Learning-based Decision Making in Wireless Communications

Fueled by emerging applications and exponential increase in data traffic, wireless networks have recently grown significantly and become more complex. In such large-scale complex wireless networks, it is challenging and, oftentimes, infeasible for conventional optimization methods to quickly solve critical decision-making problems. With this motivation, in this thesis, machine learning methods are developed and utilized for obtaining optimal/near-optimal solutions for timely decision making in wireless networks. Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. In this context, we in the first part of the thesis study content caching at the wireless network edge using a deep reinforcement learning framework with Wolpertinger architecture. Initially, we develop a learning-based caching policy for a single base station aiming at maximizing the long-term cache hit rate. Then, we extend this study to a wireless communication network with multiple edge nodes. In particular, we propose deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching. Next, with the purpose of making efficient use of limited spectral resources, we develop a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. In the single-user model, in order to evaluate the performance of the proposed channel access policy and the framework\u27s tolerance against uncertainty, we explore different channel switching patterns and different switching probabilities. In the case of multiple users, we analyze the probabilities of each user accessing channels with favorable channel conditions and the probability of collision. Following the analysis of the proposed learning-based dynamic multichannel access policy, we consider adversarial attacks on it. In particular, we propose two adversarial policies, one based on feed-forward neural networks and the other based on deep reinforcement learning policies. Both attack strategies aim at minimizing the accuracy of a deep reinforcement learning based dynamic channel access agent, and we demonstrate and compare their performances. Next, anomaly detection as an active hypothesis test problem is studied. Specifically, we study deep reinforcement learning based active sequential testing for anomaly detection. We assume that there is an unknown number of abnormal processes at a time and the agent can only check with one sensor in each sampling step. To maximize the confidence level of the decision and minimize the stopping time concurrently, we propose a deep actor-critic reinforcement learning framework that can dynamically select the sensor based on the posterior probabilities. Separately, we also regard the detection of threshold crossing as an anomaly detection problem, and analyze it via hierarchical generative adversarial networks (GANs). In the final part of the thesis, to address state estimation and detection problems in the presence of noisy sensor observations and probing costs, we develop a soft actor-critic deep reinforcement learning framework. Moreover, considering Byzantine attacks, we design a GAN-based framework to identify the Byzantine sensors. To evaluate the proposed framework, we measure the performance in terms of detection accuracy, stopping time, and the total probing cost needed for detection

Experience-driven Control For Networking And Computing

Modern networking and computing systems have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this thesis, we aim to study system control problems from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL), to develop experience-driven model-free approaches, which enable a network or a device to learn the best way to control itself from its own experience (e.g., runtime statistics data) rather than from accurate mathematical models, just as a human learns a new skill (e.g., driving, swimming, etc). To demonstrate the feasibility and superiority of this experience-driven control design philosophy, we present the design, implementation, and evaluation of multiple DRL-based control frameworks on two fundamental networking problems, Traffic Engineering (TE) and Multi-Path TCP (MPTCP) congestion control, as well as one cutting-edge application, resource co-scheduling for Deep Neural Network (DNN) models on mobile and edge devices with heterogeneous hardware. We first propose DRL-TE, a DRL-based framework that enables experience-driven networking for TE. DRL-TE maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful DNNs. We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. Furthermore, we propose an Actor-Critic-based Transfer learning framework for TE, ACT-TE, which solves a practical problem in experience-driven networking: when network configurations are changed, how to train a new DRL agent to effectively and quickly adapt to the new environment. In the new network environment, ACT-TE leverages policy distillation to rapidly learn a new control policy from both old knowledge (i.e., distilled from the existing agent) and new experience (i.e., newly collected samples). In addition, we propose DRL-CC to enable experience-driven congestion control for MPTCP. DRL-CC utilizes a single (instead of multiple independent) DRL agent to dynamically and jointly perform congestion control for all active MPTCP flows on an end host with the objective of maximizing the overall utility. The novelty of our design is to utilize a flexible recurrent neural network, LSTM, under a DRL framework for learning a representation for all active flows and dealing with their dynamics. Moreover, we integrate the above LSTM-based representation network into an actor-critic framework for continuous congestion control, which applies the deterministic policy gradient method to train actor, critic, and LSTM networks in an end-to-end manner. With the emergence of more and more powerful chipsets and hardware and the rise of Artificial Intelligence of Things (AIoT), there is a growing trend for bringing DNN models to empower mobile and edge devices with intelligence such that they can support attractive AI applications on the edge in a real-time or near real-time manner. To leverage heterogeneous computational resources (such as CPU, GPU, DSP, etc) to effectively and efficiently support concurrent inference of multiple DNN models on a mobile or edge device, in the last part of this thesis, we propose a novel experience-driven control framework for resource co-scheduling, which we call COSREL. COSREL has the following desirable features: 1) it achieves significant speedup over commonly-used methods by efficiently utilizing all the computational resources on heterogeneous hardware; 2) it leverages DRL to make dynamic and wise online scheduling decisions based on system runtime state; 3) it is capable of making a good tradeoff among inference latency, throughput and energy efficiency; and 4) it makes no changes to given DNN models, thus preserves their accuracies. To validate and evaluate the proposed frameworks, we conduct extensive experiments on packet-level simulation (for TE), testbed with modified Linux kernel (for MPTCP), and off-the-shelf Android devices (for resource co-scheduling). The results well justify the effectiveness of these frameworks, as well as their superiority over several baseline methods

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms

Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey

International audienceNowadays, many research studies and industrial investigations have allowed the integration of the Internet of Things (IoT) in current and future networking applications by deploying a diversity of wireless-enabled devices ranging from smartphones, wearables, to sensors, drones, and connected vehicles. The growing number of IoT devices, the increasing complexity of IoT systems, and the large volume of generated data have made the monitoring and management of these networks extremely difficult. Numerous research papers have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques to overcome these difficulties by building IoT systems with effective and dynamic decision-making mechanisms, dealing with incomplete information related to their environments. The paper first reviews pre-existing surveys covering the application of RL and DRL techniques in IoT communication technologies and networking. The paper then analyzes the research papers that apply these techniques in wireless IoT to resolve issues related to routing, scheduling, resource allocation, dynamic spectrum access, energy, mobility, and caching. Finally, a discussion of the proposed approaches and their limits is followed by the identification of open issues to establish grounds for future research directions proposal

Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.Comment: 15 pages, 10 figures, 2 algorithm