English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As a fundamental problem, numerous methods are dedicated to the optimizationof signal-to-interference-plus-noise ratio (SINR), in a multi-user setting.Although traditional model-based optimization methods achieve strongperformance, the high complexity raises the research of neural network (NN)based approaches to trade-off the performance and complexity. To fully leveragethe high performance of traditional model-based methods and the low complexityof the NN-based method, a knowledge distillation (KD) based algorithmdistillation (AD) method is proposed in this paper to improve the performanceand convergence speed of the NN-based method, where traditional SINRoptimization methods are employed as ``teachers" to assist the training of NNs,which are ``students", thus enhancing the performance of unsupervised andreinforcement learning techniques. This approach aims to alleviate commonissues encountered in each of these training paradigms, including theinfeasibility of obtaining optimal solutions as labels and overfitting insupervised learning, ensuring higher convergence performance in unsupervisedlearning, and improving training efficiency in reinforcement learning.Simulation results demonstrate the enhanced performance of the proposedAD-based methods compared to traditional learning methods. Remarkably, thisresearch paves the way for the integration of traditional optimization insightsand emerging NN techniques in wireless communication system optimization.

Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach