Estimation In Wireless Networks Research Articles

Coverage and Rate of Joint Communication and Parameter Estimation in Wireless Networks

Coverage and Rate of Joint Communication and Parameter Estimation in Wireless Networks

User Experience Estimation in Multi-Service Scenario of Cellular Network.

The estimation of user experience in a wireless network has always been a research hotspot, especially for the realization of network automation. In order to solve the problem of user experience estimation in wireless networks, we propose a two-step optimization method for the selection of the kernel function and bandwidth in a naive Bayesian classifier based on kernel density estimation. This optimization method can effectively improve the accuracy of estimation. At present, research on user experience estimation in wireless networks does not include an in-depth analysis of the reasons for the decline of user experience. We established a scheme integrating user experience prediction and network fault diagnosis. Key performance indicator (KPI) data collected from an actual network were divided into five categories, which were used to estimate user experience. The results of these five estimates were counted through the voting mechanism, and the final estimation results could be obtained. At the same time, this voting mechanism can also feed back to us which KPIs lead to the reduction of user experience. In addition, this paper also puts forward the evaluation standard of the multi-service perception capability of cell-level wireless networks. We summarize the user experience estimation for three main services in a cell to obtain a cell-level user experience evaluation. The results showed that the proposed method can accurately estimate user experience and diagnosis abnormal values in a timely manner. This method can improve the efficiency of network management.

Overcoming the Channel Estimation Barrier in Massive MIMO Communication via Deep Learning

A new wave of wireless services, including virtual reality, autonomous driving and Internet of Things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive numbers of connections and ultra-low latency. Massive multiple-input multiple-output (MIMO) is one of the critical underlying technologies that allow future wireless networks to meet these service needs. This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular deployment. We develop important insights derived from the physical radio frequency (RF) channel properties and present a comprehensive overview on the application of DL for accurately estimating channel state information (CSI) with low overhead. We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.

Energy-Efficient Channel Estimation

We investigate the energy-efficient channel estimation in wireless networks, where one pilot is inserted for every several data slots to estimate the channel coefficients. Both the channel state information estimation error and the time-varying model are considered. We first formulate the energy-efficient channel estimation problem into a mixed integer nonlinear programming (MINLP) problem, where the variables include the transmitted power and the number of data slots. Due to the NP-hard nature, we degenerate the MINLP problem into a series of non-concave optimization problems without integer variables. Then we solve these problems using successive convex approximation, geometric programming, and the Dinkelbach algorithm to obtain a point satisfying the Karush-Kuhn-Tucker (KKT) conditions. Furthermore, we develop a low-complexity sub-optimal scheme through binary variable relaxation to obtain a solution, and the convergence point satisfies the KKT conditions of the relaxed non-concave problem. Simulation results demonstrate the convergence and effectiveness of our proposed schemes.

Path loss estimation in wireless networks using partial derivative based convex optimisation method

In this paper, we develop a multi-dimensional mathematical optimisation problem for predicting the optimised path loss in general wireless systems under non-linear network operating constraints. The proposed nonconvex optimisation problem is reformulated through convex relaxation programming in order to guarantee global optimal solution. The underlying convexity of the transformed reformulated problem is derived through the application of second-order partial derivatives. Numerical simulation experiments are conducted to compute the propagation path loss and optimal values of system parameters using free space and two-ray ground models. The graphical comparison between the two propagation models and the previously existing wireless channel models is illustrated in terms of various channel performance metrics like optimal path loss, signal-to-noise ratio (SNR), bit error rate (BER), and transmit power consumption. Finally, simulation results are provided to demonstrate the effectiveness of our optimisation model in achieving significantly lower path loss than the previous models based on conventional machine learning systems.

