Generalized Selection Combining in Cognitive MIMO Relay Networks

We propose transmit antenna selection with receive generalized selection combining (TAS/GSC) in dual-hop cognitive decode-and-forward (DF) relay networks for reliability enhancement and interference relaxation. In this paradigm, a single antenna which maximizes the receive signal-to-noise ratio (SNR) is selected at the secondary transmitter and a subset of receive antennas with the highest SNRs are combined at the secondary receiver. To demonstrate the impact of multiple primary users on the cognitive relay network, we derive new closed-form expressions for the exact and asymptotic outage probability with TAS/GSC in the secondary network. Several important design insights are reached. We corroborate that the full diversity gain is achieved, which is entirely determined by the total number of antennas in the secondary network. The negative impact of the primary network on the secondary network is reflected in the SNR gain.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

<p>Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.</p>

Transmit Antenna Selection for Interference Management in Cognitive Relay Networks

We propose transmit antenna selection (TAS) in decode-and-forward (DF) relaying as an effective approach to reduce the interference in underlay spectrum sharing networks with multiple primary users (PUs) and multiple antennas at the secondary users (SUs). We compare two distinct protocols: 1) TAS with receiver maximal-ratio combining (TAS/MRC) and 2) TAS with receiver selection combining (TAS/SC). For each protocol, we derive new closed-form expressions for the exact and asymptotic outage probability with independent Nakagami-m fading in the primary and secondary networks. Our results are valid for two scenarios related to the maximum SU transmit power, i.e., P, and the peak PU interference temperature, i.e., Q. When P is proportional to Q, our results confirm that TAS/MRC and TAS/SC relaying achieve the same full diversity gain. As such, the signal-to-noise ratio (SNR) advantage of TAS/MRC relaying relative to TAS/SC relaying is characterized as a simple ratio of their respective SNR gains. When P is independent of Q, we find that an outage floor is obtained in the large P regime where the SU transmit power is constrained by a fixed value of Q. This outage floor is accurately characterized by our exact and asymptotic results.