Gregory Dudek

Seowoo Jang

Xue Liu

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G sys… (see more)tems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixed-variable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using a-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.

2023-05-31

ICC 2023 - IEEE International Conference on Communications (published)

Multi-Agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks

Jikun Kang

Ju Wang

Ekram Hossain

Xue Liu

In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the… (see more) BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and satisfy the UEs' service requirements. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate collaboration among the BSs (i.e., agents). In particular, to solve the Markov game and find a Nash equilibrium policy, we embrace the idea of adopting a nature agent to model the system uncertainty. Moreover, we utilize the self-attention mechanism, which encourages high-performance BSs to assist low-performance BSs. In addition, we consider two types of schemes, which can facilitate load balancing for both active UEs and idle UEs. We carry out extensive evaluations by simulations, and simulation results illustrate that, compared to the state-of-the-art MARL methods, Robust-MA3C scheme can improve the overall performance by up to 45%.

2023-05-31

ICC 2023 - IEEE International Conference on Communications (published)

Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios

Yi Tian Xu

Jimmy Li

M. Jenkin

Seowoo Jang

Xue Liu

With the continuous growth in communication network complexity and traffic volume, communication load balancing solutions are receiving incr… (see more)easing attention. Specifically, reinforcement learning (RL)-based methods have shown impressive performance compared with traditional rule-based methods. However, standard RL methods generally require an enormous amount of data to train, and generalize poorly to scenarios that are not encountered during training. We propose a policy reuse framework in which a policy selector chooses the most suitable pre-trained RL policy to execute based on the current traffic condition. Our method hinges on a policy bank composed of policies trained on a diverse set of traffic scenarios. When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training. Experiments demonstrate that this framework can outperform classical and adaptive rule-based methods by a large margin.

2023-05-31

ICC 2023 - IEEE International Conference on Communications (published)

Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks

Igor Kozlov

Dmitriy Rivkin

Wei-Di Chang

Xue Liu

Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thou… (see more)sands of transmitting devices (cells). Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance. The effects of such changes are challenging to predict and will become even more so with the adoption of fifth-generation/sixth-generation (5G/6G) networks. Therefore, RAN monitoring is vital for network operators. We propose a self-supervised learning framework that leverages self-attention and self-distillation for this task. It works by detecting changes in Performance Measurement data, a collection of time-varying metrics which reflect a set of diverse measurements of the network performance at the cell level. Experimental results show that our approach outperforms the state of the art by 4% on a real-world based dataset consisting of about hundred thousands time series. It also has the merits of being scalable and generalizable. This allows it to provide deep insight into the specifics of mode of operation changes while relying minimally on expert knowledge.

2023-05-31

ICC 2023 - IEEE International Conference on Communications (published)

Reinforcement learning for communication load balancing: approaches and challenges

Jimmy Li

Amal Ferini

Yi Tian Xu

M. Jenkin

Seowoo Jang

Xue Liu

The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for e… (see more)nhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.

2023-05-30

Frontiers of Computer Science (published)

Neural Bee Colony Optimization: A Case Study in Public Transit Network Design

Andrew Holliday

In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization. We do this in t… (see more)he context of the transit network design problem, a uniquely challenging combinatorial optimization problem with real-world importance. We train a neural network policy to perform single-shot planning of individual transit routes, and then incorporate it as one of several sub-heuristics in a modified Bee Colony Optimization (BCO) metaheuristic algorithm. Our experimental results demonstrate that this hybrid algorithm outperforms the learned policy alone by up to 20% and the original BCO algorithm by up to 53% on realistic problem instances. We perform a set of ablations to study the impact of each component of the modified algorithm.

2023-05-17

ArXiv (preprint)

Eliminating Space Scanning: Fast mmWave Beam Alignment with UWB Radios

Ju Wang

X. T. Chen

Xue Liu

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond… (see more) (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays. To eliminate the need for complex space scanning, this article presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge of applying this idea in the real world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a small number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUltiple SIgnal Classification (MF-MUSIC) algorithm, which extends the classic MUltiple SIgnal Classification (MUSIC) algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. Extensive real-world experiments and numerical simulations illustrate the advantage of the proposed MF-MUSIC algorithm. MF-MUSIC uses only three antennas to achieve an accurate angle estimation, which is a mere 0.15° (or a relative difference of 3.6%) different from the state-of-the-art 16-antenna-based angle estimation method.

