arXiv subjects

Xuanyu Liang

Publications and source records attributed to Xuanyu Liang.

6 recordsLinked to original sources

arXiv2026

FFR: Forward-Forward Learning for Regression

The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks through purely local, layer-wise optimization. However, FF is inherently designed for classification via contrastive positive-negative sample pairs, and extending it to regression poses fundamental challenges: continuous target space lack natural "opposites" for contrastive learning, and the standard goodness function carries no information about target magnitude or ordering. We propose FFR (Forward-Forward for Regression), to our knowledge, the first framework to extend FF to real-world regression and demonstrate competitive performance across diverse real-world datasets. FFR introduces three key innovations: (1) an ordinal competitive goodness function that replaces contrastive pairs with competitive learning between partitioned neuron groups under distance-aware ordinal supervision; (2) a stratified ladder architecture where shallow layers learn coarse ordinal discrimination and deeper layers refine into fine-grained regression, with multi-scale feature aggregation for inter-layer collaboration; and (3) hierarchical prediction with uncertainty estimation, where multi-scale predictors jointly provide robust predictions and prediction confidence as a free-lunch. Extensive experimental results show FFR recovers on average 98.6% of BP's accuracy across five real-world regression benchmarks while reducing peak training memory to only 27% of BP's at depth 8 and 8% at depth 32, with per-iteration time around 72% of BP's, and substantially outperforms all BP-free competitors.

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arXiv2026

Scalable machine learning-based approaches for energy saving in densely deployed Open RAN

Densely deployed base stations are responsible for the majority of the energy consumed in Radio access network (RAN). While these deployments are crucial to deliver the required data rate in busy hours of the day, the network can save energy by switching some of them to sleep mode and maintain the coverage and quality of service with the other ones. Benefiting from the flexibility provided by the Open RAN in embedding machine learning (ML) in network operations, in this work we propose Deep Reinforcement Learning (DRL)-based energy saving solutions. Firstly we propose 3 different DRL-based methods in the form of xApps which control the Active/Sleep mode of up to 6 radio units (RUs) from Near Real time RAN Intelligent Controller (RIC). We also propose a further scalable federated DRL-based solution with an aggregator as an rApp in None Real time RIC and local agents as xApps. Our simulation results present the convergence of the proposed methods. We also compare the performance of our federated DRL across three layouts spanning 6--24 RUs and 500--1000\,m regions, including a composite multi-region scenario. The results show that our proposed federated TD3 algorithm achieves up to 43.75\% faster convergence, more than 50\% network energy saving and 37. 4\% lower training energy versus centralized baselines, while maintaining the quality of service and improving the robustness of the policy.

eess.SY↗

arXiv2025

Green O-RAN Operation: a Modern ML-Driven Network Energy Consumption Optimisation

The increasing energy demand of next-generation mobile networks, especially 6G, is becoming a major concern, particularly due to the high power usage of base station components RU, which often remain active even during low traffic periods. To tackle this challenge, our study focuses on improving energy efficiency in O-RAN systems using intelligent control strategies. TD3 leverages a continuous action space to overcome the limitations of traditional discrete-action methods like DQN. By avoiding exponential growth in action space, TD3 enables more precise control of RU sleep modes in dense and large radio environments. Simulation results show that our approach consistently achieves over 50% energy savings compared to the always-on baseline, with TD3 outperforming DQN-based methods by up to 6%, while also offering better stability and faster convergence.

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arXiv2024

Energy Saving in 6G O-RAN Using DQN-based xApp

Open Radio Access Network (RAN) is a transformative paradigm that supports openness, interoperability, and intelligence, with the O-RAN architecture being the most recognized framework in academia and industry. In the context of Open RAN, the importance of Energy Saving (ES) is heightened, especially with the current direction of network densification in sixth generation of mobile networks (6G). Traditional energy-saving methods in RAN struggle with the increasing dynamics of the network. This paper proposes using Reinforcement Learning (RL), a subset of Machine Learning (ML), to improve ES. We present a novel deep RL method for ES in 6G O-RAN, implemented as xApp (ES-xApp). We developed two Deep Q-Network (DQN)-based ES-xApps. ES-xApp-1 uses RSS and User Equipment (UE) geolocations, while ES-xApp-2 uses only RSS. The proposed models significantly outperformed heuristic and baseline xApps, especially with over 20 UEs. With 50 UEs, 50% of Radio Cards (RCs) were switched off, compared to 17% with the heuristic algorithm. We have observed that more informative inputs may lead to more stable training and results. This paper highlights the necessity of energy conservation in wireless networks and offers practical strategies and evidence for future research and industry practices.

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arXiv2024

Continuous Transfer Learning for UAV Communication-aware Trajectory Design

Deep Reinforcement Learning (DRL) emerges as a prime solution for Unmanned Aerial Vehicle (UAV) trajectory planning, offering proficiency in navigating high-dimensional spaces, adaptability to dynamic environments, and making sequential decisions based on real-time feedback. Despite these advantages, the use of DRL for UAV trajectory planning requires significant retraining when the UAV is confronted with a new environment, resulting in wasted resources and time. Therefore, it is essential to develop techniques that can reduce the overhead of retraining DRL models, enabling them to adapt to constantly changing environments. This paper presents a novel method to reduce the need for extensive retraining using a double deep Q network (DDQN) model as a pretrained base, which is subsequently adapted to different urban environments through Continuous Transfer Learning (CTL). Our method involves transferring the learned model weights and adapting the learning parameters, including the learning and exploration rates, to suit each new environment specific characteristics. The effectiveness of our approach is validated in three scenarios, each with different levels of similarity. CTL significantly improves learning speed and success rates compared to DDQN models initiated from scratch. For similar environments, Transfer Learning (TL) improved stability, accelerated convergence by 65%, and facilitated 35% faster adaptation in dissimilar settings.

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arXiv2024

Enhancing Energy Efficiency in O-RAN Through Intelligent xApps Deployment

The proliferation of 5G technology presents an unprecedented challenge in managing the energy consumption of densely deployed network infrastructures, particularly Base Stations (BSs), which account for the majority of power usage in mobile networks. The O-RAN architecture, with its emphasis on open and intelligent design, offers a promising framework to address the Energy Efficiency (EE) demands of modern telecommunication systems. This paper introduces two xApps designed for the O-RAN architecture to optimize power savings without compromising the Quality of Service (QoS). Utilizing a commercial RAN Intelligent Controller (RIC) simulator, we demonstrate the effectiveness of our proposed xApps through extensive simulations that reflect real-world operational conditions. Our results show a significant reduction in power consumption, achieving up to 50% power savings with a minimal number of User Equipments (UEs), by intelligently managing the operational state of Radio Cards (RCs), particularly through switching between active and sleep modes based on network resource block usage conditions.

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