Anand Oka

Rateless coding for hybrid free-space optical and radio-frequency communication

Free-space optical (FSO) transmission systems enable high-speed communication with relatively small deployment costs. However, FSO suffers a critical disadvantage, namely susceptibility to fog, smoke, and conditions alike. A possible solution to this dilemma is the use of hybrid systems employing FSO and radio frequency (RF) transmission. In this paper we propose the application of a rateless coded automatic repeatrequest scheme for such hybrid FSO/RF systems. The advantages of our approach are (a) the full utilization of available FSO and RF channel resources at any time, regardless of FSO or RF channel conditions and temporal variations, and (b) no need for a-priori rate selection at the transmitter. In order to substantiate these claims, we establish the pertinent capacity limits for hybrid FSO/RF transmission and present simulation results for transmission with off-the-shelf Raptor codes, which achieve realized rates close to these limits under a wide range of channel conditions. We also show that in conditions of strong atmospheric turbulence, rateless coding is advantageous over fixed-rate coding with rate adaptation at the transmitter.

Full length article: A compressed sensing receiver for UWB impulse radio in bursty applications like wireless sensor networks

We propose a novel receiver for Ultra-Wide-band Impulse-Radio communication in Wireless Sensor Networks, which are characterized by bursty traffic and severe power constraints. The receiver is based on the principle of Compressed Sensing, and exploits the sparsity of the transmitted signal to achieve reliable demodulation from a relatively small number of projections. The projections are implemented in an analog front-end as correlations with tractable test-functions, and a joint decoding of the time of arrival and the data bits is done by a DSP back-end using an efficient quadratic program. The proposed receiver differs from extant schemes in the following respects: (i) It needs neither a high-rate analog-to-digital converter nor wide-band analog delay lines, and can operate in a significantly under-sampled regime. (ii) It is robust to large timing uncertainty and hence the transmitter need not waster power on explicit training headers for timing synchronization. (iii) It can operate in a regime of heavy inter-symbol interference (ISI), and therefore allows a very high baud rate (close to the Nyquist rate). (iv) It has a built-in capability to blindly acquire and track the channel response irrespective of line-of-sight/non-line-of-sight conditions. We demonstrate that the receivers performance remains close to the maximum likelihood receiver under every scenario of under-sampling, timing uncertainty, ISI, and channel delay spread.

Decoding with Early Termination for Raptor Codes

Rateless codes, and especially Raptor codes, have received considerable attention in the recent past due to their inherent ability to adapt to channel conditions and their capacity- approaching performance. Since decoding of rateless codes typically involves multiple decoding attempts, early termination of such attempts is mandatory for overall efficient decoding. In this letter, we propose a new decoding scheme with early termination that is particularly suited for rateless codes. Simulation results for the example of the binary symmetric channel show complexity reductions (in terms of the total required number of decoding iterations) by 87% compared to conventional message-passing decoding and 54% compared to a recently proposed incremental decoding scheme for Raptor codes.

A comparison between Unscented Kalman Filtering and particle filtering for RSSI-based tracking

The task of tracking targets carrying active radio-frequency identification (RFID) tags based on the received signal strength indication (RSSI) values of tag transmissions is a classical Bayesian filtering problem. Since the problem is nonlinear, no closed-form solution is known and tractable approximations must be used. Unscented Kalman Filtering (UKF) and Particle Filtering (PF) are two leading candidates proposed in literature. However, a head-to-head comparison of the two is currently unavailable. In this paper, we address this issue by comparing and contrasting these two tracking techniques in terms of their tracking accuracies and consistencies in various scenarios. Based on extensive simulation results as well as real-life experimental data, we conclude that the UKF significantly underperforms relative to the PF in two realistic scenarios: (i) when there are significant co-dependencies in the motion of the targets, and (ii) when a diverse radio environment affects the propagation characteristics of the tag transmissions (like occlusions, multipath and shadowing). The second situation is especially significant because it implies that the success of the UKF is contingent on a free-space like environment. Therefore, it is not a robust solution in practice.

Data extraction from wireless sensor networks using distributed fountain codes

A Wireless Sensor Network (WSN) observes a natural field and aims to recreate it with sufficient fidelity at a, perhaps distant, Fusion Center (FC) using a wireless communication channel of arbitrary capacity. We propose a universal and power efficient method for such data extraction, based on Digital Fountain Codes (DFCs) and joint-source channel decoding. Our method implements a distributed `rate-lessiquest DFC which automatically tunes the number of transmissions to the channel capacity. Furthermore, instead of directly compressing the WSN data, we achieve rate reduction by treating the spatiotemporal dependencies in the field as an outer code, and jointly decoding this concatenation at the FC using a multi-stage iterative decoder. We demonstrate that a power efficiency close to the capacity-rate-distortion limit is achieved at moderate distortion levels, irrespective of the channel capacity or field dependencies. As compared to the traditional approach of source-channel separation, the proposed data extraction scheme is particularly attractive for WSN applications due its computationally simple encoding procedure, low latency and the ability to seamlessly trade-off fidelity of reconstruction for power consumption.

