arXiv subjects

Yunmei Shi

Publications and source records attributed to Yunmei Shi.

4 recordsLinked to original sources

arXiv2026

A Cancellation Mechanism in AFDM Radar Sensing: Exact Fisher Information and Delay-Doppler Decoupling

We consider radar sensing with affine frequency division multiplexing (AFDM), a chirp-based waveform recently proposed for high-mobility integrated sensing and communication. While numerical Cramér-Rao bounds for AFDM radar are available in the literature, no closed-form Fisher information analysis has so far revealed how the waveform's chirp structure shapes delay-Doppler estimation accuracy.In this paper, we provide such an analysis. We identify a cancellation in the AFDM likelihood: the frequency drift introduced by the chirp modulation is exactly compensated by a discrete phase correction built into the chirp-periodic prefix, leaving only a small residual. Exploiting this cancellation, we derive an exact closed-form Fisher information matrix that depends on the AFDM chirp structure through a single scalar, and from it we obtain closed-form Cramér-Rao bounds for joint delay and Doppler estimation.Three consequences follow. AFDM is provably less delay-Doppler-coupled than OFDM for any nonzero chirp rate. The delay Cramér-Rao bound improves quadratically with the chirp rate, while the Doppler bound is unaffected by it. Finally, our framework reduces continuously to the classical OFDM result as the chirp vanishes, certifying it as a strict generalization of OFDM radar sensing theory.Overall, our work shows that the chirp-periodic prefix -- until now studied only as a channel-equalization device -- is the structural element that decouples delay and Doppler in AFDM sensing, and that AFDM's superior sensing performance can be characterized analytically rather than through numerical bounds alone. Numerical experiments at realistic vehicular and low-Earth-orbit parameters validate all closed-form expressions.

eess.SP↗

arXiv2026

DRL-Enabled Trajectory Planing for UAV-Assisted VLC: Optimal Altitude and Reward Design

Recently, the integration of unmanned aerial vehicle (UAV) and visible light communication (VLC) technologies has emerged as a promising solution to offer flexible communication and efficient lighting. This letter investigates the three-dimensional trajectory planning in a UAV-assisted VLC system, where a UAV is dispatched to collect data from ground users (GUs). The core objective is to develop a trajectory planning framework that minimizes UAV flight distance, which is equivalent to maximizing the data collection efficiency. This issue is formulated as a challenging mixed-integer non-convex optimization problem. To tackle it, we first derive a closed-form optimal flight altitude under specific VLC channel gain threshold. Subsequently, we optimize the UAV horizontal trajectory by integrating a novel pheromone-driven reward mechanism with the twin delayed deep deterministic policy gradient algorithm, which enables adaptive UAV motion strategy in complex environments. Simulation results validate that the derived optimal altitude effectively reduces the flight distance by up to 35% compared to baseline methods. Additionally, the proposed reward mechanism significantly shortens the convergence steps by approximately 50%, demonstrating notable efficiency gains in the context of UAV-assisted VLC data collection.

cs.LG↗

arXiv2024

UAV-Mounted Movable Antenna: Joint Optimization of UAV Placement and Antenna Configuration

Recently, movable antennas (MAs) have garnered immense attention due to their capability to favorably alter channel conditions through agile movement. In this letter, we delve into a spectrum sharing system enabled by unmanned aerial vehicle (UAV) mounted MAs, thereby introducing a new degree of freedom vertically alongside the horizontal local mobility for MAs. Our objective is to maximize the minimum beamforming gain for secondary users (SUs) while ensuring that interference to the primary users (PUs) remains below a predefined threshold, which necessitates a joint optimization involving the UAV's height, the antenna weight vector (AWV), and the antenna position vector (APV). However, the formulated optimization problem is non-convex and challenging to solve optimally. To tackle this issue, we propose an alternating optimization algorithm that optimizes the UAV's height, APV and AWV in an iterative manner, thus yielding a near-optimal solution. Numerical results demonstrate the superiority of the proposed scheme as well as its ability to deliver full beamforming gain to SUs with reduced computational complexity.

math.NA↗

arXiv2019

A Block Alternating Optimization Method for Direction-of-Arrival Estimation with Nested Array

In this paper, direction-of-arrival estimation using nested array is studied in the framework of sparse signal representation. With the vectorization operator, a new real-valued nonnegative sparse signal recovery model which has a wider virtual array aperture is built. To leverage celebrated compressive sensing algorithms, the continuous parameter space has to be discretized to a number of fixed grid points, which inevitably incurs modeling error caused by off-grid gap. To remedy this issue, a block alternating optimization method is put forth that jointly estimates the sparse signal and refines the locations of grid points. Specifically, inspired by the majorization minimization, the proposed method iteratively minimizes a surrogate function majorizing the given objective function, where only a single block of variables are updated per iteration while the remaining ones are kept fixed. The proposed method features affordable computational complexity, and numerical tests corroborate its superior performance relative to existing alternatives in both overdetermined and underdetermined scenarios.

eess.SP↗