Abstract — In wireless cellular or ad hoc networks where Quality of Service (QoS) is interference-limited, a variety of power control problems can be formulated as nonlinear optimization with a system-wide objective, e.g., maximizing the total system throughput or the worst user throughput, subject to QoS constraints from individual users, e.g., on data rate, delay, and outage probability. We show that in the high Signal-to-Interference Ratios (SIR) regime, these nonlinear and apparently difficult, nonconvex optimization problems can be transformed into convex optimization problems in the form of geometric programming; hence they can be very efficiently solved for global optimality even with a large number of users. In the medium to low SIR regime, some of these constrained nonlinear optimization of power control cannot be turned into tractable convex formulations, but a heuristic can be used to compute in most cases the optimal solution by solving a series of geometric programs through the approach of successive convex approximation. While efficient and robust algorithms have been extensively studied for centralized solutions of geometric programs, distributed algorithms have not been explored before. We present a systematic method of distributed algorithms for power control that is geometric-programming-based. These techniques for power control, together with their implications to admission control and pricing in wireless networks, are illustrated through several numerical examples. Index Terms — Convex optimization, CDMA power control, Distributed algorithms. I.

ABSTRACT | Over the coming decades, high-definition situationally-aware networks have the potential to create revolutionary applications in the social, scientific, commercial, and military sectors. Ultrawide bandwidth (UWB) technology is a viable candidate for enabling accurate localization capabilities through time-of-arrival (TOA)-based ranging techniques. These techniques exploit the fine delay resolution property of UWB signals by estimating the TOA of the first signal path. Exploiting the full capabilities of UWB TOA estimation can be challenging, especially when operating in harsh propagation environments, since the direct path may not exist or it may not be the strongest. In this paper, we first give an overview of ranging techniques together with the primary sources of TOA error (including propagation effects, clock drift, and interference). We then describe fundamental TOA bounds (such as the Cramér-Rao bound and the tighter Ziv-Zakai bound) in both ideal and multipath environments. These bounds serve as useful benchmarks in assessing the performance of TOA estimation techniques. We also explore practical low-complexity TOA estimation techniques and analyze their performance in the presence of multipath and interference using IEEE 802.15.4a channel models as well as experimental data measured in indoor residential environments.

Abstract. Recently, analytical approaches based on the eigenfunctions of spatial configuration matrices have been proposed in order to consider explicitly spatial predictors. The present study demonstrates the usefulness of eigenfunctions in spatial modeling applied to ecological problems and shows equivalencies of and differences between the two current implementations of this methodology. The two approaches in this category are the distancebased (DB) eigenvector maps proposed by P. Legendre and his colleagues, and spatial filtering based upon geographic connectivity matrices (i.e., topology-based; CB) developed by D. A. Griffith and his colleagues. In both cases, the goal is to create spatial predictors that can be easily incorporated into conventional regression models. One important advantage of these two approaches over any other spatial approach is that they provide a flexible tool that allows the full range of general and generalized linear modeling theory to be applied to ecological and geographical problems in the presence of nonzero spatial autocorrelation.

For the first time, we present modeling of microwave circuits using artificial neural networks (ANN's) based on space-mapping (SM) technology. SM-based neuromodels decrease the cost of training, improve generalization ability, and reduce the complexity of the ANN topology with respect to the classical neuromodeling approach. Five creative techniques are proposed to generate SM-based neuromodels. A frequency-sensitive neuromapping is applied to overcome the limitations of empirical models developed under quasi-static conditions. Huber optimization is used to train the ANN's. We contrast SM-based neuromodeling with the classical neuromodeling approach as well as with other state-of-the-art neuromodeling techniques. The SMbased neuromodeling techniques are illustrated by a microstrip bend and a high-temperature superconducting filter.

this paper, we present a method to extend the results in [23] and [22] to multi-class optical networks. While multi-class circuit-switched networks have been studied in the literature [25], to the best of our knowledge, this is the first time that multi-class wavelength routing networks are analyzed

