Improving the performance of equalization in communication systems

In this paper, research on exploring the potential of several popular equalization techniques while overcoming their disadvantages has been conducted. First, extensive literature survey on equalization is conducted. The focus has been placed on several popular linear equalization algorithm such as the conventional least-mean-square (LMS) algorithm, the recursive least squares (RLS) algorithm, the fi1tered-X LMS algorithm and their development. The approach in analysing the performance of the filtered-X LMS Algorithm, a heuristic method based on linear time-invariant operator theory is provided to analyse the robust perfonnance of the filtered-X structure. It indicates that the extra filter could enhance the stability margin of the corresponding non filtered X structure. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H2 optimal initialization is proposed.

Channel estimation scheme for 3.9G wireless communication systems using RLS algorithm

Main challenges for a terminal implementation are efficient realization of the receiver, especially for channel estimation (CE) and equalization. In this paper, training based recursive least square (RLS) channel estimator technique is presented for a long term evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) wireless communication system. This CE scheme uses adaptive RLS estimator which is able to update parameters of the estimator continuously, so that knowledge of channel and noise statistics are not required. Simulation results show that the RLS CE scheme with 500 Hz Doppler frequency has 3 dB better performances compared with 1.5 kHz Doppler frequency.

Modelling emergency response communication systems

Subsequent to the Australian 'Black Saturday' bushfires there were a number of issues arising from investigations with regard to the functional stability and resilience of communications systems and the flow of information between emergency response organisations, and their ability to provide relevant information to the general public. In some cases, the transference of information failed or was late or ineffective with regard to decisions, advice and information broadcasting during the crisis. This was particularly evident in terms of managing emergency organisational information requests and field situational advice both to and from emergency response management teams and the delivery of informative advice to the public. This paper analyses one such case study with a view of applying a systems modelling technique to determine the viability of the communication systems and information exchange structures associated with an emergency response agency.

Rural realities: digital communication challenges for rural Australian local governments

Purpose – This article explores challenges for rural Australian local governments during the transition to high-speed broadband infrastructure. Despite the National Broadband Network’s promised ubiquitous connectivity, significant access discrepancies remain between rural and urban areas.Methodology – Empirical findings are drawn from a full-day workshop on digital connectivity, which included participants from seven rural local governments in New South Wales, Australia. Thematic analysis of the workshop transcript was undertaken in order to extrapolate recurring nuances of rural digital exclusion. Findings – Rural communities face inequitable prospects for digital inclusion, and authorities confront dual issues of accommodating connected and unconnected citizens. Many areas have no or poor broadband access, and different digital engagement expectations are held by citizens and local governments. Citizens seek interactive opportunities, but rural authorities often lack the necessary resources to offer advanced participatory practices. Research limitations/implications – While this research draws from a small sample of government officials, their insights are nonetheless heuristically valuable in identifying connectivity issues faced in rural Australia. These issues can guide further research into other regions as well as civic experiences of digital inclusion. Practical/social implications – There is a need to reconceive Australia’s current policy approach to broadband. Greater rural digital inclusion may be achieved by focusing on connectivity as a public interest goal, targeting infrastructure developments to suit local contexts, and implementing participatory digital government practices. Originality/value – The actions suggested would help ensure equity of digital inclusion across Australian municipal areas. Without such changes, there is a risk of rural citizens facing further marginalisation through digital exclusion.

Medical image classification using an efficient data mining technique

Data mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.

Precoder design for multi-antenna systems with temporally and spatially correlated fading channels

This paper addresses the problem of the design of a precoder for multiple transmit antenna communication systems with spatially and temporally correlated fading channels. Using the theories of matrix differential calculus, the paper derives a precoder for unitary space-time codes that can exploit the spatio-temporal correlation in the time-varying fading channels. The design criterion is based on minimizing the mean square error of the channel estimates. Computer simulation results show that a significant performance gain can be achieved by using the designed precoder.

Distributed power control in cellular mobile radio systems with time-varying link gains

A simple distributed power control algorithm for communication systems with mobile users and unknown time-varying link gains is proposed. We prove that the proposed algorithm is exponentially converging. Furthermore, we show that the algorithm significantly outperforms the well-known Foschini and Miljanic algorithm in the case of quickly moving mobile users.

Distributed power control in cellular mobile radio systems with time-varying link gains

A simple distributed power control algorithm for communication systems with mobile users and unknown timevarying link gains is proposed. We prove that the proposed algorithm is exponentially converging. Furthermore, we show that the algorithm significantly outperforms the well-known
Foschini and Miljanic algorithm in the case of quickly moving mobile users.

