensive research, the physical meaning of FDA-induced degrees of freedom remains debated, particularly in relation to range-angle coupling, the feasibility of time-invariant focusing, and the distinction between frequency-driven and waveform-driven range selectivity. This paper reexamines FDA from a structural and manifold-based perspective. A central contribution is the introduction of an irreducibility criterion, which distinguishes genuine range-domain physical degrees of freedom from effects that can be reproduced by equivalent signal-processing transformations. Based on this perspective, PA, multiple-input multiple-output (MIMO), FDA, and FDA-MIMO are comparatively interpreted according to the physical origin of their effective degrees of freedom, including spatial phase, waveform orthogonality, frequency gradients, and their interaction. The paper further clarifies the role of frequency across different array paradigms, contrasts FDA with time-coding-based architectures, and explains how key FDA properties such as manifold expansion, range--angle coupling, time variation, and multi-frequency diversity translate into system capabilities. Building on these structural insights, the paper connects FDA to a broad range of radar and communication functionalities, including parameter estimation, target detection, imaging, physical-layer security, and integrated sensing and communication.

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arXiv2026

Geometry-Aware Networking for Low-Altitude Economy: Movable Antennas in Space-Air-Ground Integrated Systems

Space--air--ground integrated networks (SAGINs) are emerging as a key foundation for future non-terrestrial networks (NTNs) and low-altitude economy services. However, their performance is increasingly limited not only by communication resources, but by the inability to adapt to rapidly changing spatial geometry. Here, spatial geometry refers to the relative configuration among network nodes, obstacles, and targets, which directly determines propagation conditions, blockage states, interference patterns, and sensing observability.This trend becomes more pronounced as low-altitude operations grow in density and complexity, causing the dominant bottleneck to shift from static resource allocation toward real-time maintenance of favorable spatial geometry across layers.In this article, we argue that movable antenna (MA) technology provides a fundamentally new perspective for SAGIN design. By enabling controlled antenna displacement, MA introduces a spatial degree of freedom that allows the network to directly adapt local spatial geometry at fine granularity, rather than passively reacting to it through beamforming or platform mobility.We present a geometry-aware, layered SAGIN architecture, where Low-Earth-Orbit (LEO) provides macro-scale coverage and coordination, High-Altitude Platform Stations (HAPS) enables regional continuity and backhaul support, and MA is incorporated into the layered design to enable fine-grained geometry adaptation, particularly at unmanned aerial vehicles (UAVs) and terrestrial layers where local channel dynamics are most pronounced. We further discuss how such geometry control enhances robustness, supports multi-functional operation spanning communication, sensing, control, and navigation, and enables more flexible spatial cooperation across layers.

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arXiv2026

Toward Environment-Aware LAE: SAR as a Shared Sensing Infrastructure

The rapid growth of the low-altitude economy (LAE) is making aerial systems an important part of future digital infrastructure. Although major advances have been achieved in unmanned aerial vehicle (UAV) platforms, communications, and autonomous control, environmental perception remains a key bottleneck to reliable and scalable LAE operations. Existing sensing modalities, such as optical, LiDAR, and millimeter-wave radar, are limited by visibility, sensing range, and environmental conditions, resulting in fragmented situational awareness. This article argues that addressing these limitations requires a shift from platform-centric sensing to a shared, environment-aware sensing infrastructure. In this context, synthetic aperture radar (SAR) offers a distinct advantage by enabling all-weather, wide-area perception. We show that SAR can support UAV operations through global environmental awareness, enhance task-level sensing, and enable cooperative sensing across satellites, high-altitude platforms, UAVs, and ground systems. Building on this perspective, we present a system-level view of SAR-enabled LAE, highlighting key transformations from fragmented to infrastructure-centric sensing, from reactive to predictive operation, and from device-centric to environment-aware networking. We further discuss enabling architectures, including multi-platform sensing hierarchies, integration with integrated sensing and communication (ISAC), and the role of artificial intelligence and digital twins, along with the key challenges toward real-world deployment. By positioning SAR as a shared sensing foundation rather than a standalone modality, this article provides new insights into the design of scalable, reliable, and intelligent LAE systems.