Path loss estimation in wireless networks using partial derivative based convex optimisation method

In this paper, we develop a multi-dimensional mathematical optimisation problem for predicting the optimised path loss in general wireless systems under non-linear network operating constraints. The proposed nonconvex optimisation problem is reformulated through convex relaxation programming in order to guarantee global optimal solution. The underlying convexity of the transformed reformulated problem is derived through the application of second-order partial derivatives. Numerical simulation experiments are conducted to compute the propagation path loss and optimal values of system parameters using free space and two-ray ground models. The graphical comparison between the two propagation models and the previously existing wireless channel models is illustrated in terms of various channel performance metrics like optimal path loss, signal-to-noise ratio (SNR), bit error rate (BER), and transmit power consumption. Finally, simulation results are provided to demonstrate the effectiveness of our optimisation model in achieving significantly lower path loss than the previous models based on conventional machine learning systems.

A computationally efficient approach for hidden-Markov model-augmented fingerprint-based positioning

This paper presents a computationally efficient approach for mobile subscriber position estimation in wireless networks. A method of data scaling assisted by timing adjust is introduced in fingerprint-based location estimation under a framework which allows for minimising computational cost. The proposed method maintains a comparable level of accuracy to the traditional case where no data scaling is used and is evaluated in a simulated environment under varying channel conditions. The proposed scheme is studied when it is augmented by a hidden-Markov model to match the internal parameters to the channel conditions that present, thus minimising computational cost while maximising accuracy. Furthermore, the timing adjust quantity, available in modern wireless signalling messages, is shown to be able to further reduce computational cost and increase accuracy when available. The results may be seen as a significant step towards integrating advanced position-based modelling with power-sensitive mobile devices.

Stochastic Bandwidth Estimation in Networks With Random Service

Numerous methods for available bandwidth estimation have been developed for wireline networks, and their effectiveness is well-documented. However, most methods fail to predict bandwidth availability reliably in a wireless setting. It is accepted that the increased variability of wireless channel conditions makes bandwidth estimation more difficult. However, a (satisfactory) explanation why these methods are failing is missing. This paper seeks to provide insights into the problem of bandwidth estimation in wireless networks or, more broadly, in networks with random service. We express bandwidth availability in terms of bounding functions with a defined violation probability. Exploiting properties of a stochastic min-plus linear system theory, the task of bandwidth estimation is formulated as inferring an unknown bounding function from measurements of probing traffic. We present derivations showing that simply using the expected value of the available bandwidth in networks with random service leads to a systematic overestimation of the traffic departures. Furthermore, we show that in a multihop setting with random service at each node, available bandwidth estimates requires observations over (in principle infinitely) long time periods. We propose a new estimation method for random service that is based on iterative constant-rate probes that take advantage of statistical methods. We show how our estimation method can be realized to achieve both good accuracy and confidence levels. We evaluate our method for wired single-and multihop networks, as well as for wireless networks.

LossEstimate: Distributed failure estimation in wireless networks

The ongoing evolution of software-intensive distributed systems to ultra-large-scale (ULS) systems require innovative methods for building, running, and managing these systems. Component self-adaptation and self-configuration properties are thus becoming mandatory requirements in order to cope with application complexity. An increasing number of systems, such as video content distribution, make use of distributed feed-back mechanisms to build-up intelligent, robust and self-managing services. Technology wise, with the wide-spread usage of wireless communication interfaces on today's mobile devices, communication failures are an ever increasing nuisance in the design of distributed self-adaptive services and applications.Communication protocols designed for wired networks are not suited for this new class of networks (including mobile ad-hoc networks, wireless sensor networks, vehicular ad-hoc networks, etc.) due to the several orders of magnitude higher amount of communication failures. Although virtually every single existing communication protocol tries to deal with the various effects introduced by communication failures, almost all existing state of the art relies on previous knowledge about the amount of errors occurring at run time (information usually collected from previous deployments). A survey of current literature easily shows that, in contrast, applications that make use of distributed feedback mechanisms via online estimation of communication errors has received relatively small attention.In this paper we introduce a new distributed feedback mechanism, named LossEstimate, for runtime quantification of the global amount of communication failures present in a large-scale network. The new algorithm helps building self-adaptive services and has the advantage of being fully distributed – each node computes an estimate of the amount of errors using a gossip-alike approach. The algorithm is adaptive in the sense that it can follow changes in the mean value of the amount of communication failures over time.We focus our analysis on the impact of various network topologies, discussing the case of fully connected networks (relevant for the case of peer-to-peer networks), static multihop topologies (mapping on the case of wireless sensor networks) and mobile multihop networks (mapping on the case of mobile ad-hoc networks and vehicular ad-hoc networks). The results show that the algorithm performs well in all three scenarios, without requiring specific adaptations.Besides the lack of an alternative protocol, the gossip-alike characteristics make LossEstimate an attractive choice for building a distributed feedback mechanism via the online quantification of the amount of communication failures in large-scale networks, due to the fact that it exhibits a small communication overhead and has a small convergence time. It stands as an important building-block for engineering self-adaptive distributed applications and services, such as video streaming, by means of distributed feedback mechanisms.

