Cramér-Rao, Bound for Hybrid Peer-to-Peer Positioning

Presented at IEEE Communication Theory Workshop (CTW), Cancun, Mexico, May 10-12, 2010. PROCEEDINGS ARE NOT AVAILABL

Cramér-Rao Bound for Hybrid GNSS-Terrestrial Cooperative Positioning

In this contribution we derive an expression of the Cramer-Rao bound for hybrid cooperative positioning, where GNSS information is combined with terrestrial range measurements through exchange of peer-to-peer messages. These results provide a theoretical characterization of achievable performance of hybrid positioning schemes, as well as allow to identify critical network configurations and devise optimized node placement strategies

Upper bounds on position error of a single location estimate in wireless sensor networks

This paper studies upper bounds on the position error for a single estimate of an unknown target node position based on distance estimates in wireless sensor networks. In this study, we investigate a number of approaches to confine the target node position to bounded sets for different scenarios. Firstly, if at least one distance estimate error is positive, we derive a simple, but potentially loose upper bound, which is always valid. In addition assuming that the probability density of measurement noise is nonzero for positive values and a sufficiently large number of distance estimates are available, we propose an upper bound, which is valid with high probability. Secondly, if a reasonable lower bound on negative measurement errors is known a priori, we manipulate the distance estimates to obtain a new set with positive measurement errors. In general, we formulate bounds as nonconvex optimization problems. To solve the problems, we employ a relaxation technique and obtain semidefinite programs. We also propose a simple approach to find the bounds in closed forms. Simulation results show reasonable tightness for different bounds in various situations. © 2014 Gholami et al.; licensee Springer

Cramér-Rao, Bound for Hybrid Peer-to-Peer Positioning

Presented at IEEE Communication Theory Workshop (CTW), Cancun, Mexico, May 10-12, 2010. PROCEEDINGS ARE NOT AVAILABLE

Network traffic flow optimization under performance constraints

In this paper, a model-based perimeter control policy for large-scale urban vehicular networks is proposed. Assuming a homogeneously loaded vehicle network and the existence of a well-posed Network Fundamental Diagram (NFD), we describe a protected network throughout its aggregated dynamics including nonlinear exit flow characteristics. Within this framework of constrained optimal boundary flow gating, two main performance metrics are considered: (a) first, connected to the NFD, the concept of average network travel time and delay as a performance metric is defined; (b) second, at boundaries, we take into account additional external network queue dynamics governed by uncontrolled inflow demands. External queue capacities in terms of finite-link lengths are used as the second performance metric. Hence, the corresponding performance requirement is an upper bound of external queues. While external queues represent vehicles waiting to enter the protected network, internal queue describes the protected network’s aggregated behavior. By controlling the number of vehicles joining the internal queue from the external ones, herewith a network traffic flow maximization solution subject to the internal and external dynamics and their performance constraints is developed. The originally non-convex optimization problem is transformed to a numerically efficiently convex one by relaxing the performance constraints into time-dependent state boundaries. The control solution can be interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed performance requirements on travel time in the network and upper bound on the external queue are satisfied. Comparative numerical simulation studies on a microscopic traffic simulator are carried out to show the benefits of the proposed method

5G mmwave positioning for vehicular networks

5G technologies present a new paradigm to provide connectivity to vehicles, in support of high data-rate services, complementing existing inter-vehicle communication standards based on IEEE 802.11p. As we argue, the specific signal characteristics of 5G communication turn out to be highly conducive for vehicle positioning. Hence, 5G can work in synergy with existing on-vehicle positioning and mapping systems to provide redundancy for certain applications, in particular automated driving. This article provides an overview of the evolution of cellular positioning and discusses the key properties of 5G as they relate to vehicular positioning. Open research challenges are presented

Learning Physical-Layer Communication with Quantized Feedback

Data-driven optimization of transmitters and receivers can reveal new modulation and detection schemes and enable physical-layer communication over unknown channels. Previous work has shown that practical implementations of this approach require a feedback signal from the receiver to the transmitter. In this paper, we study the impact of quantized feedback on data-driven learning of physical-layer communication. A novel quantization method is proposed, which exploits the specific properties of the feedback signal and is suitable for nonstationary signal distributions. The method is evaluated for linear and nonlinear channels. Simulation results show that feedback quantization does not appreciably affect the learning process and can lead to similar performance as compared to the case where unquantized feedback is used for training, even with 1-bit quantization. In addition, it is shown that learning is surprisingly robust to noisy feedback where random bit flips are applied to the quantization bits