Capacity scaling law by multiuser diversity in cognitive radio systems

This paper analyzes the multiuser diversity gain in a cognitive radio (CR) system where secondary transmitters opportunistically utilize the spectrum licensed to primary users only when it is not occupied by the primary users. To protect the primary users from the interference caused by the missed detection of primary transmissions in the secondary network, minimum average throughput of the primary network is guaranteed by transmit power control at the secondary transmitters. The traffic dynamics of a primary network are also considered in our analysis. We derive the average achievable capacity of the secondary network and analyze its asymptotic behaviors to characterize the multiuser diversity gains in the CR system.Comment: 5 pages, 2 figures, ISIT2010 conferenc

An overall comparison of small molecules and large biologics in ADME testing

Biologics mainly monoclonal antibodies (mAbs) and antibody-drug conjugates (ADCs) as new therapeutics are becoming increasingly important biotherapeutics. This review is intended to provide an overall comparison between small molecules (SMs) and biologics or large molecules (LMs) concerning drug metabolism and pharmacokinetic (DMPK) or associated with absorption, distribution, metabolism and elimination (ADME) testing from pharmaceutical industry drug discovery and development points of view, which will help design and conduct relevant ADME testing for biologics such as mAbs and ADCs. Recent advancements in the ADME for testing biologics and related bioanalytical methods are discussed with an emphasis on ADC drug development as an example to understand its complexity and challenges from extensive in vitro characterization to in vivo animal PK studies. General non-clinical safety evaluations of biologics in particular for ADC drugs are outlined including drug-drug interaction (DDI) and metabolite/catabolite assessments. Regulatory guidance on the ADME testing and safety evaluations including immunogenicity as well as bioanalytical considerations are addressed for LMs. In addition, the preclinical and human PK data of two marked ADC drugs (ADCETRIS, SGN-35 and KADCYLA, T-DM1) as examples are briefly discussed with regard to PK considerations and PK/PD perspectives

Doctor of Philosophy

dissertationThis dissertation addresses several key challenges in multiple-antenna communications, including information-theoretical analysis of channel capacity, capacity-achieving signaling design, and practical statistical detection algorithms. The first part of the thesis studies the capacity limits of multiple-input multiple-output (MIMO) multiple access channel (MAC) via virtual representation (VR) model. The VR model captures the physical scattering environment via channel gains in the angular domain, and hence is a realistic MIMO channel model that includes many existing channel models as special cases. This study provides analytical characterization of the optimal input distribution that achieves the sum-capacity of MAC-VR. It also investigates the optimality of beamforming, which is a simple scalar coding strategy desirable in practice. For temporally correlated channels, beamforming codebook designs are proposed that can efficiently exploit channel correlation. The second part of the thesis focuses on statistical detection for time-varying frequency-selective channels. The proposed statistical detectors are developed based on Markov Chain Monte Carlo (MCMC) techniques. The complexity of such detectors grows linearly in system dimensions, which renders them applicable to inter-symbol-interference (ISI) channels with long delay spread, for which the traditional trellis-based detectors fail due to prohibitive complexity. The proposed MCMC detectors provide substantial gain over the de facto turbo minimum-mean square-error (MMSE) detector for both synthetic channel and underwater acoustic (UWA) channels. The effectiveness of the proposed MCMC detectors is successfully validated through experimental data collected from naval at-sea experiments

Issues in Group Sequential/Adaptive Designs

In recent years, there has been great interest in the use of adaptive features in clinical trials (i.e., changes in design or analyses guided by examination of the accumulated data at an interim point in the trial) that may make the studies more efficient (e.g., shorter duration, fewer patients). Many statistical methods have been developed to maintain the validity of study results when adaptive designs are used (e.g., control of the Type I error rate). Group sequential designs, which allow early stopping for efficacy in light of compelling evidence of benefit or early stopping for futility when the likelihood of success is low at interim analyses, have been widely used for many years. In this dissertation, we study several aspects of statistical issues in group sequential/adaptive designs. Sample size re-estimation has drawn a great deal of interest due to its permitting revision of the target treatment difference based on the unblinded interim analysis results from an ongoing trial. A possible risk of ublinded sample size re-estimation is that the exact treatment effect being observed at interim analysis might be back-calculated from the modified sample size, which might jeopardize the integrity of the trial. In the first project, we propose a pre-specified stepwise two-stage sample size adaptation to lessen the information on treatment effect that would be revealed. We minimize expected sample size among a class of these designs and compare efficiency with the fully optimized two-stage design, optimal two-stage group sequential design and designs based on promising conditional power. In the second project, we define the complete ordering of a group sequential sample space and show that a Wang-Tsiatis boundary family or an exponential spending function family can completely order the sample space. We also propose a simple method to transform a spending function to a completely ordered sample space when using the sequential p-value ordering. This method is also extended to β-spending functions for p-values to reject the alternative hypothesis. In the third project, we propose a simple approach for controlling the familywise error rate in a group sequential design with multiple testing. We apply sequential p-values at the interim analysis from a group sequential design to the sequentially rejective graphical procedure which is based on the closure principle. We also use simulations to study the operating characteristics of multiple testing in group sequential designs. We show that in terms of expected sample size, using a group sequential design in multiple hypothesis testing is more efficient than fixed sample size designs in many scenarios