On the feasibility of bandwidth estimation in wireless access networks

Bandwidth estimation refers to the measurement of a bandwidth-related metric over a network path, performed only by the end hosts, without access to the intermediate routers. The problem of fast bandwidth estimation has been extensively studied in the wired Internet. More recently, researchers have shown that tools developed for the wired Internet cannot be used in wireless networks, due to the different characteristics of these networks which invalidate many of the assumptions made for the wired Internet. This observation has led to new tools that take into account the different characteristics of wireless networks. These tools have only been evaluated in controlled environments, under controlled settings and interferers, and only for 802.11 CSMA-based WLANs. In addition, no tool has been reported so far in the literature for cellular networks. This paper presents the first study of the feasibility of fast bandwidth estimation in wireless networks "in the wild", i.e., in deployed operational networks. We discuss the challenges associated with bandwidth measurements in operational networks in contrast to in controlled lab environments. We study the applicability of a state-of-the-art probe-based tool for 802.11 WLANs in a commercial 1 × EVDO network. In addition, we evaluate the accuracy of the tool in three different WLAN environments, i.e., lab, home, and hotspot. Our results show that bandwidth estimation using a probe-based tool can be challenging in certain WLAN environments, and practically infeasible in EVDO cellular networks, due to the short-scale dynamics in this type of networks.

Investigation of radio channel uncertainty in distance estimation in wireless sensor networks

The distance estimation between nodes is a crucial requirement for localization and object tracking. Received signal strength (RSS) measurement is one of the used methods for the distance estimation in wireless networks. Its main advantage is that there are no additional hardware requirements. This paper describes a lateration approach for localization and distance estimation using RSS. For the purpose of investigation of RSS uncertainty, several scenarios were designed for both indoor and outdoor measurements. The first set of RSS measurement scenarios was proposed with the intention of hardware independent investigation of radio channel. For the second set of measurements, we employed IRIS sensor nodes to evaluate the distance estimation with certain devices. The experiments considered also obstacles in the radio channel. The results obtained in the proposed scenarios present usability of the method under different conditions. There is also a signal propagation model constructed from measured data at a node, which subsequently serves for distance determination.

LOCATION ESTIMATION IN WIRELESS NETWORKS: A BAYESIAN APPROACH

We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that our model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model drastically reduces the requirement for training data as compared with existing approaches.

A data fusion architecture for enhanced position estimation in wireless networks

The problem of position determination of a mobile user in a wireless network has been studied extensively-particularly in the context of military operations and emergency 911 service in cellular networks. The Global Positioning System (GPS) and network-based angle of arrival (AOA), time difference of arrival (TDOA), and time of arrival (TOA) techniques offer solutions to the position estimation problem. However, each method has limitations that prevent its adoption as a universal solution. Data fusion is a collection of methods and techniques for merging disparate types of information. We present a data fusion model for the enhanced accuracy of position estimates within wireless networks. We illustrate our model with several examples and conclude with recommendations for future research directions.