arXiv subjects

Haichao Wang

Publications and source records attributed to Haichao Wang.

At least 19 recordsLinked to original sources

arXiv2026

Integrated Sensing and Covert Communication In Low-Altitude Networks: A Smart Radio Environment Perspective

The rise of low-altitude economies and 6G is driving the evolution of low-altitude networks (LANs), making communication security a pressing concern. Unlike traditional security approaches, covert communication offers enhanced protection by hiding the transmission behavior itself. Integrated sensing and communication (ISAC), a key technology of 6G, efficiently supports both sensing and communication tasks through hardware integration, thereby promising significant gains for covert communication. Nevertheless, the complexity and dynamics of urban environments pose critical challenges. Drawing on the latest advances in smart radio environment (SRE) technologies, this paper introduces them into integrated sensing and covert communication (ISACC) to suppress covert channel fading and counteract sensing precision loss in LANs. We first survey the applications and state-of-the-art findings of ISACC in LANs, highlighting key practical challenges. Subsequently, we introduce the core concept of SRE and elaborate on its enabling techniques across four dimensions. To deliver more insights, we explore potential pathways for integrating SRE into ISACC. To maximize covert throughput, a reinforcement learning-based case study is conducted by jointly optimizing flight trajectory, jamming power, movable antenna position, bandwidth allocation, and beamforming vectors. Simulation results show that the proposed scheme achieves superior performance compared to the benchmark. Finally, some open challenges and potential directions are discussed.

eess.SP↗

arXiv2026

Diffusion Path Alignment for Long-Range Motion Generation and Domain Transitions

Long-range human movement generation remains a central challenge in computer vision and graphics. Generating coherent transitions across semantically distinct motion domains remains largely unexplored. This capability is particularly important for applications such as dance choreography, where movements must fluidly transition across diverse stylistic and semantic motifs. We propose a simple and effective inference-time optimization framework inspired by diffusion-based stochastic optimal control. Specifically, a control-energy objective that explicitly regularizes the transition trajectories of a pretrained diffusion model. We show that optimizing this objective at inference time yields transitions with fidelity and temporal coherence. This is the first work to provide a general framework for controlled long-range human motion generation with explicit transition modeling.

cs.CV↗

arXiv2025

Efficient Bayer-Domain Video Computer Vision with Fast Motion Estimation and Learned Perception Residual

Video computer vision systems face substantial computational burdens arising from two fundamental challenges: eliminating unnecessary processing and reducing temporal redundancy in back-end inference while maintaining accuracy with minimal extra computation. To address these issues, we propose an efficient video computer vision framework that jointly optimizes both the front end and back end of the pipeline. On the front end, we remove the traditional image signal processor (ISP) and feed Bayer raw measurements directly into Bayer-domain vision models, avoiding costly human-oriented ISP operations. On the back end, we introduce a fast and highly parallel motion estimation algorithm that extracts inter-frame temporal correspondence to avoid redundant computation. To mitigate artifacts caused by motion inaccuracies, we further employ lightweight perception residual networks that directly learn perception-level residuals and refine the propagated features. Experiments across multiple models and tasks demonstrate that our system achieves substantial acceleration with only minor performance degradation.

cs.CV↗

arXiv2025

Leveraging Motion Estimation for Efficient Bayer-Domain Computer Vision

Existing computer vision processing pipeline acquires visual information using an image sensor that captures pixel information in the Bayer pattern. The raw sensor data are then processed using an image signal processor (ISP) that first converts Bayer pixel data to RGB on a pixel by pixel basis, followed by video convolutional network (VCN) processing on a frame by frame basis. Both ISP and VCN are computationally expensive with high power consumption and latency. In this paper, we propose a novel framework that eliminates the ISP and leverages motion estimation to accelerate video vision tasks directly in the Bayer domain. We introduce Motion Estimation-based Video Convolution (MEVC), which integrates sliding-window motion estimation into each convolutional layer, enabling prediction and residual-based refinement that reduces redundant computations across frames. This design bridges the structural gap between block-based motion estimation and spatial convolution, enabling accurate, low-cost processing. Our end-to-end pipeline supports raw Bayer input and achieves over 70\% reduction in FLOPs with minimal accuracy degradation across video semantic segmentation, depth estimation, and object detection benchmarks, using both synthetic Bayer-converted and real Bayer video datasets. This framework generalizes across convolution-based models and marks the first effective reuse of motion estimation for accelerating video computer vision directly from raw sensor data.

