Steve McLaughlin

Amplify-and-forward with partial relay selection

This letter offers a statistical analysis of the basic two-hop Amplify-and-Forward link, where the relay node is selected based on instantaneous and partial knowledge of the channel. In contrast with previously reported work, where relay selection requires global knowledge (2 hops) of the relaying link, the problem considered is interesting in practical ad-hoc systems, where only neighboring (1 hop) channel information is available to the nodes. The probability density function of the received signal-to-noise ratio for the considered relaying link is approximated in closed form, and an asymptotic exponential expression is proposed to simplify performance estimation.

Max-min relay selection for legacy amplify-and-forward systems with interference

In this paper, an amplify-and-forward (AF) cooperative strategy for interference limited networks is considered. In contrast to previously reported work, where the effect of interference is ignored, the effect of multi-user interference in AF schemes is analyzed. It is shown that the interference changes the statistical description of the conventional AF protocol and a statistical expression is subsequently derived. Asymptotic analysis of the expression shows that interference limits the diversity gain of the system and the related channel capacity is bounded by a stationary point. In addition, it is proven that previously proposed relay selection criteria for multi-relay scenarios become inefficient in the presence of interference. Based on consideration of the interference term, two extensions to the conventional max-min selection scheme suitable for different system setups are proposed. The extensions investigated are appropriate for legacy architectures with limitations on their flexibility where the max-min operation is pre-designed. A theoretical framework for selecting when to apply the proposed selection criteria is also presented. The algorithm investigated is based on some welldefined capacity approximations and incorporates the outage probabilities averaged over the fading statistics. Analytical results and simulation studies reveal enhancements of the proposed algorithm.

Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid, and other nonlinear models need to be considered, for instance, when there are multiscattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this article, we present an overview of recent advances in nonlinear unmixing modeling.

Complex-valued radial basis function network, Part I: network architecture and learning algorithms

Abstract The paper proposes a complex radial basis function network. The network has complex centres and connection weights, but the nonlinearity of its hidden nodes remains a real-valued function. This kind of network is able to generate complicated nonlinear decision surfaces or to approximate an arbitrary nonlinear function in complex multi-dimensional space, and it provides a powerful tool for nonlinear signal processing involving complex signals. The paper is divided into two parts. The first part introduces the network architecture and derives both block-data and recursive learning algorithms for this complex radial basis function network. The complex orthogonal least squares algorithm is a batch learning algorithm capable of constructing an adequate network structure, while a complex version of the hybrid clustering and least squares algorithm offers real-time adaptation capability. The identification of a nonlinear communications channel model is used to illustrate these two learning algorithms. In the second part of the paper, a practical application of this complex radial basis function network is demonstrated using digital communications channel equalisation.

Protocol design and throughput analysis for multi-user cognitive cooperative systems

This paper deals with protocol design for cognitive cooperative systems with many secondary users. In contrast with previous cognitive configurations, the channel model considered assumes a cluster of secondary users which perform both a sensing process for transmitting opportunities and can relay data for the primary user. Appropriate relaying improves the throughput of the primary users and can increase the transmission opportunities for the cognitive users. Based on different multi-access protocols, the schemes investigated enable relaying either between the primary user and a selected secondary user or between two selected secondary users. This collaboration can be a simple distributed multiple-input single-output transmission of the primary data or a simultaneous transmission of primary and secondary data using dirty-paper coding (DPC). The parametrization of DPC as well as its combination with opportunistic relay selection yields an interesting trade-off between the primary and the secondary performance which is investigated by theoretical and simulation results under the perspective of a desired primary throughput. The proposed protocols are studied from a networking point of view and the stable throughput for primary and secondary users is derived based on the principles of queueing theory.

Capacity and power investigation of opportunity driven multiple access (ODMA) networks in TDD-CDMA based systems

ODMA is a multi-hop relaying routing protocol, the use of which has been investigated in conventional cellular scenarios. This paper compares the performance of ODMA with direct transmission for cases where links may be required directly to other nodes, as well as to a controlling (backbone) node. For an interference-limited system, it is shown that whereas the topology is not supportable by a conventional (single-hop) system, a relayed system is able to provide service. A new admission control and routing algorithm based on receiver interference is presented which is shown to further enhance performance.

Techniques for improving cellular radio base station energy efficiency

The last ten years have witnessed explosive growth in the number of subscribers for mobile telephony. The technology has evolved from early voice only services to todays mobile wireless broadband (Internet) data delivery. The increasing use of wireless connectivity via smartphones and laptops has led to an exponential surge in network traffic. Meeting traffic demands will cause a significant increase in operator energy cost as an enlarged network of radio base stations will be needed to support mobile broadband effectively and maintain operational competitiveness. This article explores approaches that will assist in delivering significant energy efficiency gains in future wireless networks, easing the burden on network operators. It investigates three approaches to saving energy in future wireless networks. These include sleep mode techniques to switch off radio transmissions whenever possible; femtocell and relay deployments; and multiple antenna wireless systems. The impact of these approaches on achieving energy-efficient wireless communication systems is discussed.

Multi-stage blind clustering equaliser

A multi-stage blind clustering algorithm is proposed for equalisation of multi-level quadrature amplitude modulation (M-QAM) channels. A hierarchical decomposition divides the task of equalising a high-order QAM channel into much simpler sub-tasks. Each sub-task can be accomplished fast and reliably using a blind clustering algorithm derived originally for 4-QAM signals. The constant modulus algorithm (CMA) is used as a benchmark to assess this multi-stage blind equaliser. It is demonstrated that the new blind algorithm achieves much faster convergence and is very robust when input symbols are not sufficiently white. This multi-stage clustering equaliser only requires slightly more computations than the CMA and, like the latter, its computational complexity does not increase as the levels of digital symbols increase. >

Nonlinear Spectral Unmixing of Hyperspectral Images Using Gaussian Processes

This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and the nonlinear function are unknown. The first step of the proposed method estimates the abundance vectors for all the image pixels using a Bayesian approach an a Gaussian process latent variable model for the nonlinear function (relating the abundance vectors to the observations). The endmembers are subsequently estimated using Gaussian process regression. The performance of the unmixing strategy is first evaluated on synthetic data. The proposed method provides accurate abundance and endmember estimations when compared to other linear and nonlinear unmixing strategies. An interesting property is its robustness to the absence of pure pixels in the image. The analysis of a real hyperspectral image shows results that are in good agreement with state of the art unmixing strategies and with a recent classification method.

Joint detection-estimation of directional channel parameters using the 2-D frequency domain SAGE algorithm with serial interference cancellation

In this paper, the serial interference cancellation (SIC) technique and the frequency domain SAGE (FD-SAGE) algorithm are jointly used to detect and estimate the radio channel parameters of interest. The implementation of the SAGE algorithm in the frequency domain is novel. Furthermore, the parallel interference cancellation (PIC) technique in the standard SAGE algorithm is replaced by the SIC technique. The SIC technique demonstrates more stable performance especially in a multipath rich environment. The two-dimensional (2-D) FD-SAGE algorithm and the SIC technique are introduced and their performance demonstrated by using real indoor channel measurement data to jointly estimate the number of multipath components (MPCs), their time-of-arrivals (TOAs), angle-of-arrivals (AOAs) and complex amplitudes. Their performance is evaluated and compared using synthetic data and results using 2-D unitary ESPRIT (another form of super-resolution algorithm).