The long-standing analog-to-digital conversion paradigm based on Shannon/Nyquist sampling has been challenged lately, mostly in situations such as radar and communication signal processing where signal bandwidth is so large that sampling architectures constraints are simply not manageable. Compressed sensing (CS) is a new emerging signal acquisition/compression paradigm that offers a striking alternative to traditional signal acquisition. Interestingly, by merging the sampling and compression steps, CS also removes a large part of the digital architecture and might thus considerably simplify analog-to-information (A2I) conversion devices. This so-called “analog CS,” where compression occurs directly in the analog sensor readout electronics prior to analog-to-digital conversion, could thus be of great importance for applications where bandwidth is moderate, but computationally complex, and power resources are severely constrained. In our

Abstract—We propose to model the image differentials of astrophysical source maps by Student’s t-distribution and to use them in the Bayesian source separation method as priors. We introduce an efficient Markov Chain Monte Carlo (MCMC) sampling scheme to unmix the astrophysical sources and describe the derivation details. In this scheme, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and reduces the computation time significantly (by two orders of magnitude). In addition, Student’s t-distribution parameters are updated throughout the iterations. The results on astrophysical source separation are assessed with two performance criteria defined in the pixel and the frequency domains. Index Terms—Astrophysical images, Bayesian source separation,

Abstract—In a multihop wireless network environment, it has been shown that IEEE 802.11 media access control (MAC) suf-fers from a low-throughput problem, which is largely due to the inefficiency in carrier sensing and spatial reuse. In this paper, we present a location-assisted MAC protocol that schedules “feasible” concurrent transmissions in a multihop wireless network. A simple procedure based on location information is adopted in the pro-posed MAC to validate the feasibility of a concurrent transmission. Our simulation results show that the proposed scheme can effec-tively increase the throughput and reduce the average end-to-end delay of multihop wireless networks. Index Terms—Concurrent transmissions, exposed terminal, me-dia access control (MAC), multihop wireless networks. I.

Abstract 1 — In this paper we study the problem of using the rate adaptation technique to achieve energy efficiency in an IEEE 802.11-based multi-hop network. Specifically, we formulate it as an optimization problem, i.e., minimizing the total transmission power over transmission data rates, subject to the traffic requirements of all the nodes in a multi-hop network. Interestingly, we can show that this problem is actually a well-known multiple-choice knapsack problem, which is proven to be an NP-hard problem. So, instead of finding an optimal solution, which is NP-hard, we seek a sub-optimal solution. Our key technique to attack this problem is distributed cooperative rate adaptation. Here, we promote node cooperation due to our observation that the inequality in non-cooperative channel contention among nodes caused by hidden terminal phenomenon in a multi-hop network tends to result in energy inefficiency. Under this design philosophy, we propose a distributed cooperative rate adaptation (CRA) scheme and prove that it converges. Simulation results show that our CRA scheme can reduce the power consumption up to 86 % as compared to the existing (non-cooperative) algorithm. Keywords-rate adaptation; energy efficiency; IEEE 802.11; cooperation; wireless multi-hop network 1 This work is performed when Kun Wang is a visiting student in Microsoft Research Asia. 1 I.

Abstract. Frequent spotbeam handovers in low earth orbit (LEO) satellite networks require a technique to decrease the handover blocking probabilities. A large variety of schemes have been proposed to achieve this goal in terrestrial mobile cellular networks. Most of them focus on the notion of prioritized channel allocation algorithms. However, these schemes cannot provide the connection-level quality of service (QoS) guarantees. Due to the scarcity of resources in LEO satellite networks, a connection admission control (CAC) technique becomes important to achieve this connection-level QoS for the spotbeam handovers. In this paper, a geographical connection admission control (GCAC) algorithm is introduced, which estimates the future handover blocking performance of a new call attempt based on the user location database, in order to decrease the handover blocking. Also, for its channel allocation scheme, an adaptive dynamic channel allocation (ADCA) scheme is introduced. By simulation, it is shown that the proposed GCAC with ADCA scheme guarantees the handover blocking probability to a predefined target level of QoS. Since GCAC algorithm utilizes the user location information, performance evaluation indicates that the quality of service (QoS) is also guaranteed in the non-uniform traffic pattern.