Ian J. Wassell

Comparison of empirical propagation path loss models for fixed wireless access systems

Empirical propagation models have found favour in both research and industrial communities owing to their speed of execution and their limited reliance on detailed knowledge of the terrain. Although the study of empirical propagation models for mobile channels has been exhaustive, their applicability for FWA systems is yet to be properly validated. Among the contenders, the ECC-33 model, the Stanford University Interim (SUI) models, and the COST-231 Hata model show the most promise. In this paper, a comprehensive set of propagation measurements taken at 3.5 GHz in Cambridge, UK is used to validate the applicability of the three models mentioned previously for rural, suburban and urban environments. The results show that in general the SUI and the COST-231 Hata model over-predict the path loss in all environments. The ECC-33 models shows the best results, especially in urban environments.

Smart bridges, smart tunnels: Transforming wireless sensor networks from research prototypes into robust engineering infrastructure

We instrumented large civil engineering infrastructure items, such as bridges and tunnels, with sensors that monitor their operational performance and deterioration. In so doing we discovered that commercial offerings of wireless sensor networks (WSNs) are still geared towards research prototypes and are currently not yet mature for deployment in practical scenarios. We distill the experience gained during this 3-year interdisciplinary project into specific advice for researchers and developers. We discuss problems and solutions in a variety of areas including sensor hardware, radio propagation, node deployment, system security and data visualization. We also point out the problems that are still open and that the community needs to address to enable widespread adoption of WSNs outside the research lab.

Wireless sensor network: Water distribution monitoring system

Historically, wireless sensor networks have mainly addressed military applications. However, in recent years, many civilian applications, such as managing inventory, monitoring product quality and monitoring disaster zones have emerged. Various technical issues, such as power consumption, radio propagation models, routing protocols, sensors etc need to be considered for different applications. In this paper, we propose a particular application for wireless sensor networks, specifically a water distribution network monitoring system. We propose a possible communication model for the water distribution monitoring network, and describe our channel measurement approach for the determination of an appropriate path-loss model. The accuracy of the proposed measurement approach has been confirmed using the flat earth two-ray model [1].

A new ordering for efficient sphere decoding

This paper presents a novel pre-processing stage that offers significant improvement in the computational efficiency of sphere decoding by imposing a geometrically-inspired ordering on the columns of the channel matrix. By studying the performance of a genie decoder, which has knowledge of the optimal radius, we find that the optimal ordering depends not only on the channel matrix, but also on the received point. Analysis of this idealized problem leads to the proposal of an enhanced ordering. We demonstrate via simulation that it closely matches with the optimal ordering and more importantly that it results in a dramatic increase in sphere decoding efficiency over a 4/spl times/4 MIMO flat fading channel. We emphasize that the performance benefit is particularly great at low SNRs and for high modulation orders, two traditionally challenging regimes for sphere decoders. We conclude by briefly discussing the polynomial complexity of the new ordering algorithm.

Towards commercial mobile ad hoc network applications: a radio dispatch system

We propose a novel and plausibly realistic application scenario for mobile ad hoc networks in the form of a radio dispatch system. We evaluate the system from both financial and technical perspectives to gain a complete picture of its feasibility. Using a realistic mobility and propagation model drawn from real world data we investigate the effects of node density, connection times and traffic congestion on the network coverage. We discuss design considerations in the light of the results. These findings are not limited to this particular scenario but are applicable to any mobile ad hoc system operating in similar conditions.

Projection Design for Statistical Compressive Sensing: A Tight Frame Based Approach

In this paper, we develop a framework to design sensing matrices for compressive sensing applications that lead to good mean squared error (MSE) performance subject to sensing cost constraints. By capitalizing on the MSE of the oracle estimator, whose performance has been shown to act as a benchmark to the performance of standard sparse recovery algorithms, we use the fact that a Parseval tight frame is the closest design - in the Frobenius norm sense - to the solution of a convex relaxation of the optimization problem that relates to the minimization of the MSE of the oracleestimator with respect to the equivalent sensing matrix, subject to sensing energy constraints. Based on this result, we then propose two sensing matrix designs that exhibit two key properties: the designs are closed form rather than iterative; the designs exhibit superior performance in relation to other designs in the literature, which is revealed by our numerical investigation in various scenarios with different sparse recovery algorithms including basis pursuit de-noise (BPDN), the Dantzig selector and orthogonal matching pursuit (OMP).

Performance of IEEE 802.11a in Vehicular Contexts

A key component of intelligent transportation is the provision of adequate network infrastructure to support vehicle-to-vehicle and vehicle-to-roadside communication. In this paper we report on performance evaluations carried out using the IEEE 802.11a protocol at 5.2 GHz between a moving vehicle and a fixed base station. We concentrate our evaluation on realistic urban speeds and environments, observing that performance at very low speeds is degraded due to the presence of null zones. We vary the modulation scheme and analyse the spread of resulting throughputs. Our results have implications for multimedia and other real-time applications that will utilise vehicle-to-roadside connectivity.

Energy-efficient signal acquisition in wireless sensor networks: a compressive sensing framework

The sampling rate of the sensors in wireless sensor networks (WSNs) determines the rate of its energy consumption, since most of the energy is used in sampling and transmission. To save the energy in WSNs and thus prolong the network lifetime, the authors present a novel approach based on the compressive sensing (CS) framework to monitor 1-D environmental information in WSNs. The proposed technique is based on CS theory to minimise the number of samples taken by sensor nodes. An innovative feature of the proposed approach is a new random sampling scheme that considers the causality of sampling, hardware limitations and the trade-off between the randomisation scheme and computational complexity. In addition, a sampling rate indicator feedback scheme is proposed to enable the sensor to adjust its sampling rate to maintain an acceptable reconstruction performance while minimising the number of samples. A significant reduction in the number of samples required to achieve acceptable reconstruction error is demonstrated using real data gathered by a WSN located in the Hessle Anchorage of the Humber Bridge.

On the Use of Unit-Norm Tight Frames to Improve the Average MSE Performance in Compressive Sensing Applications

This letter considers the design of sensing matrices with good expected-case performance for compressive sensing applications. By capitalizing on the mean squared error (MSE) of the oracle estimator, whose performance has been shown to act as a benchmark to the performance of standard sparse recovery algorithms, we demonstrate that a unit-norm tight frame is the closest design-in the Frobenius norm sense-to the solution of a convex relaxation of the optimization problem that relates to the minimization of the MSE of the oracle estimator with respect to the sensing matrix. Simulation results reveal that the MSE performance of a unit-norm tight frame based sensing matrix surpasses that of other standard sensing matrix designs in various scenarios, which include sparse recovery with basis pursuit denoise (BPDN), the Dantzig selector and orthogonal matching pursuit (OMP). This also has important practical implications because a unit-norm tight frame based sensing matrix can be designed very efficiently.

On the frame error rate of transmission schemes on quasi-static fading channels

It is known that the frame error rate of turbo codes on quasi-static fading channels can be accurately approximated using the convergence threshold of the corresponding iterative decoder. This paper considers quasi-static fading channels and demonstrates that non-iterative schemes can also be characterized by a similar threshold based on which their frame error rate can be readily estimated. In particular, we show that this threshold is a function of the probability of successful frame detection in additive white Gaussian noise, normalized by the squared instantaneous signal-to-noise ratio. We apply our approach to uncoded binary phase shift keying, convolutional coding and turbo coding and demonstrate that the approximated frame error rate is within 0.4 dB of the simulation results. Finally, we introduce performance evaluation plots to explore the impact of the frame size on the performance of the schemes under investigation.