Informed Scheduling by Stochastic Residual Belief Propagation in Distributed Wireless Networks

This letter devises a novel algorithm for cooperative spectrum sensing based on belief propagation (BP) for distributed wireless networks. The algorithm, called stochastic residual belief propagation (SR-BP), extends the use of residual belief propagation (R-BP) to distributed networks, improving the accuracy, convergence rate, and communication cost for cooperative spectrum sensing. We demonstrate that SR-BP converges to a unique fixed point under conditions similar to those ensuring convergence of asynchronous BP. Then, we develop a way to derive a probability distribution from the residual of each message. Finally, we provide numerical results to showcase the improvements in convergence speed, message overhead and detection accuracy of SR-BP

Probabilistic graphical models for mobile pedestrian localization in 3D environments

This PhD thesis considers the problem of locating wireless nodes in indoors GPS-denied environments using probabilistic graphical models. Time-of-arrival (ToA) distance observations are assumed with Non-Line-of-Sight (NLoS) communications and a lack of adequate anchors. As a solution cooperative localization is developed using Probabilistic Graphical Models (PGMs). The nodes infer their position in an iterative message-passing algorithm, in a distributed manner, given a set of noisy distance observations and a few anchors. The focus of this thesis is to develop algorithms that decrease computational complexity, while maintaining or improving accuracy. Firstly, we develop the Hybrid Ellipsoid Variational Algorithm (HEVA), which extends probabilistic inference in 3D localization, combining NLoS mitigation for ToA. Simulation results illustrate that HEVA significantly outperforms traditional Non-parametric Belief Propagation (NBP) methods in localization while requires only 50% of their complexity. In addition, we present a novel parametric for Belief Propagation (BP) algorithm. The proposed Grid Belief Propagation (Grid-BP) approach allows extremely fast calculations and works nicely with existing grid-based coordinate systems, e.g. NATO military grid reference system (MGRS). This allows localization using a Global Coordinate System (GCS). Simulation results demonstrate that Grid-BP achieves similar accuracy at much reduced complexity when compared to common techniques. We also present an algorithm that combines Inertial Navigation System (INS) and Pedestrian Dead Reckoning (PDR), namely Probabilistic Hybrid INS/PDR Mobility Tracking Algorithm (PHIMTA), which provides high accuracy tracking for mobile nodes. We combine it with Grid-BP and stop-and-go (SnG) algorithms, showcasing improved accuracy, at very low computational cost. Finally, we present Stochastic Residual Belief Propagation (SR-BP). SR-BP extends the use of Residual Belief Propagation (R-BP) to distributed networks, improving the accuracy, convergence rate, and communication cost. We prove SR-BP convergence to a unique fixed point under conditions similar to those ensuring convergence of asynchronous BP. Finally, numerical results showcase the improvements in convergence speed, message overhead and detection accuracy of SR-BP

