Search Results for author:
Found 13 papers, 1 papers with code
Mitigating Sensitive Information Leakage in LLMs4Code through Machine Unlearning
Addressing this issue is crucial for ensuring privacy compliance and upholding user trust, but till now there is a dearth of dedicated studies in the literature that focus on this specific direction.
Principle Driven Parameterized Fiber Model based on GPT-PINN Neural Network
Therefore, the model can greatly alleviate the heavy burden of re-training since only the linear combination coefficients need to be found when changing the transmission condition.
Fiber Transmission Model with Parameterized Inputs based on GPT-PINN Neural Network
By taking into the account of the previously proposed principle driven fiber model, the reduced basis expansion method and transforming the parameterized inputs into parameterized coefficients of the Nonlinear Schrodinger Equations, universal solutions with respect to inputs corresponding to different bit rates can all be obtained without the need of re-training the whole model.
A Reconfigurable Subarray Architecture and Hybrid Beamforming for Millimeter-Wave Dual-Function-Radar-Communication Systems
Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks.
Sparse Bayesian Learning-Based 3D Spectrum Environment Map Construction-Sampling Optimization, Scenario-Dependent Dictionary Construction and Sparse Recovery
Then, a maximum and minimum distance (MMD) clustering-based SBL algorithm is proposed to recover the spectrum data at the unsampled positions and construct the whole 3D SEM.
Domain Specified Optimization for Deployment Authorization
As a complementary of SDPA, we also propose Target-Combined Deployment Authorization (TPDA), where unauthorized domains are partially accessible, and simplify the DSO method to a perturbation operation on the pseudo predictions, referred to as Target-Dependent Domain-Specified Optimization (TDSO).
Geometry-Based Stochastic Probability Models for the LoS and NLoS Paths of A2G Channels under Urban Scenario
Path probability prediction is essential to describe the dynamic birth and death of propagation paths, and build the accurate channel model for air-to-ground (A2G) communications.
Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language Models
In this work, for the first time, we propose to investigate this problem where only a small number of labeled training samples are available.
Wide-Beam Array Antenna Power Gain Maximization via ADMM Framework
This paper proposes two algorithms to maximize the minimum array power gain in a wide-beam mainlobe by solving the power gain pattern synthesis (PGPS) problem with and without sidelobe constraints.
Tensor-based Multi-dimensional Wideband Channel Estimation for mmWave Hybrid Cylindrical Arrays
Channel estimation is challenging for hybrid millimeter wave (mmWave) large-scale antenna arrays which are promising in 5G/B5G applications.
Signal Processing
Complex ResNet Aided DoA Estimation for Near-Field MIMO Systems
By converting it into a regression task, a novel DoA estimation framework based on complex-valued deep learning (CVDL) is proposed for the near-field region in short-range MIMO communication systems.
Signal Processing
Nested Hybrid Cylindrical Array Design and DoA Estimation for Massive IoT Networks
As a result, only a small number of RF chains are required to preserve the DoF of the UCyA.
Delay-Constrained Joint Power Control, User Detection and Passive Beamforming in Intelligent Reflecting Surface Assisted Uplink mmWave System
Millimeter-wave (mmWave) communications provide access to spectra with bandwidths and in abundance.