cs.CV↗

arXiv2018

D2D Communications Underlaying Wireless Powered Communication Networks

In this paper, we investigate the resource allocation problem for D2D communications underlaying wireless powered communication networks, where multiple D2D pairs harvest energy from a power station equipped with multiple antennas and then transmit information signals simultaneously over the same spectrum resource. The aim is to maximize the sum throughput via joint time scheduling and power control, while satisfying the energy causality constraints. The formulated non-convex problem is first transformed into a nonlinear fractional programming problem with a tactful reformulation. Then, by leveraging D.C. (difference of two convex functions) programming, a suboptimal solution to the non-convex problem is obtained by iteratively solving a sequence of convex problems. Simulation results demonstrate that the proposed scheme works well in different scenarios and can significantly improve the system throughput compared with the-state-of-the-art schemes.

cs.IT↗

arXiv2018

Network-Connected UAV Communications: Potentials and Challenges

This article explores the use of network-connected unmanned aerial vehicle (UAV) communications as a compelling solution to achieve high-rate information transmission and support ultra-reliable UAV remote command and control. We first discuss the use cases of UAVs and the resulting communication requirements, accompanied with a flexible architecture for network-connected UAV communications. Then, the signal transmission and interference characteristics are theoretically analyzed, and subsequently we highlight the design and optimization considerations, including antenna design, non-orthogonal multiple access communications, as well as network selection and association optimization. Finally, case studies are provided to show the feasibility of network-connected UAV communications.

cs.NI↗

arXiv2018

Analysis of Fourier ptychographic microscopy with half of the captured images

Fourier ptychography microscopy (FPM) is a new computational imaging technique that can provide gigapixel images with both high resolution and a wide field of view (FOV). However, time consuming of the data-acquisition process is a critical issue. In this paper, we make an analysis on the FPM imaging system with half number of the captured images. Based on the image analysis of the conventional FPM system, we then compare the reconstructed images with different number of captured data. Simulation and experiment results show that the reconstructed image with half number captured data do not show obvious resolution degradation compared to that with all the captured data, except a contrast reduction. In particular in the case when the object is close to phase-only/amplitude only, the quality of the reconstructed image with half of the captured data is nearly as good as the one reconstructed with full data.

eess.IV↗

arXiv2018

Unmanned Aerial Vehicle-Aided Communications: Joint Transmit Power and Trajectory Optimization

This letter investigates the transmit power and trajectory optimization problem for unmanned aerial vehicle (UAV)-aided networks. Different from majority of the existing studies with fixed communication infrastructure, a dynamic scenario is considered where a flying UAV provides wireless services for multiple ground nodes simultaneously. To fully exploit the controllable channel variations provided by the UAV's mobility, the UAV's transmit power and trajectory are jointly optimized to maximize the minimum average throughput within a given time length. For the formulated non-convex optimization with power budget and trajectory constraints, this letter presents an efficient joint transmit power and trajectory optimization algorithm. Simulation results validate the effectiveness of the proposed algorithm and reveal that the optimized transmit power shows a water-filling characteristic in spatial domain.

eess.SP↗

arXiv2017

Power Control in UAV-Supported Ultra Dense Networks: Communications, Caching, and Energy Transfer

By means of network densification, ultra dense networks (UDNs) can efficiently broaden the network coverage and enhance the system throughput. In parallel, unmanned aerial vehicles (UAVs) communications and networking have attracted increasing attention recently due to their high agility and numerous applications. In this article, we present a vision of UAV-supported UDNs. Firstly, we present four representative scenarios to show the broad applications of UAV-supported UDNs in communications, caching and energy transfer. Then, we highlight the efficient power control in UAV-supported UDNs by discussing the main design considerations and methods in a comprehensive manner. Furthermore, we demonstrate the performance superiority of UAV-supported UDNs via case study simulations, compared to traditional fixed infrastructure based networks. In addition, we discuss the dominating technical challenges and open issues ahead.

cs.NI↗

arXiv2017

Fast and High Quality Light Field Acquisition using Defocus Modulation

Light field reconstruction from images captured by focal plane sweeping, such as light field moment imaging (LFMI) and light field reconstruction with back projection (LFBP), can achieve high lateral resolution comparable to the modern camera sensor. This is impossible for the conventional lens array based light field capture systems. However, capturing a series of focal sweeping images along the optical axis is time consuming and requires fine alignment, and different focal plane based light field reconstruction techniques require images with different characteristics. To solve this problem, we present an efficient approach for fast light field acquisition with precise focal plane sweeping capture by defocus modulation rather than axial movement. Because of the controllable point spread function, we can capture images for light field reconstruction with both LFMI and LFBP.