2023-05-15

ACM Transactions on Sensor Networks (published)

Bayesian Q-learning With Imperfect Expert Demonstrations

Xiru Zhu

Guided exploration with expert demonstrations improves data efficiency for reinforcement learning, but current algorithms often overuse expe… (see more)rt information. We propose a novel algorithm to speed up Q-learning with the help of a limited amount of imperfect expert demonstrations. The algorithm avoids excessive reliance on expert data by relaxing the optimal expert assumption and gradually reducing the usage of uninformative expert data. Experimentally, we evaluate our approach on a sparse-reward chain environment and six more complicated Atari games with delayed rewards. With the proposed methods, we can achieve better results than Deep Q-learning from Demonstrations (Hester et al., 2017) in most environments.

2022-12-08

NeurIPS.cc/2022/Workshop/DeepRL (accepted)

openreview.net

Behaviour Learning with Adaptive Motif Discovery and Interacting Multiple Model

Hanging Zhao

Travis Manderson

Hao Zhang

Xue Liu

We propose an approach that enables simultaneous interpretable learning of a high-level discrete behaviour and its low-level rhythmic sub-be… (see more)haviour. We do this though a unified reward function, where a reward function that only describes low-level behaviour, with less impact on learning of other behaviours is recovered from few-shot motion demonstrations. To this end, we first extract local behaviour motifs from state-only human demonstrations and random driving samples using an adaptive motif discovery approach derived from the Matrix Profile algorithm. We then optimize parameters for motif discovery by maximizing the sum and entropy over motif sizes. Interacting Multiple Model (IMM) estimators are constructed on top of linear-Gaussian dynamics of discovered motifs, the cumulative distributions over motifs estimated by IMMs serve as the basis of the reward function. By combining the recovered reward with the terrain type signal gathered from the environment, we are able to train a dual-objective off-road vehicle controller that demonstrates both terrain selection and human-like driving behaviours. Compared with related approaches across 10 people, our rhythmic behaviour reward recovery approach enables the controller to produce higher preference over human driving demonstrations. In addition to performing more stable across different people with 87% less variance than the best baseline in rhythmic behaviour indicator, our method reduces the negative effects on higher-level behaviour learning while maintaining high interpretability at all stages of the algorithm.

2022-10-22

IEEE/RJS International Conference on Intelligent Robots and Systems (published)

Finger-STS: Combined Proximity and Tactile Sensing for Robotic Manipulation

Francois R. Hogan

Jean-François Tremblay

Bobak H. Baghi

Michael Jenkin

Kaleem Siddiqi

This paper introduces and develops novel touch sensing technologies that enable robots to better sense and react to to intermittent contact … (see more)interactions. We present Finger-STS, a robotic finger embodiment of the See-Through-your-Skin (STS) sensor that can capture 1) an “in the hand” visual perspective of an object that is being manipulated and 2) a high resolution tactile imprint of the contact geometry. We demonstrate the value of the sensor on a Bead Maze task. Here the multimodal feedback provided by the Finger-STS is leveraged by a robot to locate a bead visually and to guide it across a wire in response to tactile cues, with no additional sensing or planning required. To achieve this, we introduce a set of relevant visuotactile operations using computer vision-based algorithms. In particular, we sense the proximity of the object relative to the sensor as well as the nature of contact as a high resolution stick/slip vector field tracking the object motion in the finger.

2022-09-30

IEEE Robotics and Automation Letters (published)

IL-flOw: Imitation Learning from Observation using Normalizing Flows

Wei-Di Chang

Juan Higuera

Scott Fujimoto

David Meger

2022-05-18

ArXiv (preprint)

Learning Assisted Identification of Scenarios Where Network Optimization Algorithms Under-Perform

Dmitriy Rivkin

David Meger

X. T. Chen

Xue Liu

We present a generative adversarial method that uses deep learning to identify network load traffic conditions in which network optimization… (see more) algorithms under-perform other known algorithms: the Deep Convolutional Failure Generator (DCFG). The spatial distribution of network load presents challenges for network operators for tasks such as load balancing, in which a network optimizer attempts to maintain high quality communication while at the same time abiding capacity constraints. Testing a network optimizer for all possible load distributions is challenging if not impossible. We propose a novel method that searches for load situations where a target network optimization method underperforms baseline, which are key test cases that can be used for future refinement and performance optimization. By modeling a realistic network simulator's quality assessments with a deep network and, in parallel, optimizing a load generation network, our method efficiently searches the high dimensional space of load patterns and reliably finds cases in which a target network optimization method under-performs a baseline by a significant margin.

2021-12-06

2021 IEEE Global Communications Conference (GLOBECOM) (published)