Robert J. Piechocki

Expanding window fountain codes for unequal error protection

A novel approach to provide unequal error protection (UEP) using rateless codes over erasure channels, named Expanding Window Fountain (EWF) codes, is developed and discussed. EWF codes use a windowing technique rather than a weighted (non-uniform) selection of input symbols to achieve UEP property. The windowing approach introduces additional parameters in the UEP rateless code design, making it more general and flexible than the weighted approach. Furthermore, the windowing approach provides better performance of UEP scheme, which is confirmed both theoretically and experimentally.

AND-OR tree analysis of distributed LT codes

In this contribution, we consider design of distributed LT codes, i.e., independent rateless encodings of multiple sources which communicate to a common relay, where relay is able to combine incoming packets from the sources and forwards them to receivers. We provide density evolution formulae for distributed LT codes, which allow us to formulate distributed LT code design problem and prove the equivalence of performance of distributed LT codes and LT codes with related parameters in the asymptotic regime. Furthermore, we demonstrate that allowing LT coding apparatus at both the sources and the relay may prove advantageous to coding only at the sources and coding only at the relay.

Expanding Window Fountain Codes for Unequal Error Protection

A novel approach to provide unequal error protection (UEP) using rateless codes over erasure channels, named Expanding Window Fountain (EWF) codes, is developed and discussed. EWF codes use a windowing technique rather than a weighted (non-uniform) selection of input symbols to achieve UEP property. The windowing approach introduces additional parameters in the UEP rateless code design, making it more general and flexible than the weighted approach. Furthermore, the windowing approach provides better performance of UEP scheme, which is confirmed both theoretically and experimentally.

A channel estimation algorithm for MIMO-SCFDE

This letter proposes a novel method for channel estimation in a single-carrier multiple input-multiple output (MIMO) system with frequency-domain equalization/detection. To this end, we construct novel short MIMO training sequences that have constant envelope in the time domain to preclude the peak-to-average power ratio problem encountered in many systems that utilize the frequency domain for data recovery. Simultaneously, the spectrum in the frequency domain is flat except for a grid of nulls for predefined frequency tones. Armed with these sequences, we provide an algorithm that is optimal in the least squares (LS) sense at a potentially low computational cost. Results show that the algorithm performs identically to other proposed LS techniques. Furthermore, the algorithm is extremely bandwidth efficient in that the total training overhead required to obtain full CSI is just one block.

A multi-modal sensor infrastructure for healthcare in a residential environment

Ambient Assisted Living (AAL) systems based on sensor technologies are seen as key enablers to an ageing society. However, most approaches in this space do not provide a truly generic ambient space - one that is not only capable of assisting people with diverse medical conditions, but can also recognise the habits of healthy habitants, as well as those with developing medical conditions. The recognition of Activities of Daily Living (ADL) is key to the understanding and provisioning of appropriate and efficient care. However, ADL recognition is particularly difficult to achieve in multi-resident spaces; especially with single-mode (albeit carefully crafted) solutions, which only have limited capabilities. To address these limitations we propose a multi-modal system architecture for AAL remote healthcare monitoring in the home, gathering information from multiple, diverse (sensor) data sources. In this paper we report on developments made to-date in various technical areas with respect to critical issues such as cost, power consumption, scalability, interoperability and privacy.

Low-complexity smart antenna methods for third-generation W-CDMA systems

There is still an open debate within the research community regarding the likely performance enhancement of smart antennas versus their complexity for commercial wireless applications. The goal of the study presented in this paper is to investigate the performance improvement attainable using relatively simple smart antenna techniques when applied to the third-generation W-CDMA air interface. Methods to achieve this goal include fixed multibeam architectures with different beam selection algorithms (maximum power criterion, combined beams) or adaptive solutions driven by relatively simple direction finding algorithms. After comparing these methods against each other for several representative scenarios, some issues related to the sensitivity of these methods are also studied, (e.g., robustness to environment, mismatches originating from implementation limitations, etc.). Results indicate that overall, conventional beamforming seems to be the best choice in terms of balancing the performance and complexity requirements, in particular when the problem with interfering high-bit-rate W-CDMA users is considered.

Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids

This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems.

Bayesian sequential compressed sensing in sparse dynamical systems

While the theory of compressed sensing provides means to reliably and efficiently acquire a sparse high-dimensional signal from a small number of its linear projections, sensing of dynamically changing sparse signals is still not well understood. We pursue a Bayesian approach to the problem of sequential compressed sensing and develop methods to recursively estimate the full posterior distribution of the signal.

Fountain code design for data multicast with side information

Fountain codes are a robust solution for data multicasting to a large number of receivers which experience variable channel conditions and different packet loss rates. However, the standard fountain code design becomes inefficient if all receivers have access to some side information correlated with the source information. We focus our attention on the cases where the correlation of the source and side information can be modelled by a binary erasure channel (BEC) or by a binary input additive white Gaussian noise channel (BIAWGNC). We analyse the performance of fountain codes in data multicasting with side information for these cases, derive bounds on their performance and provide a fast and robust linear programming optimization framework for code parameters. We demonstrate that systematic Raptor code design can be employed as a possible solution to the problem at the cost of higher encoding/decoding complexity, as it reduces the side information scenario to a channel coding problem. However, our results also indicate that a simpler solution, non-systematic LT and Raptor codes, can be designed to perform close to the information theoretic bounds.

Decentralised distributed fountain coding: asymptotic analysis and design

A class of generic decentralised distributed fountain coding schemes is introduced and the tools of analysis of the performance of such schemes are presented. It is demonstrated that the developed approach can be used to formulate a robust code design methodology in a number of instances. We show that two non-standard applications of fountain codes, fountain codes for distributed source coding and fountain codes for unequal error protection lie within this decentralised distributed fountain coding framework.