Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation

Radio signals are used broadly as navigation aids, and current and future terrestrial wireless communication systems have properties that make their dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable widespread coverage for data communication and navigation, but typically offer smaller bandwidths and limited resolution for precise estimation of geometries, particularly in environments where propagation channels are diffuse in time and/or space. Non-parametric methods have been employed with some success for such scenarios both commercially and in literature, but often with an emphasis on low-cost hardware and simple models of propagation, or with simulations that do not fully capture hardware impairments and complex propagation mechanisms. In this article, we make opportunistic observations of downlink signals transmitted by commercial cellular networks by using a software-defined radio and massive antenna array mounted on a passenger vehicle in an urban non line-of-sight scenario, together with a ground truth reference for vehicle pose. With these observations as inputs, we employ artificial neural networks to generate estimates of vehicle location and heading for various artificial neural network architectures and different representations of the input observation data, which we call wiometrics, and compare the performance for navigation. Position accuracy on the order of a few meters, and heading accuracy of a few degrees, are achieved for the best-performing combinations of networks and wiometrics. Based on the results of the experiments we draw conclusions regarding possible future directions for wireless navigation using statistical methods

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

Localization Techniques in Multiple-Input Multiple-Output Communication: Fundamental Principles, Challenges, and Opportunities

This chapter provides an overview of localization techniques in Multiple-Input Multiple-Output (MIMO) communication systems. The chapter mainly focuses on sub-6 GHz and mmWave bands. MIMO technology enables high-capacity wireless communication, but also presents challenges for localization due to the complexity of the signal propagation environment. Various methods have been developed to overcome these challenges, which utilize side information such as the map of the area, or techniques such as Compressive Sensing (CS), Deep Learning (DL), Gaussian Process Regression (GPR), or clustering. These techniques utilize wireless communication parameters such as Received Signal Strength Indicator (RSSI), Channel State Information (CSI), Angle-Delay-Profile (ADP), Angle-of-Departure (AoD), Angle-of-Arrival (AoA), or Time-of-Arrival (ToA) as inputs to estimate the user’s location. The goal of this chapter is to offer a comprehensive understanding of MIMO localization techniques, along with an overview of the challenges and opportunities associated with them. Furthermore, it also aims to provide the theoretical background on channel models and wireless channel parameters required to understand the localization techniques

From Data Inferring to Physics Representing: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE

In this paper, we propose an innovative learning-based channel prediction scheme so as to achieve higher prediction accuracy and reduce the requirements of huge amount and strict sequential format of channel data. Inspired by the idea of the neural ordinary differential equation (Neural ODE), we first prove that the channel prediction problem can be modeled as an ODE problem with a known initial value through analyzing the physical process of electromagnetic wave propagation within a varying space. Then, we design a novel physics-inspired spatial channel gradient network (SCGNet), which represents the derivative process of channel varying as a special neural network and can obtain the gradients at any relative displacement needed for the ODE solving. With the SCGNet, the static channel at any location served by the base station is accurately inferred through consecutive propagation and integration. Finally, we design an efficient recurrent positioning algorithm based on some prior knowledge of user mobility to obtain the velocity vector, and propose an approximate Doppler compensation method to make up the instantaneous angular-delay domain channel. Only discrete historical channel data is needed for the training, whereas only a few fresh channel measurements is needed for the prediction, which ensures the scheme's practicability