physics.optics↗

arXiv2016

5G Multimedia Massive MIMO Communications Systems

In the Fifth generation (5G) wireless communication systems, a majority of the traffic demands is contributed by various multimedia applications. To support the future 5G multimedia communication systems, the massive multiple-input multiple-output (MIMO) technique is recognized as a key enabler due to its high spectral efficiency. The massive antennas and radio frequency (RF) chains not only improve the implementation cost of 5G wireless communication systems but also result in an intense mutual coupling effect among antennas because of the limited space for deploying antennas. To reduce the cost, an optimal equivalent precoding matrix with the minimum number of RF chains is proposed for 5G multimedia massive MIMO communication systems considering the mutual coupling effect. Moreover, an upper bound of the effective capacity is derived for 5G multimedia massive MIMO communication systems. Two antenna receive diversity gain models are built and analyzed. The impacts of the antenna spacing, the number of antennas, the quality of service (QoS) statistical exponent, and the number of independent incident directions on the effective capacity of 5G multimedia massive MIMO communication systems are analyzed. Comparing with the conventional zero-forcing precoding matrix, simulation results demonstrate that the proposed optimal equivalent precoding matrix can achieve a higher achievable rate for 5G multimedia massive MIMO communication systems.

cs.IT↗

arXiv2016

Multi-user Massive MIMO Communication Systems Based on Irregular Antenna Arrays

In practical mobile communication engineering applications, surfaces of antenna array deployment regions are usually uneven. Therefore, massive multi-input-multi-output (MIMO) communication systems usually transmit wireless signals by irregular antenna arrays. To evaluate the performance of irregular antenna arrays, the matrix correlation coefficient and ergodic received gain are defined for massive MIMO communication systems with mutual coupling effects. Furthermore, the lower bound of the ergodic achievable rate, symbol error rate (SER) and average outage probability are firstly derived for multi-user massive MIMO communication systems using irregular antenna arrays. Asymptotic results are also derived when the number of antennas approaches infinity. Numerical results indicate that there exists a maximum achievable rate when the number of antennas keeps increasing in massive MIMO communication systems using irregular antenna arrays. Moreover, the irregular antenna array outperforms the regular antenna array in the achievable rate of massive MIMO communication systems when the number of antennas is larger than or equal to a given threshold.

cs.IT↗

arXiv2016

Energy Efficiency Optimization of 5G Radio Frequency Chain Systems

With the massive multi-input multi-output (MIMO) antennas technology adopted for the fifth generation (5G) wireless communication systems, a large number of radio frequency (RF) chains have to be employed for RF circuits. However, a large number of RF chains not only increase the cost of RF circuits but also consume additional energy in 5G wireless communication systems. In this paper we investigate energy and cost efficiency optimization solutions for 5G wireless communication systems with a large number of antennas and RF chains. An energy efficiency optimization problem is formulated for 5G wireless communication systems using massive MIMO antennas and millimeter wave technology. Considering the nonconcave feature of the objective function, a suboptimal iterative algorithm, i.e., the energy efficient hybrid precoding (EEHP) algorithm is developed for maximizing the energy efficiency of 5G wireless communication systems. To reduce the cost of RF circuits, the energy efficient hybrid precoding with the minimum number of RF chains (EEHP-MRFC) algorithm is also proposed. Moreover, the critical number of antennas searching (CNAS) and user equipment number optimization (UENO) algorithms are further developed to optimize the energy efficiency of 5G wireless communication systems by the number of transmit antennas and UEs. Compared with the maximum energy efficiency of conventional zero-forcing (ZF) precoding algorithm, numerical results indicate that the maximum energy efficiency of the proposed EEHP and EEHP-MRFC algorithms are improved by 220% and 171%, respectively.