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arXiv2026

Movable-Antenna Index Modulation (MA-IM): System Framework and Performance Analysis

This paper proposes a movable-antenna-based index modulation (MA-IM) framework that exploits the spatial mobility of a single reconfigurable antenna to create additional information-bearing dimensions for next-generation wireless systems. By discretizing the continuous movable region into a dense set of candidate sampling points and selecting representative anchors for indexing, the proposed framework converts spatial degrees of freedom into a practical modulation resource. Building on this framework, we develop a family of anchor-selection strategies with different levels of channel awareness, including geometry-based, SNR-based, max--min channel-domain, and joint constellation-aware designs. For the resulting MA-IM schemes, joint maximum-likelihood (ML) detectors are derived, along with a low-complexity two-stage detector, and unified analytical upper bounds on the average bit error probability (ABEP) are established based on the joint index--modulation constellation. The results reveal that directly indexing all sampling points is generally unreliable, highlighting the necessity of anchor optimization. The performance of MA-IM is shown to depend on key system parameters, including channel richness, spatial correlation, the number of index states, and the modulation order. In particular, increasing the number of index states and increasing the QAM order affect MA-IM in fundamentally different ways, even under the same transmission rate. Among the proposed schemes, the joint constellation-aware anchor design achieves the best error performance, demonstrating that optimizing channel-domain separation alone is insufficient and that effective MA-IM design must account for the geometry of the joint signal constellation. Simulation results further show that, with properly designed anchors, MA-IM can approach or even outperform same-spectral-efficiency QAM baselines.

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arXiv2026

Innovation Discovery System for Networking Research

As networking systems become increasingly complex, achieving disruptive innovation grows more challenging. At the same time, recent progress in Large Language Models (LLMs) has shown strong potential for scientific hypothesis formation and idea generation. Nevertheless, applying LLMs effectively to networking research remains difficult for two main reasons: standalone LLMs tend to generate ideas by recombining existing solutions, and current open-source networking resources do not provide the structured, idea-level knowledge necessary for data-driven scientific discovery. To bridge this gap, we present SciNet, a research idea generation system specifically designed for networking. SciNet is built upon three key components: (1) constructing a networking-oriented scientific discovery dataset from top-tier networking conferences, (2) simulating the human idea discovery workflow through problem setting, inspiration retrieval, and idea generation, and (3) developing an idea evaluation method that jointly measures novelty and practicality. Experimental results show that \system consistently produces practical and novel networking research ideas across multiple LLM backbones, and outperforms standalone LLM-based generation in overall idea quality.

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arXiv2026

High-Altitude Platforms in the Low-Altitude Economy: Bridging Communication, Computing, and Regulation

The Low-Altitude Economy (LAE) is rapidly emerging as a new technological and industrial frontier, with unmanned aerial vehicles (UAVs), electric vertical takeoff and landing (eVTOL) aircraft, and aerial swarms increasingly deployed in logistics, infrastructure inspection, security, and emergency response. However, the large-scale development of the LAE demands a reliable aerial foundation that ensures not only real-time connectivity and computational support, but also navigation integrity and safe airspace management for safety-critical operations. High-Altitude Platforms (HAPs), positioned at around 20 km, provide a unique balance between wide-area coverage and low-latency responsiveness. Compared with low earth orbit (LEO) satellites, HAPs are closer to end users and thus capable of delivering millisecond-level connectivity, fine-grained regulatory oversight, and powerful onboard computing and caching resources. Beyond connectivity and computation, HAPs-assisted sensing and regulation further enable navigation integrity and airspace trust, which are essential for safety-critical UAV and eVTOL operations in the LAE. This article proposes a five-stage evolutionary roadmap for HAPs in the LAE: from serving as aerial infrastructure bases, to becoming super back-ends for UAV, to acting as frontline support for ground users, further enabling swarm-scale UAV coordination, and ultimately advancing toward edge-air-cloud closed-loop autonomy. In parallel, HAPs complement LEO satellites and cloud infrastructures to form a global-regional-local three-tier architecture. Looking forward, HAPs are expected to evolve from simple platforms into intelligent hubs, emerging as pivotal nodes for air traffic management, intelligent logistics, and emergency response. By doing so, they will accelerate the transition of the LAE toward large-scale deployment, autonomy, and sustainable growth.

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arXiv2025

Joint Trajectory and Resource Optimization for HAPs-SAR Systems with Energy-Aware Constraints

This paper investigates the joint optimization of trajectory planning and resource allocation for a high-altitude platform stations synthetic aperture radar (HAPs-SAR) system. To support real-time sensing and conserve the limited energy budget of the HAPs, the proposed framework assumes that the acquired radar data are transmitted in real time to a ground base station for SAR image reconstruction. A dynamic trajectory model is developed, and the power consumption associated with radar sensing, data transmission, and circular flight is comprehensively analyzed. In addition, solar energy harvesting is considered to enhance system sustainability. An energy-aware mixed-integer nonlinear programming (MINLP) problem is formulated to maximize radar beam coverage while satisfying operational constraints. To solve this challenging problem, a sub-optimal successive convex approximation (SCA)-based framework is proposed, incorporating iterative optimization and finite search. Simulation results validate the convergence of the proposed algorithm and demonstrate its effectiveness in balancing SAR performance, communication reliability, and energy efficiency. A final SAR imaging simulation on a 9-target lattice scenario further confirms the practical feasibility of the proposed solution.

