GPU-Accelerated Parallel Bilevel Optimization for Roubst 6G ISAC
Authors: Xingdi Chen, Kai Yang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments have been conducted to evaluate the performances of proposed methods. In particular, the proposed GPU-accelerated parallel bilevel optimization can accelerate the convergence speed by up to 50 times compared to conventional gradient-based methods. |
| Researcher Affiliation | Academia | Xingdi Chen, Kai Yang* School of Computer Science and Technology, Tongji University, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: BOBLRBF: Bi-Objective Bi Level optimization based Robust Beam Forming. |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is provided, nor does it include a link to a code repository. |
| Open Datasets | No | In the simulation, we consider the networked ISAC scenario with L = 3 BSs. BSs are deployed with uniform linear arrays (ULAs) with half-wavelength spacing between consecutive antennas, and each BS serves one communication user. The number of antennas at each BS is N = 5. BSs are located at ( 40m, 40 3m), (80m, 0m) and ( 40m, 40 3m), respectively, while communication users are randomly distributed around BSs. There are J = 2 targets located at (2m, 5m) and ( 5m, 1m). The communication channels between BSs and communication users are set as Rayleigh fading following the standard assumption, i.e., each channel coefficient hl,c,k is generated according to a complex standard normal distribution, with zero mean and unit variance. |
| Dataset Splits | No | The paper describes a simulated environment and does not mention any dataset splits (e.g., training, validation, test) for reproducibility. Data is generated according to specific parameters. |
| Hardware Specification | Yes | The algorithm BOBLRBF is executed on a machine equipped with 12th Gen Intel(R) Core(TM) i7-12700H and the algorithm BOBLRBF-DNN runs on NVIDIA Ge Force RTX 3060. |
| Software Dependencies | No | The paper mentions that W y, y = 1, . . . , Y 1 were set as multilayer perceptrons (MLPs), but does not provide specific software names or version numbers for libraries, frameworks, or programming languages used (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | In the simulation, we consider the networked ISAC scenario with L = 3 BSs. ... The number of antennas at each BS is N = 5. BSs are located at ( 40m, 40 3m), (80m, 0m) and ( 40m, 40 3m), respectively... There are J = 2 targets located at (2m, 5m) and ( 5m, 1m)... The power budgets {Pl} of all BSs are set to be 40 dBm... In experiments, we set W y, y = 1, . . . , Y 1 as multilayer perceptrons (MLPs). |