Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Under-actuated back-stepping: An introduction

The stabilization problem for a class of underactuated systems is solved. This is achieved via a novel backstepping based method that we call under-actuated backstepping. The method is developed for linear under-actuated systems first and then extended to nonlinear systems via an example. Numerical simulations are given to demonstrate the effectiveness of the proposed under-actuated back-stepping method

Shared-control for fully actuated linear mechanical systems

This paper presents a shared-control algorithm for fully actuated, linear, mechanical systems. It is assumed that the position of the mechanical system is constrained by a set of linear inequalities. These model convex and with the addition of “logical variables” non-convex feasible sets. The shared-control action is implemented using an hysteresis-based switching strategy. Formal properties of the algorithm are established using a partial Lyapunov analysis. Simulation results on simple case studies illustrate the effectiveness of the proposed algorithm

Shared-control for a UAV operating in the 3D space

This paper presents a shared-control scheme for a UAV moving in a 3D space while its feasible Cartesian position set is defined by a group of linear inequalities. A hysteresis switch is used to combine the human input and the feedback control input based on the definitions of a safe set, a hysteresis set and a “dangerous” set. Case studies given in the paper show the effectiveness of the shared-control algorithm

Shared-control for the kinematic model of a rear-wheel drive car

This paper presents a shared-control algorithm for the kinematic model of a rear-wheel drive car, for which the set of feasible Cartesian positions is defined by a group of linear inequalities. The shared-control scheme is based on a hysteresis switch and its properties are established by a Lyapunov-like analysis. Simple numerical examples demonstrate the effectiveness of the shared-control law

Shared-control for the kinematic model of a mobile robot

This paper presents a shared-control algorithm for the kinematic model of a mobile robot. The set of feasible position of the robot is defined by a group of linear inequalities. The shared-control strategy is based on a hysteresis switch and its properties are established by a Lyapunov-like analysis. Simulation results illustrate the effectiveness of the algorithm

Shared-control for typical driving scenarios

A shared-control algorithm for the kinematic model of a rear-wheel driving car is presented. The design of the shared-controller is based on a hysteresis switch and its properties are established by a Lyapunov-like analysis. The shared-controller guarantees the safety of the car in both predefined, static environments and time-varying environments. The effectiveness of the controller is verified by two studies

Shared-control for the kinematic model of a mobile robot

This paper presents a shared-control algorithm for the kinematic model of a mobile robot. The set of feasible position of the robot is defined by a group of linear inequalities. The shared-control strategy is based on a hysteresis switch and its properties are established by a Lyapunov-like analysis. Simulation results illustrate the effectiveness of the algorithm

Output-feedback shared-control for fully actuated linear mechanical systems

This paper presents an output feedback shared-control algorithm for fully-actuated, linear, mechanical systems. The feasible configurations of the system are described by a group of linear inequalities which characterize a convex admissible set. The properties of the shared-control algorithm are established with a Lyapunov-like analysis. Simple numerical examples demonstrate the effectiveness of the strategy

State and output-feedback shared-control for a class of linear constrained systems

This technical note presents state and output feedback shared-control algorithms for a class of linear systems in the presence of constraints on the output described by means of linear inequalities. The properties of the closed-loop shared-control systems are studied using Lyapunov arguments. Simulation results demonstrate the effectiveness of the algorithm