Energy Efficient Distributed Filtering With Wireless Sensor Networks

We consider a wireless sensor network (WSN) that monitors a physical field and communicates pertinent data to a distant fusion center (FC). We study the case of a binary valued hidden natural field observed in a significant amount of Gaussian clutter, which is relevant to applications like detection of plumes or oil slicks. The considerable spatio-temporal dependencies found in natural fields can be exploited to improve the reliability of the detection/estimation of hidden phenomena. While this problem has been previously treated using kernel-regression techniques, we formulate it as a task of delay-free filtering on a process observed by the sensors. We propose a distributed scalable implementation of the filter within the network. This is achieved by i) exploiting the localized spatio-temporal dependencies to define a hidden Markov model (HMM) in terms of an exponential family with O(N) parameters, where N is the size of the WSN, ii) using a reduced- state approximation of the propagated probability mass function, and iii) making a tractable approximation of model marginals by using iterated decoding algorithms like the Gibbs sampler (GS), mean field decoding (MFD), iterated conditional modes (ICM), and broadcast belief propagation (BBP). We compare the marginalization algorithms in terms of their information geometry, performance, complexity and communication load. Finally, we analyze the energy efficiency of the proposed distributed filter relative to brute force data fusion. It is demonstrated that when the FC is sufficiently far away from the sensor array, distributed filtering is significantly more energy efficient and can increase the lifetime of the WSN by one to two orders of magnitude.

A compressed sensing receiver for bursty communication with UWB Impulse Radio

We propose a novel receiver for Ultra-Wideband Impulse-Radio communication in bursty applications like Wireless Sensor Networks. It is based on the principle of Compressed Sensing, and exploits the sparsity of the transmitted signal to achieve reliable demodulation. Instead of a full-fledged high-rate A/D, a modest number of projections of the received signal are acquired using analog correlators, and a joint decoding of the time of arrival and the data bits is performed from these under-sampled measurements via an efficient quadratic program. The receiver does not use wideband analog delay lines, and is robust to large timing uncertainty, hence the transmitter need not waste power on explicit training headers for timing synchronization. Moreover, the receiver can operate in a regime of heavy inter-symbol interference (ISI), and allows a very high baud rate (close to the Nyquist rate). Its performance is shown to remain close to the maximum likelihood receiver under every scenario of under-sampling, timing uncertainty, ISI, and delay spread.

Incremental Distributed Identification of Markov Random Field Models in Wireless Sensor Networks

Wireless sensor networks (WSNs) comprise of highly power constrained nodes that observe a hidden natural field and reconstruct it at a distant data fusion center. Algorithmic strategies for extending the lifetime of such networks invariably require a knowledge of the statistical model of the underlying field. Since centralized model identification is communication intensive and eats into any potential power savings, we present a stochastic recursive identification algorithm which can be implemented in a fully distributed and scalable manner within the network. We demonstrate that it consumes modest resources relative to centralized estimation, and is stable, unbiased, and asymptotically efficient.

Decoding with Early Termination for Rateless (Luby Transform) Codes

Fountain codes have recently gained wide attention in communications due to their capacity-approaching performance and rateless properties that allow them to seamlessly adapt to unknown channel statistics. In this paper, we consider the problem of low complexity decoding of Luby transform codes, which are a class of linear fountain codes. We adapt the recently proposed technique of informed dynamic scheduling to the rateless regime, and combine it with the method of incremental decoding to obtain a decoder that has a significantly reduced computational load compared to the commonly used alternative of message-reset decoding with a flooding schedule. This reduction in complexity, in some cases as large as a factor of sixty, is obtained without affecting the error rate performance of the code.

Model Identification for Wireless Sensor Networks

In many lifetime enhancement strategies for wireless sensor networks (WSNs) it is often necessary to identify the statistical model of the underlying physical field. We consider the problem of in-situ inference as an exemplary application and propose an in-situ model estimation algorithm that works in tandem with a parametric distributed filtering procedure. We demonstrate, via averaged-gradient analysis and simulations, that the resulting adaptive filter is stable, robust and, importantly, fully scalable. It compares favorably with kernel-regression inference, and typically significantly outperforms the latter when the spatio-temporal variations in the natural field are relatively rapid.