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arXiv2025

Design of 3D Beamforming and Deployment Strategies for ISAC-based HAPS Systems

This paper explores high-altitude platform station (HAPS) systems enabled by integrated sensing and communication (ISAC), in which a HAPS simultaneously transmits communication signals and synthetic aperture radar (SAR) imaging signals to support multi-user communication while performing ground target sensing. Taking into account the operational characteristics of SAR imaging, we consider two HAPS deployment strategies: (i) a quasi-stationary HAPS that remains fixed at an optimized location during SAR operation, following the stop-and-go scanning model; and (ii) a dynamic HAPS that continuously adjusts its flight trajectory along a circular path. For each strategy, we aim at maximizing the weighted sum-rate throughput for communication users while ensuring that SAR imaging requirements, such as beampattern gain and signal-to-noise ratio (SNR), are satisfied. This is achieved by jointly optimizing the HAPS deployment strategy, i.e., its placement or trajectory, along with three-dimensional (3D) transmit beamforming, under practical constraints including transmit power limits, energy consumption, and flight dynamics. Nevertheless, the formulated optimization problems corresponding to the two deployment strategies are inherently non-convex. To address the issue, we propose efficient algorithms that leverage both convex and non-convex optimization techniques to obtain high-quality suboptimal solutions. Numerical results demonstrate the effectiveness and advantages of the proposed approaches over benchmark schemes.

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arXiv2024

Design of Frequency Index Modulated Waveforms for Integrated SAR and Communication on High-Altitude Platforms (HAPs)

This paper, addressing the integration requirements of radar imaging and communication for High-Altitude Platform Stations (HAPs) platforms, designs a waveform based on linear frequency modulated (LFM) frequency-hopping signals that combines synthetic aperture radar (SAR) and communication functionalities. Specifically, each pulse of an LFM signal is segmented into multiple parts, forming a sequence of sub-pulses. Each sub-pulse can adopt a different carrier frequency, leading to frequency hops between sub-pulses. This design is termed frequency index modulation (FIM), enabling the embedding of communication information into different carrier frequencies for transmission. To further enhance the data transmission rate at the communication end, this paper incorporates quadrature amplitude modulation (QAM) into waveform design. %For the SAR portion, this approach reduces the ADC sampling requirements while maintaining range resolution. The paper derives the ambiguity function of the proposed waveform and analyzes its Doppler and range resolution, establishing upper and lower bounds for the range resolution. In processing SAR signals, the receiver first removes QAM symbols, and to address phase discontinuities between sub-pulses, a phase compensation algorithm is proposed to achieve coherent processing. For the communication receiver, the user first performs de-chirp processing and then demodulates QAM symbols and FIM index symbols using a two-step maximum likelihood (ML) algorithm. Numerical simulations further confirm the theoretical validity of the proposed approach.

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arXiv2024

FDA-MIMO-Based Integrated Multi-Target Sensing and Communication System with Complex Coefficients Information Embedding

The echo signals of frequency diverse array multiple-input multiple-output (FDA-MIMO) feature angle-range coupling, enabling simultaneous discrimination and estimation of multiple targets at different locations. In light of this, based on FDA-MIMO, this paper explores an sensing-centric integrated sensing and communication (ISAC) system for multi-target sensing. At the base station, we propose the FDA-MIMO-based spatial spectrum multi-target estimation (SSMTE) method, which first jointly estimates the angle and distance of targets and then estimates the velocities. To reduce the sensing computational complexity, the low-complexity spatial spectrum estimation (LCSSE) algorithm is proposed. LCSSE reduces the complexity without degrading the sensing performance by converting the joint angle-range search into two one-dimensional searches. To address the range ambiguity caused by frequency offset, a frequency offset design criterion (FODC) is proposed. It designs the integer and fractional components of the frequency offset to ensure the ambiguity distance exceeds the maximum sensing range, thereby alleviating parameters pairing errors. Moreover, the complex coefficients information embedding (CCIE) scheme is designed to improve system communication rates, which carries extra bits by selecting complex coefficients from the coefficient vector. The closed-form expressions for the bit error rate (BER) tight upper bound and the Cram\'er-Rao bound (CRB) are derived. Simulation results show that the proposed system excels in multi-target sensing and communications.

