Reciprocity of Algorithms Solving Distributed Consensus-Based Optimization and Distributed Resource Allocation

This paper aims at proposing a procedure to derive distributed algorithms for distributed consensus-based optimization by using distributed algorithms for network resource allocation and vice versa over switching networks with/without synchronous protocol. It is shown that first-order gradient distributed consensus-based optimization algorithms can be used for finding an optimal solution of distributed resource allocation with synchronous protocol under weaker assumptions than those given in the literature for non-switching (static) networks. It is shown that first-order gradient distributed resource allocation algorithms can be utilized for finding an optimal solution of distributed consensus-based optimization. The results presented here can be applied to time-varying and random directed networks with or without synchronous protocol with arbitrary initialization. As a result, several algorithms can now be used to derive distributed algorithms for both consensus-based optimization and resource allocation, that can overcome limitations of the existing results. While the focus of this paper is on the first-order gradient algorithms, it is to be noted that the results also work with second-order gradient algorithms.Comment: 8 page

Power Allocation and Parameter Estimation for Multipath-based 5G Positioning

We consider a single-anchor multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system with imperfectly synchronized transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position based on the received reference signals. The Tx, having (imperfect) prior knowledge about the Rx location and the surrounding geometry, transmits the reference signals based on a set of fixed beams. In this work, we develop strategies for the power allocation among the beams aiming to minimize the expected Cram\'er-Rao lower bound (CRLB) for Rx positioning. Additional constraints on the design are included to ensure that the line-of-sight (LOS) path is detected with high probability. Furthermore, the effect of clock asynchronism on the resulting allocation strategies is also studied. We also propose a gridless compressed sensing-based position estimation algorithm, which exploits the information on the clock offset provided by non-line-of-sight paths, and show that it is asymptotically efficient.Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless Communication

Reaching Consensus with uncertainty on a network

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 189-197).As modern communication networks become increasingly advanced, so does the ability and necessity to communicate to make more informed decisions. However, communication alone is not sucient; the method by which information is incorporated and used to make the decision is of critical importance. This thesis develops a novel distributed agreement protocol that allows multiple agents to agree upon a parameter vector particularly when each agent has a unique measure of possibly non-Gaussian uncertainty in its estimate. The proposed hyperpa- rameter consensus algorithm builds upon foundations in both the consensus and data fusion communities by applying Bayesian probability theory to the agreement problem. Unique to this approach is the ability to converge to the centralized Bayesian parameter estimate of non-Gaussian distributed variables over arbitrary, strongly connected networks and without the burden of the often prohibitively complex lters used in traditional data fusion solutions. Convergence properties are demonstrated for local estimates described by a number of common probability distributions and over a range of networks. The benet of the proposed method in distributed estimation is shown through its application to a multi-agent reinforcement learning problem. Additionally, this thesis describes the hardware implementation and testing of a distributed coordinated search, acquisition and track algorithm, which is shown to capably handle the con icting goals of searching and tracking. However, it is sensitive to the estimated target noise characteristics and assumes consistent search maps across the fleet.(cont.) Two improvements are presented to correct these issues: an adaptive tracking algorithm which improves the condence of target re-acquisition in periodic tracking scenarios, and a method to combine disjoint probabilistic search maps using the hyperparameter consensus algorithm to obtain the proper centralized search map.by Cameron S. R. Fraser.S.M

