125,017 research outputs found
A Polyphase Multipath Technique for Software-Defined Radio Transmitters
Transmitter circuits using large signal swings and hard-switched mixers are power-efficient, but also produce unwanted harmonics and sidebands, which are commonly removed using dedicated filters. This paper presents a polyphase multipath technique to relax or eliminate filters by canceling a multitude of harmonics and sidebands. Using this technique, a wideband and flexible power upconverter with a clean output spectrum is realized in 0.13-mum CMOS, aiming at a software-defined radio application. Prototype chips operate from DC to 2.4 GHz with spurs smaller than -40 dBc up to the 17th harmonic (18-path mode) or 5th harmonic (6-path mode) of the transmit frequency, without tuning or calibration. The transmitter delivers 8 mW of power to a 100-Omega load (2.54 Vpp-diff voltage swing) and the complete chip consumes 228 mW from a 1.2-V supply. It uses no filters, but only digital circuits and mixer
Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions
The Internet of Things (IoT) is a collection of Internet connected devices
capable of interacting with the physical world and computer systems. It is
estimated that the IoT will consist of approximately fifty billion devices by
the year 2020. In addition to the sheer numbers, the need for IoT security is
exacerbated by the fact that many of the edge devices employ weak to no
encryption of the communication link. It has been estimated that almost 70% of
IoT devices use no form of encryption. Previous research has suggested the use
of Specific Emitter Identification (SEI), a physical layer technique, as a
means of augmenting bit-level security mechanism such as encryption. The work
presented here integrates a Nelder-Mead based approach for estimating the
Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA
fingerprinting. The performance of this estimator is assessed for degrading
signal-to-noise ratio and compared with least square and minimum mean squared
error channel estimators. Additionally, this work presents classification
results using RF-DNA fingerprints that were extracted from received signals
that have undergone Rayleigh fading channel correction using Minimum Mean
Squared Error (MMSE) equalization. This work also performs radio discrimination
using RF-DNA fingerprints generated from the normalized magnitude-squared and
phase response of Gabor coefficients as well as two classifiers. Discrimination
of four 802.11a Wi-Fi radios achieves an average percent correct classification
of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a
Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE
Transactions on Information Forensics and Securit
Robust Localization from Incomplete Local Information
We consider the problem of localizing wireless devices in an ad-hoc network
embedded in a d-dimensional Euclidean space. Obtaining a good estimation of
where wireless devices are located is crucial in wireless network applications
including environment monitoring, geographic routing and topology control. When
the positions of the devices are unknown and only local distance information is
given, we need to infer the positions from these local distance measurements.
This problem is particularly challenging when we only have access to
measurements that have limited accuracy and are incomplete. We consider the
extreme case of this limitation on the available information, namely only the
connectivity information is available, i.e., we only know whether a pair of
nodes is within a fixed detection range of each other or not, and no
information is known about how far apart they are. Further, to account for
detection failures, we assume that even if a pair of devices is within the
detection range, it fails to detect the presence of one another with some
probability and this probability of failure depends on how far apart those
devices are. Given this limited information, we investigate the performance of
a centralized positioning algorithm MDS-MAP introduced by Shang et al., and a
distributed positioning algorithm, introduced by Savarese et al., called
HOP-TERRAIN. In particular, for a network consisting of n devices positioned
randomly, we provide a bound on the resulting error for both algorithms. We
show that the error is bounded, decreasing at a rate that is proportional to
R/Rc, where Rc is the critical detection range when the resulting random
network starts to be connected, and R is the detection range of each device.Comment: 40 pages, 13 figure
A general framework for coloring problems: old results, new results, and open problems
In this survey paper we present a general framework for coloring problems that was introduced in a joint paper which the author presented at WG2003. We show how a number of different types of coloring problems, most of which have been motivated from frequency assignment, fit into this framework. We give a survey of the existing results, mainly based on and strongly biased by joint work of the author with several different groups of coauthors, include some new results, and discuss several open problems for each of the variants
Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels
The paper investigates adaptive equalization of time dispersive mobile ratio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE
Impact of Obstacles on the Degree of Mobile Ad Hoc Connection Graphs
What is the impact of obstacles on the graphs of connections between stations
in Mobile Ad hoc Networks? In order to answer, at least partially, this
question, the first step is to define both an environment with obstacles and a
mobility model for the stations in such an environment. The present paper
focuses on a new way of considering the mobility within environments with
obstacles, while keeping the core ideas of the well-known Random WayPoint
mobility model (a.k.a RWP). Based on a mesh-partitioning of the space, we
propose a new model called RSP-O-G for which we compute the spatial
distribution of stations and analyse how the presence of obstacles impacts this
distribution compared to the distribution when no obstacles are present.
Coupled with a simple model of radio propagation, and according to the density
of stations in the environment, we study the mean degree of the connection
graphs corresponding to such mobile ad hoc networks
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