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arXiv2024

Generalized code index modulation-aided frequency offset realign multiple-antenna spatial modulation approach for next-generation green communication systems

For next-generation green communication systems, this article proposes an innovative communication system based on frequency-diverse array-multiple-input multiple-output (FDA-MIMO) technology, which aims to achieve high data rates while maintaining low power consumption. This system utilizes frequency offset index realign modulation, multiple-antenna spatial index modulation, and spreading code index modulation techniques. In the proposed generalized code index modulation-aided frequency offset realign multiple-antenna spatial modulation (GCIM-FORMASM) system, the coming bits are divided into five parts: spatial modulation bits by activating multiple transmit antennas, frequency offset index bits of the FDA antennas, including frequency offset combination bits and frequency offset realign bits, spreading code index modulation bits, and modulated symbol bits. Subsequently, this paper utilizes the orthogonal waveforms transmitted by the FDA to design the corresponding transmitter and receiver structures and provide specific expressions for the received signals. Meanwhile, to reduce the decoding complexity of the maximum likelihood (ML) algorithm, we propose a three-stage despreading-based low complexity (DBLC) algorithm leveraging the orthogonality of the spreading codes. Additionally, a closed-form expression for the upper bound of the average bit error probability (ABEP) of the DBLC algorithm has been derived. Analyzing metrics such as energy efficiency and data rate shows that the proposed system features low power consumption and high data transmission rates, which aligns better with the concept of future green communications. The effectiveness of our proposed methods has been validated through comprehensive numerical results.

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arXiv2024

Adaptive Target Detection for FDA-MIMO Radar with Training Data in Gaussian noise

This paper addresses the problem of detecting a moving target embedded in Gaussian noise with an unknown covariance matrix for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. To end it, assume that obtaining a set of training data is available. Moreover, we propose three adaptive detectors in accordance with the one-step generalized likelihood ratio test (GLRT), two-step GLRT, and Rao criteria, namely OGLRT, TGLRT, and Rao. The LH adaptive matched filter (LHAMF) detector is also introduced when decomposing the Rao test. Next, all provided detectors have constant false alarm rate (CFAR) properties against the covariance matrix. Besides, the closed-form expressions for false alarm probability (PFA) and detection probability (PD) are derived. Finally, this paper substantiates the correctness of the aforementioned algorithms through numerical simulations.

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arXiv2023

FDA-MIMO-based Integrated Sensing and Communication System with Frequency Offset Permutation Index Modulation

Considering that frequency diverse array multiple-input multiple-output (FDA-MIMO) possesses extra range information to enhance sensing performance, this paper explores the FDA-MIMO-based integrated sensing and communication (ISAC) system. To reinforce the system communication capability, we propose the frequency offset permutation index modulation (FOPIM) scheme, which conveys extra information bits by selecting and permutating frequency offsets from a frequency offsets pool. For the system communication sub-functionality, considering the fact that the traditional maximum likelihood detection method suffers from high complexity and bit error rate (BER), the maximum likelihood-based two-stage detection (MLTSD) approach is presented to overcome this issue. For the system sensing sub-function, we employ the two-step maximum likelihood estimator (TSMLE) to stepwise estimate the angle and range of the interested target. Furthermore, we derive the closed-form expressions for the tight upper bound on the communication BER, along with the sensing Cram\'er-Rao bound (CRB). The simulation results validate the theoretical analysis, demonstrating that the proposed system exhibits lower BER and superior range resolution than independent MIMO communication and MIMO sensing modules.

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arXiv2023

Toward Reproducing Network Research Results Using Large Language Models

Reproducing research results in the networking community is important for both academia and industry. The current best practice typically resorts to three approaches: (1) looking for publicly available prototypes; (2) contacting the authors to get a private prototype; and (3) manually implementing a prototype following the description of the publication. However, most published network research does not have public prototypes and private prototypes are hard to get. As such, most reproducing efforts are spent on manual implementation based on the publications, which is both time and labor consuming and error-prone. In this paper, we boldly propose reproducing network research results using the emerging large language models (LLMs). In particular, we first prove its feasibility with a small-scale experiment, in which four students with essential networking knowledge each reproduces a different networking system published in prominent conferences and journals by prompt engineering ChatGPT. We report the experiment's observations and lessons and discuss future open research questions of this proposal. This work raises no ethical issue.

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