This work proposes a novel design automation (DA) technique that uses a multifaceted approach combining Multivariate Regression with Geometric Programming (GP) to design analog circuits. Previous DA methods employing GP have typically used analytical derivations of the various design equations representing an analog circuit. The proposed DA method eliminates the need for analytical derivations by using simulation data and multivariate regression to generate statistical models combined with GP to solve these statistical expressions with respect to optimum circuit design parameters. This presented statistical GP method has been applied to successfully design a five-transistor two-stage operational amplifier and a folded cascode amplifier in a TSMC 65nm CMOS technology. The presented statistical GP DA results are comparable to the design results obtained from both analytical GP and manual design by an experienced analog design engineer.

Online controlled experiments (e.g., A/B tests) are an integral part of successful data-driven companies. At Microsoft, supporting experimentation poses a unique challenge due to the wide variety of products being developed, along with the fact that experimentation capabilities had to be added to existing, mature products with codebases that go back decades. This paper describes the Microsoft ExP Platform (ExP for short) which enables trustworthy A/B experimentation at scale for products across Microsoft, from web properties (such as bing.com) to mobile apps to device drivers within the Windows operating system. The two core tenets of the platform are trustworthiness (an experiment is meaningful only if its results can be trusted) and scalability (we aspire to expose every single change in any product through an A/B experiment). Currently, over ten thousand experiments are run annually. In this paper, we describe the four core components of an A/B experimentation system: experimentation portal, experiment execution service, log processing service and analysis service, and explain the reasoning behind the design choices made. These four components work together to provide a system where ideas can turn into experiments within minutes and those experiments can provide initial trustworthy results within hours.

We find the exact per-node capacity scaling in an extended, distributed wireless network where the node locations are random, the channel attenuation between pairs of nodes exhibits independent random fading, and data forwarding is restricted to multihop delivery. We establish a per-node throughput upper bound equal to (Cmax)/(radicn) (bps)/(Hz), and a constructive lower bound equal to (Cmin)/(radicn) (bps)/(Hz), where Cmaxand Cminare constants andnis the expected number of nodes in the network. The results apply with probability approaching unity asnbecomes large, under independent, frequency flat, fading channel conditions where the tail probability exhibits an exponential decay (e.g., any mixture of line-of-sight and Rayleigh, Rice and Nakagami distributions). Our lower bound is mainly motivated by a clever bond-percolation-based protocol construction, introduced by Franceschetti et al., where it was shown that a per-node throughput equal to a constant times 1/(radicn) (bps)/(Hz) is achievable in networks with random node locations under a deterministic channel gain modeling path-loss and absorption. We extend the result to more realistic channel gain models in which the channel gains are modeled as random due to multipath effects. The protocol systematically leverages multiuser diversity to overcome the added uncertainty. The key to the upper bound is to establish an upper bound for the total network transport capacity. This upper bound proves that, relative to the lower bound construction, any attempt to leverage multiuser diversity further will result in no more than a constant factor of throughput gain.

A multiple-input multiple-output (MIMO) downlink system in which data is transmitted to two users over a common wireless channel is considered. The channel is assumed to be fixed for all transmissions over the period of interest and the ratio of anticipated average arrival rates for the two users, also known as the relative traffic rate, is the system design parameter. A packet-based traffic model is considered where data for each user is queued at the transmit end. A queueing analog for this system leads to a coupled queueing system for which a simple policy is known to be throughput-optimal under Markovian assumptions. Since an exact expression for the performance is not available, as a measure of performance (in heavy traffic), a diffusion approximation is established. This diffusion process is a two-dimensional (2-D) Semimartingale Reflecting Brownian Motion (SRBM) living in the positive quadrant of 2-D space

We consider the problem of achievable per-node throughput in an extended distributed wireless network where the node locations are random and the channel attenuation between pairs of nodes exhibits independent random multipath fading. In [1] a clever protocol construction based on percolation theory was used to show that a per node throughput of a constant times 1/√n bps is achievable with probability approaching one as the expected number of nodes in network, n, becomes large (i.e. w.h.p.), for networks with random node locations and no multipath fading. We use a similar approach to extend the result to a more realistic channel gain model where the channel gains are random due to multipath fading effects. In particular, we show that when the channels are modeled as independent, frequency flat with Rayleigh distributed gains, a throughput of a constant times 1/√n log log n bps/node is achievable w.h.p..