Showing papers by "Dinesh Rajan published in 2012"

Optimal Placement and Configuration of Roadside Units in Vehicular Networks

Abstract: In this paper, we propose a novel optimization framework for Roadside Unit (RSU) deployment and configuration in a vehicular network. We formulate the problem of placement of RSUs and selecting their configurations (e.g. power level, types of antenna and wired/wireless back haul network connectivity) as a linear program. The objective function is to minimize the total cost to deploy and maintain the network of RSU's. A user specified constraint on the minimum coverage provided by the RSU is also incorporated into the optimization framework. Further, the framework also supports the option of specifying selected regions of higher importance such as locations of frequently occurring accidents and incorporating constraints requiring stricter coverage in those areas. Simulation results are presented to demonstrate the feasibility of deployment on the campus map of Southern Methodist University (SMU). The efficiency and scalability of the optimization procedure for large scale problems are also studied and results shows that optimization over an area with the size of Cambridge, Massachusetts is completed in under 2 minutes. Finally, the effects of variation in several key parameters on the resulting design are studied.

Context-aware analysis and adaptation

Abstract: A system and method for providing multi-dimensional context-aware adaptation in vehicular networks is disclosed. The system comprises a collection module, a context resolving module, a parameter determination module and a distribution module. The collection module collects context data describing a context in a communication environment. The context resolving module resolves the context data to a matching historical context and determines one or more historical context groups associated with the matching historical context. The parameter determination module determines a subset of operating parameters from the one or more historical context groups. The distribution module distributes the subset of operating parameters to a network stack communication module.

Multiple input multiple output Dirty paper coding: System design and performance

Abstract: In this paper, a practical multiple input multiple output Dirty paper coding (MIMO-DPC) scheme is designed to cancel the effect of additive interference that is known perfectly to the transmitter. The proposed system uses a trio of encoders — a LDPC code, a vector quantizer implemented as a convolutional decoder and an Orthogonal space time block code (STBC) to achieve temporal coding gain, interference cancelation and spatial diversity, respectively. First, we derive the equivalent noise seen by the receiver using an equivalent lattice based dirty paper code. Then the optimal value of the power inflation factor, which is one of the key system parameters used to minimize the equivalent noise seen by the receiver is derived. Furthermore, we analytically prove that the equivalent noise seen by the receiver tends to 0 for large number of receive antennas. Performance results in the case of various number of receiver antennas are presented and show that significant reduction in bit error probabilities can be obtained over a system that uses no interference cancelation.

Capacity of the Multiple Access Channel in energy harvesting wireless networks

Abstract: In this paper, we obtain the capacity region for the AWGN Multiple Access Channel (MAC) with transmitters equipped with energy harvesters. We next obtain the sum-of-rates capacity for the flat fading Multiple Access Channel with energy harvesting transmitters. We then develop low delay, adaptive transmission strategies that surprisingly achieve near optimal performance under finite battery resource constrained settings. We study the effect of asymmetries in the energy harvesting and characterize the achievable performance.

Space-time super-resolution from multiple-videos

Abstract: In this paper, a new method for reconstructing a video of higher spatial and temporal resolutions from multiple low-resolution video sequences is introduced. The proposed cost function includes a new space-time regularization term based upon Huber-Markov random field (HMRF) model which is convex but non-quadratic. However, this prior can be efficiently and accurately approximated by a quadratic form through an iterative process. This regularization form is a type of variational integral that exploits the piecewise smoothness nature of high-resolution images. We also address in detail the main reason for appearance of the so called “ghosting effect” in the temporal super-resolution reconstruction and explain how it can be resolved.

Design and experimental evaluation of context-aware link-level adaptation

Abstract: Context awareness has received increasing attention with the proliferation of various types of sensors on mobile devices. However, while wireless performance is known to be highly correlated with environmental settings, mobile devices have yet to fully exploit the awareness of context to improve wireless performance. In this paper, we leverage available context information to improve link-level adaptation via decision-tree classifiers and extensively evaluate its performance over emulated channels as well as with in-field trials. We first propose a classification method based on decision trees to select the optimal transmission parameters such as modulation, coding rate and packet size. We then quantify the throughput improvement using the proposed scheme and show that in some scenarios the throughput increases by over 100% compared to traditional SNR-based rate adaptation protocols. Second, we analyze the amount of training to assess the classification scheme. Third, we validate classification-based method by implementation on two different test platforms for extensive experimentation. We reveal the importance of the various contextual attributes used and identify channel type as a key parameter that affects classification performance. Finally, we study and quantify the use of context information across multiple different frequency bands and demonstrate the significant throughput gains that can be obtained.

