Tricia J. Willink

Wide-Sense Stationarity of Mobile MIMO Radio Channels

Tests for the wide-sense stationarity (WSS) of multivariate random processes are developed and applied to measured multiple-input-multiple-output (MIMO) radio channel responses. Evolutionary spectral density functions are used to estimate the time-dependent power spectral density, and analysis of variance procedures are applied to determine whether the first and second moments of the data series exhibit statistically significant changes over short intervals. It is found that while the assumption of WSS is met in 40%-70% of half-second intervals for a single element at the transmitter and receiver, only 2%-12% of intervals with two elements at the transmitter and receiver are jointly WSS; furthermore, the occurrence of non-WSS intervals is not dependent on the antenna separation. The analyses also show that WSS intervals are interspersed with long non-WSS intervals.

Robust IWFA for Open-Spectrum Communications

This correspondence considers a wireless open-spectrum communication system with N orthogonal narrowband tones and K users who compete to maximize their individual data rates. The correspondence proposes a robust version of the iterative water-filling algorithm (IWFA) that takes into account potential inaccuracies in the noise-plus-interference levels available at the transmitters. It is shown that, in comparison with the conventional version, the robust IWFA (RIWFA) can lead the users to behave in a less greedy fashion and to collaborate unintentionally in order to increase the network sum-rate. In particular, using an appropriately chosen RIWFA parameter, we show that RIWFA yields almost the same sum-rate as price-based IWFA (PIWFA) which requires collaboration of users.

Channel estimation using DPSS based frames

Accurate and sparse representation of a moderately fast fading channel using bases functions is achievable when both channel and bases bands align. If a mismatch exists, usually a larger number of bases functions is needed to achieve the same accuracy. In this paper, we propose a novel approach for channel estimation based on frames, which preserves sparsity and improves estimation accuracy. Members of the frame are formed by modulating and varying the bandwidth of discrete prolate spheroidal sequences (DPSS) in order to reflect various scattering scenarios. To achieve the sparsity of the proposed representation, a matching pursuit approach is employed. The estimation accuracy of the scheme is evaluated and compared with the accuracy of a Slepian basis expansion estimator based on DPSS for a variety of mobile channel parameters. The results clearly indicate that for the same number of atoms, a significantly higher estimation accuracy is achievable with the proposed scheme when compared to the DPSS estimator.

Efficient Clustering of Cognitive Radio Networks Using Affinity Propagation

Cognitive radios must be able to form collaborative wireless network clusters in dynamically changing environments to achieve such desired objectives as interference resilience and low communications overhead. In this work, we explore the affinity propagation (AP) message-passing technique to efficiently group nodes in an ad hoc cognitive radio network (CRN). With the proposed approach, nodes exchange local messages with their immediate neighbours until a high quality set of clusterheads and a corresponding cluster structure emerges. The messages are calculated based on measures of similarity between the network nodes, which are selected based on application requirements and the objective of the grouping process. As an initial application, we focus on finding a small dominating set of a CRN with the aim of reducing the number of nodes that participate in key network functions such as resource management and routing table maintenance. To demonstrate the merits of the proposed clustering approach, the AP technique is evaluated on randomly generated open spectrum access network scenarios. The simula- tion results demonstrate that the proposed technique provides a smaller number of clusters than methods based on approximating a minimum size dominating set of the corresponding ad hoc network graphs.

Modeling and Performance Analysis of Multihop Cooperative Wireless Networks

In this paper, we develop a cross-layer analytical framework for studying the performance of reliable relaying schemes in multihop cooperative diversity systems. A generalized signaling model over independent nonidentical Nakagami-m fading channels is assumed, and mathematical expressions for end-to-end throughput rate, spectral efficiency, and latency distribution are derived. The proposed framework can capture the effect of correlated packet errors caused by cooperative retransmission attempts and, thus, adequately evaluate the network performance. Using the framework, the performance of automatic-repeat-request-based relaying schemes is demonstrated over a range of network conditions and system parameters. It is shown that tradeoffs are necessary among network robustness, throughput rate, spectral efficiency, and latency variability.