cs.NI↗

arXiv2016

Phase Retrieval of Real-Valued Signals in a Shift-Invariant Space

Phase retrieval arises in various fields of science and engineering and it is well studied in a finite-dimensional setting. In this paper, we consider an infinite-dimensional phase retrieval problem to reconstruct real-valued signals living in a shift-invariant space from its phaseless samples taken either on the whole line or on a set with finite sampling rate. We find the equivalence between nonseparability of signals in a linear space and its phase retrievability with phaseless samples taken on the whole line. For a spline signal of order $N$, we show that it can be well approximated, up to a sign, from its noisy phaseless samples taken on a set with sampling rate $2N-1$. We propose an algorithm to reconstruct nonseparable signals in a shift-invariant space generated by a compactly supported continuous function. The proposed algorithm is robust against bounded sampling noise and it could be implemented in a distributed manner.

cs.IT↗

arXiv2014

3-Factor-criticality in double domination edge critical graphs

A vertex subset $S$ of a graph $G$ is a double dominating set of $G$ if $|N[v]\cap S|\geq 2$ for each vertex $v$ of $G$, where $N[v]$ is the set of the vertex $v$ and vertices adjacent to $v$. The double domination number of $G$, denoted by $γ_{\times 2}(G)$, is the cardinality of a smallest double dominating set of $G$. A graph $G$ is said to be double domination edge critical if $γ_{\times 2}(G+e)<γ_{\times 2}(G)$ for any edge $e \notin E$. A double domination edge critical graph $G$ with $γ_{\times 2}(G)=k$ is called $k$-$γ_{\times 2}(G)$-critical. A graph $G$ is $r$-factor-critical if $G-S$ has a perfect matching for each set $S$ of $r$ vertices in $G$. In this paper we show that $G$ is 3-factor-critical if $G$ is a 3-connected claw-free $4$-$γ_{\times 2}(G)$-critical graph of odd order with minimum degree at least 4 except a family of graphs.

math.CO↗

arXiv2013

A null space property approach to compressed sensing with frames

An interesting topic in compressive sensing concerns problems of sensing and recovering signals with sparse representations in a dictionary. In this note, we study conditions of sensing matrices A for the L1-synthesis method to accurately recover sparse, or nearly sparse signals in a given dictionary D. In particular, we propose a dictionary based null space property (D-NSP) which, to the best of our knowledge, is the first sufficient and necessary condition for the success of the L1 recovery. This new property is then utilized to detect some of those dictionaries whose sparse families cannot be compressed universally. Moreover, when the dictionary is full spark, we show that AD being NSP, which is well-known to be only sufficient for stable recovery via L1-synthesis method, is indeed necessary as well.

cs.IT↗

arXiv2013

A null space analysis of the L1 synthesis method in dictionary-based compressed sensing

An interesting topic in compressed sensing aims to recover signals with sparse representations in a dictionary. Recently the performance of the L1-analysis method has been a focus, while some fundamental problems for the L1-synthesis method are still unsolved. For example, what are the conditions for it to stably recover compressible signals under noise? Whether coherent dictionaries allow the existence of sensing matrices that guarantee good performances of the L1-synthesis method? To answer these questions, we build up a framework for the L1-synthesis method. In particular, we propose a dictionary-based null space property DNSP which, to the best of our knowledge, is the first sufficient and necessary condition for the success of L1-synthesis without measurement noise. With this new property, we show that when the dictionary D is full spark, it cannot be too coherent otherwise the method fails for all sensing matrices. We also prove that in the real case, DNSP is equivalent to the stability of L1-synthesis under noise.

cs.IT↗

arXiv2013

Accurate detection of moving targets via random sensor arrays and Kerdock codes

The detection and parameter estimation of moving targets is one of the most important tasks in radar. Arrays of randomly distributed antennas have been popular for this purpose for about half a century. Yet, surprisingly little rigorous mathematical theory exists for random arrays that addresses fundamental question such as how many targets can be recovered, at what resolution, at which noise level, and with which algorithm. In a different line of research in radar, mathematicians and engineers have invested significant effort into the design of radar transmission waveforms which satisfy various desirable properties. In this paper we bring these two seemingly unrelated areas together. Using tools from compressive sensing we derive a theoretical framework for the recovery of targets in the azimuth-range-Doppler domain via random antennas arrays. In one manifestation of our theory we use Kerdock codes as transmission waveforms and exploit some of their peculiar properties in our analysis. Our paper provides two main contributions: (i) We derive the first rigorous mathematical theory for the detection of moving targets using random sensor arrays. (ii) The transmitted waveforms satisfy a variety of properties that are very desirable and important from a practical viewpoint. Thus our approach does not just lead to useful theoretical insights, but is also of practical importance. Various extensions of our results are derived and numerical simulations confirming our theory are presented.

math.NA↗