Cyber-Physical Systems for Smart Water Networks: A Review

There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.acceptedVersio

모델기반강화학습을이용한공정제어및최적화

학위논문(박사)--서울대학교 대학원 :공과대학 화학생물공학부,2020. 2. 이종민.순차적 의사결정 문제는 공정 최적화의 핵심 분야 중 하나이다. 이 문제의 수치적 해법 중 가장 많이 사용되는 것은 순방향으로 작동하는 직접법 (direct optimization) 방법이지만, 몇가지 한계점을 지니고 있다. 최적해는 open-loop의 형태를 지니고 있으며, 불확정성이 존재할때 방법론의 수치적 복잡도가 증가한다는 것이다. 동적 계획법 (dynamic programming) 은 이러한 한계점을 근원적으로 해결할 수 있지만, 그동안 공정 최적화에 적극적으로 고려되지 않았던 이유는 동적 계획법의 결과로 얻어진 편미분 방정식 문제가 유한차원 벡터공간이 아닌 무한차원의 함수공간에서 다루어지기 때문이다. 소위 차원의 저주라고 불리는 이 문제를 해결하기 위한 한가지 방법으로서, 샘플을 이용한 근사적 해법에 초점을 둔 강화학습 방법론이 연구되어 왔다. 본 학위논문에서는 강화학습 방법론 중, 공정 최적화에 적합한 모델 기반 강화학습에 대해 연구하고, 이를 공정 최적화의 대표적인 세가지 순차적 의사결정 문제인 스케줄링, 상위단계 최적화, 하위단계 제어에 적용하는 것을 목표로 한다. 이 문제들은 각각 부분관측 마르코프 결정 과정 (partially observable Markov decision process), 제어-아핀 상태공간 모델 (control-affine state space model), 일반적 상태공간 모델 (general state space model)로 모델링된다. 또한 각 수치적 모델들을 해결하기 위해 point based value iteration (PBVI), globalized dual heuristic programming (GDHP), and differential dynamic programming (DDP)로 불리는 방법들을 도입하였다. 이 세가지 문제와 방법론에서 제시된 특징들을 다음과 같이 요약할 수 있다: 첫번째로, 스케줄링 문제에서 closed-loop 피드백 형태의 해를 제시할 수 있었다. 이는 기존 직접법에서 얻을 수 없었던 형태로서, 강화학습의 강점을 부각할 수 있는 측면이라 생각할 수 있다. 두번째로 고려한 하위단계 제어 문제에서, 동적 계획법의 무한차원 함수공간 최적화 문제를 함수 근사 방법을 통해 유한차원 벡터공간 최적화 문제로 완화할 수 있는 방법을 도입하였다. 특히, 심층 신경망을 이용하여 함수 근사를 하였고, 이때 발생하는 여러가지 장점과 수렴 해석 결과를 본 학위논문에 실었다. 마지막 문제는 상위 단계 동적 최적화 문제이다. 동적 최적화 문제에서 발생하는 제약 조건하에서 강화학습을 수행하기 위해, 원-쌍대 미분동적 계획법 (primal-dual DDP) 방법론을 새로 제안하였다. 앞서 설명한 세가지 문제에 적용된 방법론을 검증하고, 동적 계획법이 직접법에 비견될 수 있는 방법론이라는 주장을 실증하기 위해 여러가지 공정 예제를 실었다.Sequential decision making problem is a crucial technology for plant-wide process optimization. While the dominant numerical method is the forward-in-time direct optimization, it is limited to the open-loop solution and has difficulty in considering the uncertainty. Dynamic programming method complements the limitations, nonetheless associated functional optimization suffers from the curse-of-dimensionality. The sample-based approach for approximating the dynamic programming, referred to as reinforcement learning (RL) can resolve the issue and investigated throughout this thesis. The method that accounts for the system model explicitly is in particular interest. The model-based RL is exploited to solve the three representative sequential decision making problems; scheduling, supervisory optimization, and regulatory control. The problems are formulated with partially observable Markov decision process, control-affine state space model, and general state space model, and associated model-based RL algorithms are point based value iteration (PBVI), globalized dual heuristic programming (GDHP), and differential dynamic programming (DDP), respectively. The contribution for each problem can be written as follows: First, for the scheduling problem, we developed the closed-loop feedback scheme which highlights the strength compared to the direct optimization method. In the second case, the regulatory control problem is tackled by the function approximation method which relaxes the functional optimization to the finite dimensional vector space optimization. Deep neural networks (DNNs) is utilized as the approximator, and the advantages as well as the convergence analysis is performed in the thesis. Finally, for the supervisory optimization problem, we developed the novel constraint RL framework that uses the primal-dual DDP method. Various illustrative examples are demonstrated to validate the developed model-based RL algorithms and to support the thesis statement on which the dynamic programming method can be considered as a complementary method for direct optimization method.1. Introduction 1 1.1 Motivation and previous work 1 1.2 Statement of contributions 9 1.3 Outline of the thesis 11 2. Background and preliminaries 13 2.1 Optimization problem formulation and the principle of optimality 13 2.1.1 Markov decision process 15 2.1.2 State space model 19 2.2 Overview of the developed RL algorithms 28 2.2.1 Point based value iteration 28 2.2.2 Globalized dual heuristic programming 29 2.2.3 Differential dynamic programming 32 3. A POMDP framework for integrated scheduling of infrastructure maintenance and inspection 35 3.1 Introduction 35 3.2 POMDP solution algorithm 38 3.2.1 General point based value iteration 38 3.2.2 GapMin algorithm 46 3.2.3 Receding horizon POMDP 49 3.3 Problem formulation for infrastructure scheduling 54 3.3.1 State 56 3.3.2 Maintenance and inspection actions 57 3.3.3 State transition function 61 3.3.4 Cost function 67 3.3.5 Observation set and observation function 68 3.3.6 State augmentation 69 3.4 Illustrative example and simulation result 69 3.4.1 Structural point for the analysis of a high dimensional belief space 72 3.4.2 Infinite horizon policy under the natural deterioration process 72 3.4.3 Receding horizon POMDP 79 3.4.4 Validation of POMDP policy via Monte Carlo simulation 83 4. A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system 88 4.1 Introduction 88 4.2 Function approximation and learning with deep neural networks 91 4.2.1 GDHP with a function approximator 91 4.2.2 Stable learning of DNNs 96 4.2.3 Overall algorithm 103 4.3 Results and discussions 107 4.3.1 Example 1: Semi-batch reactor 107 4.3.2 Example 2: Diffusion-Convection-Reaction (DCR) process 120 5. Convergence analysis of the model-based deep reinforcement learning for optimal control of nonlinear control-affine system 126 5.1 Introduction 126 5.2 Convergence proof of globalized dual heuristic programming (GDHP) 128 5.3 Function approximation with deep neural networks 137 5.3.1 Function approximation and gradient descent learning 137 5.3.2 Forward and backward propagations of DNNs 139 5.4 Convergence analysis in the deep neural networks space 141 5.4.1 Lyapunov analysis of the neural network parameter errors 141 5.4.2 Lyapunov analysis of the closed-loop stability 150 5.4.3 Overall Lyapunov function 152 5.5 Simulation results and discussions 157 5.5.1 System description 158 5.5.2 Algorithmic settings 160 5.5.3 Control result 161 6. Primal-dual differential dynamic programming for constrained dynamic optimization of continuous system 170 6.1 Introduction 170 6.2 Primal-dual differential dynamic programming for constrained dynamic optimization 172 6.2.1 Augmented Lagrangian method 172 6.2.2 Primal-dual differential dynamic programming algorithm 175 6.2.3 Overall algorithm 179 6.3 Results and discussions 179 7. Concluding remarks 186 7.1 Summary of the contributions 187 7.2 Future works 189 Bibliography 192Docto