Intelligent voltage dip mitigation in power networks with distributed generation

Includes bibliographical references.The need for ensuring good power quality (PQ) cannot be over-emphasized in electrical power system operation and management. PQ problem is associated with any electrical distribution and utilization system that experiences any voltage, current or frequency deviation from normal operation. In the current power and energy scenario, voltage-related PQ disturbances like voltage dips are a fact which cannot be eliminated from electrical power systems since electrical faults, and disturbances are stochastic in nature. Voltage dip tends to lead to malfunction or shut down of costly and mandatory equipment and appliances in consumers’ systems causing significant financial losses for domestic, commercial and industrial consumers. It accounts for the disruption of both the performance and operation of sensitive electrical and electronic equipment, which reduces the efficiency and the productivity of power utilities and consumers across the globe. Voltage dips are usually experienced as a result of short duration reduction in the r.m.s. (r.m.s.- root mean square) value of the declared or nominal voltage at the power frequency and is usually followed by recovery of the voltage dip after few seconds. The IEEE recommended practice for monitoring electric power quality (IEEE Std. 1159-2009, revised version of June 2009), provides definitions to label an r.m.s. voltage disturbance based upon its duration and voltage magnitude. These disturbances can be classified into transient events such as voltage dips, swells and spikes. Other long duration r.m.s. voltage variations are mains failures, interruption, harmonic voltage distortion and steady-state overvoltages and undervoltages. This PhD research work deals with voltage dip phenomena only. Initially, the present power network was not designed to accommodate renewable distributed generation (RDG) units. The advent and deployment of RDG over recent years and high penetration of RDG has made the power network more complex and vulnerable to PQ disturbances. It is a well-known fact that the degree of newly introduced RDG has increased rapidly and growing further because of several reasons, which include the need to reduce environmental pollution and global warming caused by emission of carbon particles and greenhouse gases, alleviating transmission congestion and loss reduction. RDG ancillary services support especially voltage and reactive power support in electricity networks are currently being recognized, researched and found to be quite useful in voltage dip mitigation

Signal Processing for Wireless Power and Information Transfer

The rapid development of the Internet of Things (IoT) and wireless sensor network (WSN) technologies enable easy access and control of a variety forms of information and data from numerous number of smart devices, and give rise to many novel applications and research areas such as smart home, machine type communications, etc. However due to the small sizes, sophisticated environment, and large number of devices in network, it is hard to directly power the devices from grid. Hence the power connectivity remains one of the major issues that needs to be addressed for related IoT applications. Wireless power transfer (WPT) and backscatter communications are provisioned to be prominent solutions to overcome the power connectivity challenge, but they suer strong efficiency limitation which becomes the barrier to universally popularize such technologies. On the other hand, network optimization is also a research focus of such applications which significantly affects the performance of the system due to the high volume of connected devices and different features. In this thesis we propose advanced techniques to overcome the challenges on the low efficiency and network design of the wireless information and power transfer systems. The thesis consists of two parts. In the first part we focus on the power transmitter design which addresses the low efficiency issue associated with backscatter communication and WPT. In Chapter 2, we consider a backscatter RFID system with the multi-antenna reader and propose a blind transmit and receive adaptive beamforming algorithm. The interrogation range and data transmission performance are both investigated under such configuration. In Chapter 3 we study wireless power transfer by the beamspace large-scale MIMO system with lens antenna arrays. We first present the WPT model for the beamspace MIMO which is derived from the spatial MIMO model. By constraining on the number of RF chains in the transmitter, we formulate two WPT optimization problems: the sum power transfer problem and the max-min power transfer problem. For both problems we consider two different transmission schemes, the multi-stream and uni-stream transmissions, and we propose different algorithms to solve both problems in both schemes respectively. In the second part we study the network optimization problems in the WPT and backscatter systems. In Chapter 4, we study the resource allocation problem for a RF-powered network, where the objective is to maximize the total data throughput of all sensors. We break the problem into two subproblems: the sensor battery energy utilization problem and the charging power allocation problem of the central node, which is an RF power transmitter that transmits RF power to the sensors. We analyze and show several key properties of both problems, and then propose computationally efficient algorithms to solve both problems optimally. In Chapter 5, we study the time scheduling problem in RF-powered backscatter communication networks, where all transmitters can operates in either backscattering mode or harvest-then-transmit (HTT) mode. The objective is to decide the operating mode of each transmitter and minimize the total transmission time of the network. We also consider both ideal and realistic transmitters based on different internal power consumption models for HTT transmitters. Under both transmitter models we show several key properties, and propose bisection based algorithms which has low computational complexity that solves the problem optimally. The results are then extended to the massive MIMO regime