Wesley M. Gifford

Network localization and navigation via cooperation

Network localization and navigation give rise to a new paradigm for communications and contextual data collection, enabling a variety of new applications that rely on position information of mobile nodes (agents). The performance of such networks can be significantly improved via the use of cooperation. Therefore, a deep understanding of information exchange and cooperation in the network is crucial for the design of location-aware networks. This article presents an exploration of cooperative network localization and navigation from a theoretical foundation to applications, covering technologies and spatiotemporal cooperative algorithms.

NLOS identification and mitigation for localization based on UWB experimental data

Sensor networks can benefit greatly from location-awareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resolution, and robust operation in harsh environments. However, the presence of walls and other obstacles presents a significant challenge in terms of localization, as they can result in positively biased distance estimates. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. From these channel pulse responses, we extract features that are representative of the propagation conditions. We then develop classification and regression algorithms based on machine learning techniques, which are capable of: (i) assessing whether a signal was transmitted in LOS or NLOS conditions; and (ii) reducing ranging error caused by NLOS conditions. We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques. In contrast to common probabilistic approaches that require statistical models of the features, the proposed optimization-based approach is more robust against modeling errors.

Diversity with practical channel estimation

In this paper, we present a framework for evaluating the bit error probability of N/sub d/-branch diversity combining in the presence of non-ideal channel estimates. The estimator structure presented is based on the maximum-likelihood (ML) estimate and arises naturally as the sample mean of N/sub p/ pilot symbols. The framework presented requires only the evaluation of a single integral involving the moment generating function of the norm square of the channel-gain vector, and is applicable to channels with arbitrary distribution, including correlated fading. Our analytical results show that the practical ML channel estimator preserves the diversity order of an N/sub d/-branch diversity system, contrary to conclusions in the literature based upon a model that assumes a fixed correlation between the channel and its estimate. Finally, we investigate the asymptotic signal-to-noise ratio penalty due to estimation error and reveal a surprising lack of dependence on the number of diversity branches.

A Machine Learning Approach to Ranging Error Mitigation for UWB Localization

Location-awareness is becoming increasingly important in wireless networks. Indoor localization can be enabled through wideband or ultra-wide bandwidth (UWB) transmission, due to its fine delay resolution and obstacle-penetration capabilities. A major hurdle is the presence of obstacles that block the line-of-sight (LOS) path between devices, affecting ranging performance and, in turn, localization accuracy. Many techniques have been proposed to address this issue, most of which make modifications to the localization algorithm. Since many localization algorithms work with distance or angle estimates, rather than received waveforms, information inherent in the wideband waveform is lost, leading to sub-optimal ranging error mitigation. To avoid this information loss, we present a novel approach to mitigate ranging errors directly in the physical layer. In contrast to existing techniques, which detect the non-line-of-sight (NLOS) condition, our approach directly mitigates the bias incurred in both LOS and non-LOS conditions. In particular, we apply two classes of non-parametric regressors to form an estimate of the ranging error. Our work is based on, and validated by, an extensive indoor measurement campaign with FCC-compliant UWB radios. The results show that the proposed regressors provide significant performance improvements in various practical localization scenarios, compared to conventional approaches.

Optimized simple bounds for diversity systems

Diversity techniques play a key role in modern wireless systems, whose design benefits from a clear understanding of how these techniques affect system performance. To this aim we propose a simple class of bounds, whose parameters are optimized, on the symbol error probability (SEP) for detection of arbitrary two-dimensional signaling constellations with diversity in the presence of non-ideal channel estimation. Unlike known bounds, the optimized simple bounds are tight for all signal-to-noise ratios (SNRs) of interest. In addition, these bounds are easily invertible, which enables us to obtain bounds on the symbol error outage (SEO) and SNR penalty. As example applications for digital mobile radio, we consider the SEO in log-normal shadowing and the SNR penalty for both maximal ratio diversity, in the case of unequal branch power profile, and subset diversity, in the case of equal branch power profile, with non-ideal channel estimation. The reported lower and upper bounds are extremely tight, that is, within a fraction of a dB from each other.

