Localization of Wireless Terminals via Ising Energy Minimization

点击翻译按钮获取中文摘要

This project aims to design a system that can estimate the position of a person in a smart environment with sub-meter accuracy. This research will use wireless channel state information (CSI), which is the temporal and spectral behaviour of the wireless channel, for location estimation. CSI is commonly used for effective communication, but unfortunately there is a non-negligible knowledge gap for the application of CSI in location estimation. An important step in our research will be the transformation of combinatorial programming problems into the Ising energy model, which is the building block for quantum bit (QUBIT) formulation in quantum computing. Using the concept of compressive sensing (CS), and by discretizing the observation space, the research will show that the localization problem can be transformed into an Ising energy minimization problem, and can be solved using the Markov Chain Monte Carlo (MCMC) method with Digital Annealer (DA). The proposed research creates a new methodology for solving NP-hard problems. The conventional approach to tackling NP-hard problems is by relaxation, were the integer program is approximated by a program that can be solved using conventional optimization programming methods. For example, an L0 norm in the objective function of an optimization problem is commonly replaced with L1 norm to create a convex program. It has been shown in the literature that this substitution frequently results in biased estimates. Our research creates a completely different approach. A main innovation in our approach is solving NP-hard problems directly, and without convex relaxation, by transforming the problem to a binary quadratic optimization (QUBO) problem. This is in sharp contrast to the existing literature and creates a great opportunity for developing new standards and methodologies. Our preliminary studies show that the proposed method removes the bias inherent in convex approximation and provides a more accurate solution to the localization problem.