ASTRA: Application of sequential training to rate adaptation

Abstract: The application of machine learning algorithms in wireless communications has attracted increasing attention due to the promising performance gains recently achieved. Static classification algorithms have been successfully applied to training protocols that adapt transmission parameters according to context information. However, in reality, there are many time-varying reasons for fading channel quality including mobility of sender, receiver, and/or obstacles within the environment. Moreover, time-varying noise further exacerbates the dynamics of the channel. These problems pose new challenges for the application of static classification algorithms in context-aware algorithms and suggest that sequential classifiers which leverage the temporal dynamics and correlation of context information might be more appropriate. In this paper, we apply sequential training to rate adaptation (ASTRA), leveraging the temporal correlation of context information. In particular, linear and non-linear sequential coding schemes are used in the training process for selecting the modulation/coding rate that achieves the highest throughput for the given context. Experimental results on measurements from emulated and in-field channels demonstrate that ASTRA can significantly increase the accuracy of selecting these target rates by up to 175% and increase the resulting throughput by up to 66% over rate adaptation training which uses static classifier-based methods.

New improved system for wwvb broadcast

Abstract: The WWVB broadcast of the time-code signal has undergone no major changes in its communications protocol and modulation scheme since its introduction in 1963. Its amplitude-modulation (AM) and pulse-width based representations of its digital symbols were designed to allow for a simple low-cost realization of a receiver based on envelope detection, widely used with AM audio broadcasting at the time, whereas present day technology allows much more efficient methods for modulation/demodulation to be realized at low cost. Over a decade ago, the station’s power was significantly increased, allowing the broadcast from Colorado to effectively cover most of North America. This has spurred the popularity of radio-controlled clocks and watches, more commonly known as “atomic clocks.” However, electromagnetic interference (EMI) experienced in typical residential and office environments can make it difficult to receive the WWVB signal in various locations, and particularly on the East Coast, where on-frequency interference from the MSF station in the UK is received at relatively high levels. The new protocol and modulation scheme being introduced by NIST effectively addresses these problems, and will enable greatly improved reception of the WWVB broadcast without affecting existing devices. This backward-compatibility is achieved by maintaining the existing AM characteristics while adding various new features through phase modulation (PM). While the new protocol can provide improved timing resolution, it was designed primarily to enhance the system’s robustness by reducing the signal-to-interference-and-noise-ratio (SINR) required for reception, to provide much improved coverage and to enable new applications. Although the demodulation of the signal’s phase involves greater complexity, today’s technology allows for such implementation to be realized in a low-cost integrated circuit. Analyses are presented, including simulated and measured results, to illustrate the challenges encountered in existing receivers. The new modulation scheme and protocol are described and are shown to be effective in addressing these challenges, resulting in several orders of magnitude in performance improvements. U.S. Government work not subject to copyright. 43 rd Annual Precise Time and Time Interval (PTTI) Systems and Applications Meeting

Capacity and modeling of nonlinear fiber optic communications as a frequency-selective fading channel

Abstract: Using a perturbation technique, we propose a new solution to the nonlinear Schrodinger equation (NLSE) governing nonlinear light propagation in the optical fiber. The solution is shown to fit the frequency-selective fading model for the wireless channel. We then use this model to derive new lower-bounds on the capacity of nonlinear fiber optic communications, with and without side information.

Prototype development and field-test results of an adaptive multiresolution PANOPTES imaging architecture.

Abstract: The design, development, and field-test results of a visible-band, folded, multiresolution, adaptive computational imaging system based on the Processing Arrays of Nyquist-limited Observations to Produce a Thin Electro-optic Sensor (PANOPTES) concept is presented. The architectural layout that enables this imager to be adaptive is described, and the control system that ensures reliable field-of-view steering for precision and accuracy in subpixel target registration is explained. A digital superresolution algorithm introduced to obtain high-resolution imagery from field tests conducted in both nighttime and daytime imaging conditions is discussed. The digital superresolution capability of this adaptive PANOPTES architecture is demonstrated via results in which resolution enhancement by a factor of 4 over the detector Nyquist limit is achieved.