Energy efficient resource allocation for future wireless communication systemsy

Next generation of wireless communication systems envisions a massive number of connected battery powered wireless devices. Replacing the battery of such devices is expensive, costly, or infeasible. To this end, energy harvesting (EH) is a promising technique to prolong the lifetime of such devices. Because of randomness in amount and availability of the harvested energy, existing communication techniques require revisions to address the issues specific to EH systems. In this thesis, we aim at revisiting fundamental wireless communication problems and addressing the future perspective on service based applications with the specific characteristics of the EH in mind. In the first part of the thesis, we address three fundamental problems that exist in the wireless communication systems, namely; multiple access strategy, overcoming the wireless channel, and providing reliability. Since the wireless channel is a shared medium, concurrent transmissions of multiple devices cause interference which results in collision and eventual loss of the transmitted data. Multiple access protocols aim at providing a coordination mechanism between multiple transmissions so as to enable a collision free medium. We revisit the random access protocol for its distributed and low energy characteristics while incorporating the statistical correlation of the EH processes across two transmitters. We design a simple threshold based policy which only allows transmission if the battery state is above a certain threshold. By optimizing the threshold values, we show that by carefully addressing the correlation information, the randomness can be turned into an opportunity in some cases providing optimal coordination between transmitters without any collisions. Upon accessing the channel, a wireless transmitter is faced with a transmission medium that exhibits random and time varying properties. A transmitter can adapt its transmission strategy to the specific state of the channel for an efficient transmission of information. This requires a process known as channel sensing to acquire the channel state which is costly in terms of time and energy. The contribution of the channel sensing operation to the energy consumption in EH wireless transmitters is not negligible and requires proper optimization. We developed an intelligent channel sensing strategy for an EH transmitter communicating over a time-correlated wireless channel. Our results demonstrate that, despite the associated time and energy cost, sensing the channel intelligently to track the channel state improves the achievable long-term throughput significantly as compared to the performance of those protocols lacking this ability as well as the one that always senses the channel. Next, we study an EH receiver employing Hybrid Automatic Repeat reQuest (HARQ) to ensure reliable end-to-end communications. In inherently error-prone wireless communications systems, re-transmissions triggered by decoding errors have a major impact on the energy consumption of wireless devices. We take into account the energy consumption induced by HARQ to develop simple-toimplement optimal algorithms that minimizes the number of retransmissions required to successfully decode the packet. The large number of connected edge devices envisioned in future wireless technologies enable a wide range of resources with significant sensing capabilities. The ability to collect various data from the sensors has enabled many exciting smart applications. Providing data at a certain quality greatly improves the performance of many of such applications. However, providing high quality is demanding for energy limited sensors. Thus, in the second part of the thesis, we optimize the sensing resolution of an EH wireless sensor in order to efficiently utilize the harvested energy to maximize an application dependent utilit

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communications -- A Tutorial

Ultra-reliable low-latency communication (URLLC) constitutes a key service class of the fifth generation and beyond cellular networks. Notably, designing and supporting URLLC poses a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multi-layer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This paper contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Specifically, we overview the frameworks related to i) reliability theory, ii) short packet communications, iii) inequalities, distribution bounds, and tail approximations, iv) rare events simulation, vi) queuing theory and information freshness, and v) large-scale tools such as stochastic geometry, clustering, compressed sensing, and mean-field games. Moreover, we often refer to prominent data-driven algorithms within the scope of the discussed tools/methodologies. Throughout the paper, we briefly review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.Comment: Accepted in IEEE Proceedings of the IEEE. 40 pages, 20 figures, 11 table