The effects of the social structure of digital networks on viral marketing performance

Viral marketing is a form of peer-to-peer communication in which individuals are encouraged to pass on promotional messages within their social networks. Conventional wisdom holds that the viral marketing process is both random and unmanageable. In this paper, we deconstruct the process and investigate the formation of the activated digital network as distinct from the underlying social network. We then consider the impact of the social structure of digital networks (random, scale free, and small world) and of the transmission behavior of individuals on campaign performance. Specifically, we identify alternative social network models to understand the mediating effects of the social structures of these models on viral marketing campaigns. Next, we analyse an actual viral marketing campaign and use the empirical data to develop and validate a computer simulation model for viral marketing. Finally, we conduct a number of simulation experiments to predict the spread of a viral message within different types of social network structures under different assumptions and scenarios. Our findings confirm that the social structure of digital networks play a critical role in the spread of a viral message. Managers seeking to optimize campaign performance should give consideration to these findings before designing and implementing viral marketing campaigns. We also demonstrate how a simulation model is used to quantify the impact of campaign management inputs and how these learnings can support managerial decision making.

Multi-input-multi-output antennas for radio frequency identification systems

In this chapter the authors discuss the physical insight of the role of wireless communication in RFID systems. In this respect, this chapter gives a brief introduction on the wireless communication model followed by various communication schemes. The chapter also discusses various channel impairments and the statistical modeling of fading channels based on the environment in which the RFID tag and reader may be present. The chapter deals with the fact that the signal attenuations can be dealt with up to some level by using multiple antennas at the reader transmitter and receiver to improve the performance. Thus, this chapter discusses the use of transmit diversity at the reader transmitter to transmit multiple copies of the signal. Following the above, the use of receiver combining techniques are discussed, which shows how the multiple copies of the signal arriving at the reader receiver from the tag are combined to reduce the effects of fading. The chapter then discusses various modulation techniques required to modulate the signal before transmitting over the channel. It then presents a few channel estimation algorithms, according to which, by estimating the channel state information of the channel paths through which transmission takes place, performance of the wireless system can be further increased. Finally, the Antenna selection techniques are presented, which further helps in improving the system performance.

Antenna selection and channel estimation for hybrid relay systems

The research addressed performance issues for wireless signal transmission and has shown that performance improves with the help of relays due to increased diversity. Further, the areas of antenna selection and channel estimation and modelling has been investigated for improved cost and complexity and has shown to further enhance the performance of the wireless relay systems.

Superimposed channel training for two-way MIMO relay systems

Two-way relaying systems are known to be capable of providing higher spectral efficiency compared with one-way relaying systems. However, the channel estimation problem for two-way relaying systems becomes more complicated. In this paper, we propose a superimposed channel training scheme for two-way MIMO relay communication systems, where the individual channel information for users-relay and relay-users links are estimated. The optimal structure of the source and relay training sequences are derived when the mean-squared error (MSE) of channel estimation is minimized. We also optimize the power allocation between the source and relay training sequences to improve the performance of the algorithm. Numerical examples are shown to demonstrate the performance of the proposed channel training algorithm.

The Twitter float and free labour : the political economy of digital media

Nick Dyer-Witheford’s Cyber-Marx was published nearly 15 years ago, but there are continuing echoes of its dire promises today. The trends that Dyer-Witheford outlined—the growth of tech-giants in the communications field at the expense of democratic media practices and the radical shedding of jobs in the traditional mass media context—are confirmed by recent events. In November 2013, Twitter launched itself on the public share register, despite having no visible means of financial support, or even much of a business plan. The Twitter IPO tells us a lot about the economy of cyber-capitalism. Aligned to the trend of ‘technological unemployment’ is the rise of what some commentators call ‘digital serfdom’. This is not just growing unemployment, but also drastic under-employment of talented media professionals and an alarming rise in the number of media outlets that want to pay contributors in ‘exposure’, rather than in corporeal, fungible dollars and cents. This articlediscusses these trends and events in the context of the political economy of digital communication.

Communicating with nurses: the experiences of 10 individuals with an acquired severe communication impairment

Successful communication is integral to quality health care and successful nursing practice. Ten people who had been in hospital in the 12 months prior to the study and who had no functional speech at that time were interviewed about their communication experiences with nurses. Overall, these individuals experienced difficulties, some of which appeared to be related to a lack of augmentative and alternative communication (AAC) resources and a lack of knowledge of AAC among nurses. In addition, the participants noted that nurses did not always have the time or the skills to communicate effectively with them. The participants suggested strategies to improve communication interactions between patients with no or limited functional speech and nurses. These strategies include pre-admission briefing and training nurses about effective strategies for communicating with patients who are unable to speak, including the use of augmentative and alternative communication systems.

Channel estimation for frequency-selective two-way MIMO relay systems

In this paper, we investigate the channel estimation problem for two-way multiple-input multiple-output (MIMO) relay communication systems in frequency-selective fading environments. We propose a superimposed channel training algorithm to estimate the individual channel state information (CSI) of the first-hop and second-hop links for two-way MIMO relay systems with frequency-selective fading channels. In this algorithm, a relay training sequence is superimposed on the received signals at the relay node to assist the estimation of the second-hop channel matrices. The optimal structure of the source and relay training sequences is derived to minimize the meansquared error (MSE) of channel estimation. We also derive the optimal power allocation between the source and relay training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm.