Robust Beamforming for Downlink Multi-Cell Systems: A Bilevel Optimization Perspective
Authors: Xingdi Chen, Yu Xiong, Kai Yang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties. ... In this section, numerical simulations are carried out to illustrate the performance of BLRBF and BLADRBF algorithms. We consider multi-cell multi-user MISO downlink systems. |
| Researcher Affiliation | Academia | Xingdi Chen1, Yu Xiong1, Kai Yang1,2,3* 1Department of Computer Science and Technology, Tongji University, China 2Key Laboratory of Embedded System and Service Computing Ministry of Education at Tongji University 3Shanghai Research Institute for Intelligent Autonomous Systems |
| Pseudocode | Yes | Algorithm 1: BLRBF: Bi Level based Robust Beam Forming. Algorithm 2: BLADRBF: Bi Level based Asynchronous Distributed Robust Beam Forming. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing the source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper states "We adopt a typical small-scale fading channel model, i.e., Rayleigh fading, which is widely used in previous literature (Choi et al. 2012; Zhang et al. 2022). Rayleigh fading: Each channel coefficient hkmn is generated according to a complex standard normal distribution, i.e., Re(hkmn) CN(0, I) 2 , Im(hkmn) CN(0, I) 2 , m, n, k." This describes a synthetic data generation process rather than the use of an existing publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper conducts numerical simulations and describes system configurations (e.g., M=N=K=2) and channel realizations but does not specify explicit training, validation, or test dataset splits in terms of percentages or counts, or refer to standard splits from well-known datasets. |
| Hardware Specification | Yes | All simulation experiments are executed on a machine equipped with a 16-core AMD Ryzen 7 5800H processor. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, specific libraries, or simulation tools). |
| Experiment Setup | No | The paper describes system parameters (M, K, N, P, SNR range) and the channel model (Rayleigh fading with normal distribution parameters). However, it does not explicitly provide hyperparameter values for the proposed BLRBF and BLADRBF algorithms (e.g., step-sizes η, η{skmn}, η{µkmn}, ηV, η, ηλi, or the frequency of cutting plane updates kpre) nor detailed training configurations that would be needed for reproduction. |