Improved estimation of the ricean K-factor from I/Q fading channel samples

The Ricean K-factor is a practical channel quality measure in many wireless communication applications as it exhibits a fast estimation convergence compared with the explicit estimation of performance metrics such as error rates. Recently, it has been shown that estimation of the K-factor can be improved relative to envelope-based detectors through the use of complex (I/Q) channel observations. In this paper, an analysis of the maximum likelihood estimator of the K-factor from I/Q samples is presented, which illustrates the bias of previous estimators. An improved estimator is then proposed, which has superior bias and efficiency for short data records. For mobile applications, a reliable estimator of the Doppler shift of the specular component is incorporated. Simulation results for a range of channel conditions illustrate that the proposed estimator outperforms prior techniques, and provide insight into the record lengths required to achieve a desired estimation accuracy.

Overloaded Array Processing Using Genetic Algorithms With Soft-Biased Initialization

The application of genetic algorithm (GA) techniques to the problem of overloaded arrays, in which the number of transmitted narrowband signals is greater than the number of receiver array elements, is explored. A new receiver algorithm is presented, which achieves nearly optimal performance but requires significantly less complexity than the maximum-likelihood joint detection (MLJD) receiver. It uses GA techniques with soft-biased initialization, which is efficiently generated using spatial filtering, providing dramatic convergence improvements compared with other initialization schemes. Simulations using both idealized channel models and measured channel responses are used to investigate the impact of spatial correlation and imperfect channel state information (CSI).

A Distributed Message-passing Approach for Clustering Cognitive Radio Networks

Forming collaborative wireless network clusters in dynamically changing environments is essential for cognitive radios to achieve such desired objectives as interference resilience and low communications overhead. In this paper, a novel approach to form efficient node clusters in an ad hoc cognitive radio network (CRN) is introduced based on the affinity propagation (AP) message-passing technique. With this approach, nodes exchange messages containing local network information with their direct neighbours until a high quality set of clusterheads and an efficient cluster structure emerges. The groupings are based on measures of similarity between the network nodes, which are selected based on application requirements. As an initial application, we show how the AP technique can be used to distributively determine cluster assignments and elect a small number of clusterheads that cover a CRN. Such an objective is commonly used to reduce communication overhead in key network functions such as resource management and routing table maintenance. To demonstrate the merits of the proposed approach, the clustering efficiency of the AP technique is evaluated on randomly generated open spectrum access scenarios. The simulation results demonstrate that the proposed approach provides a smaller number of clusters than a standard technique based on approximating the minimum dominating sets of the corresponding ad hoc network graphs.

Limited Feedback Precoding in Realistic MIMO Channel Conditions

MIMO system design for real applications requires that factors such as channel estimation errors, feedback quantisation and realistic channel responses be taken into account. Limited feedback precoding has been introduced to address the feedback overhead; it is a quantised approximation to equal-power eigenbeamforming. The performance of this technique is evaluated using channel responses obtained from a typical urban microcell. The impact of inaccurate channel state information is considered, in addition to the data rate and feedback requirement. The results of this novel approach clearly show the dependence of the system parameter selection on the local topographical environment, and the importance of codebook size and generation as well as channel estimation.

Improved Estimation of the Ricean K Factor from I/Q Samples

Knowledge of the Ricean K factor is important in many communication applications as it reveals the fading severity experienced by a wireless receiver. In this paper, a statistical analysis of the maximum likelihood estimator (MLE) of K from complex (I/Q) Ricean channel samples is derived. Based on our analysis, an improved estimator of the K factor is proposed with superior bias and efficiency properties for short data records. The lack of robustness of the tractable MLE in mobile applications is addressed by incorporating a reliable estimator of the specular Doppler shift. The simulation results show that the proposed technique can outperform prior methods for I/Q mobile channel snapshots.