Antenna subset diversity with non-ideal channel estimation

In modern wireless systems employing diversity techniques, combining all the available diversity branches may not be feasible due to complexity and resource constraints. To alleviate these issues, subset diversity (SSD) systems have been proposed. Here, we develop a framework for evaluating the symbol error probability for antenna SSD, where the signals from a subset of antenna elements are selected and combined in the presence of channel estimation error. We consider independent identically distributed Rayleigh fading channels and use an estimator structure based on the maximum likelihood (ML) estimate which arises naturally as the sample mean of Np pilot symbols. The analysis is valid for arbitrary two-dimensional signaling constellations. The expressions give insight into the performance losses of non-ideal SSD when compared to ideal SSD. Due to estimation error, these losses occur in branch combining as well as in branch selection. However, our analytical results show that the practical ML channel estimator still preserves the diversity order of an ideal SSD system with Nd branches. Finally, we investigate the asymptotic signal-to-noise ratio penalty due to estimation error.

On transmitted-reference UWB communications

In this paper we present a general framework for evaluating the exact performance of transmitted-reference (TR) signaling systems. Due to its simplicity, there has been a renewed interest in TR signaling for ultra-wide bandwidth (UWB) systems. This scheme has the benefit that it is robust in fast varying, highly selective fading channels, provided that the data and reference separation is on the order of the channel coherence. Using the framework provided, we evaluate the performance of UWB TR systems operating in arbitrary fading channels.

Nonparametric Obstruction Detection for UWB Localization

Ultra-wide bandwidth (UWB) transmission is a promising technology for indoor localization due to its fine delay resolution and obstacle-penetration capabilities. However, the presence of walls and other obstacles introduces a positive bias in distance estimates, severely degrading localization accuracy. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. Based on this campaign, we extract key features that allow us to distinguish between NLOS and LOS conditions. We then propose a nonparametric approach based on support vector machines for NLOS identification, and compare it with existing parametric (i.e., model-based) approaches. Finally, we evaluate the impact on localization through Monte Carlo simulation. Our results show that it is possible to improve positioning accuracy relying solely on the received UWB signal.

Towards a Formal Model for Optimal Task-Site Allocation and Effort Estimation in Global Software Development

Motivated by the desire to cut costs and development effort, software organizations have increasingly adopted a global development approach. However, the cost savings, if any, from this globalization, is often offset by hidden costs such as handoffs between sites, synchronization of development among sites, integration of software developed at distributed sites, language/cultural issues, travel costs, communication costs, etc. Although several empirical studies have been conducted on this issue, due to the lack of an integrated formal model, such studies have not produced consistent and usable results. To that end, in this paper, we present an integrated formal model for analyzing global software development. Our model comprises two parts. First, we consider all tasks in a software project that can be geographically distributed, and the possible sites where they can be allocated. We develop an optimal task-site allocation model. Our approach then generates an effort estimate for the new allocation, which is based on the following factors: expected general percentage allocation of overall effort estimate to each task in the development lifecycle, and effort estimate for executing a task at a particular site (in terms of the effort estimate for executing the same task at the home site, viz., without globalization). The final effort estimate is therefore derived as a function of the effort estimate for executing the overall software project in the home site; this estimate provides project managers with a more accurate understanding of expected cost savings from globalization, if any. Throughout our paper, we illustrate our approach using a real global software development project at IBM as a running example.

Diversity combining with imperfect channel estimates in correlated fading

In this paper, we present a framework for evaluating the bit error probability (BEP) of N d-branch diversity combining with imperfect channel estimates in correlated fading environments. The estimator structure is based on the maximum likelihood estimate and arises naturally as the sample mean of N p pilot symbols. Our results show that the diversity order of the system under consideration matches that of an ideal system operating in the same correlated fading environment, regardless of the number of pilot symbols.