Millimeter-wave Wireless LAN and its Extension toward 5G Heterogeneous Networks

Millimeter-wave (mmw) frequency bands, especially 60 GHz unlicensed band, are considered as a promising solution for gigabit short range wireless communication systems. IEEE standard 802.11ad, also known as WiGig, is standardized for the usage of the 60 GHz unlicensed band for wireless local area networks (WLANs). By using this mmw WLAN, multi-Gbps rate can be achieved to support bandwidth-intensive multimedia applications. Exhaustive search along with beamforming (BF) is usually used to overcome 60 GHz channel propagation loss and accomplish data transmissions in such mmw WLANs. Because of its short range transmission with a high susceptibility to path blocking, multiple number of mmw access points (APs) should be used to fully cover a typical target environment for future high capacity multi-Gbps WLANs. Therefore, coordination among mmw APs is highly needed to overcome packet collisions resulting from un-coordinated exhaustive search BF and to increase the total capacity of mmw WLANs. In this paper, we firstly give the current status of mmw WLANs with our developed WiGig AP prototype. Then, we highlight the great need for coordinated transmissions among mmw APs as a key enabler for future high capacity mmw WLANs. Two different types of coordinated mmw WLAN architecture are introduced. One is the distributed antenna type architecture to realize centralized coordination, while the other is an autonomous coordination with the assistance of legacy Wi-Fi signaling. Moreover, two heterogeneous network (HetNet) architectures are also introduced to efficiently extend the coordinated mmw WLANs to be used for future 5th Generation (5G) cellular networks.Comment: 18 pages, 24 figures, accepted, invited paper

Dynamic adaptation to CPU and memory load in scientific applications

As commodity computers and networking technologies have become faster and more affordable, fairly capable machines have become nearly ubiquitous while the effective distance between them has decreased as network connectivity and capacity has multiplied. There is considerable interest in developing means to readily access such vast amounts of computing power to solve scientific problems, but the complexity of these modern computing environments pose problems for conventional computer codes designed to run on a static, homogeneous set of resources. One source of problems is the heterogeneity that is naturally present in these settings. More problematic is the competition that arises between programs for shared resources in these semi-autonomous environments. Fluctuations in the availability of CPU, memory, and other resources can cripple application performance. Contention for CPU time between jobs may introduce significant load imbalance in parallel applications. Contention for limited memory resources may cause even more severe performance problems, as thrashing may increase execution times by an order of magnitude or more.;Our goal is to develop techniques that enable scientific applications to achieve good performance in non-dedicated environments by monitoring system conditions and adapting their behavior accordingly. We focus on two important shared resources, CPU and memory, and pursue our goal on two distinct but complementary fronts: First, we present some simple algorithmic modifications that can significantly improve load balance in a class of iterative methods that form the computational core of many scientific and engineering applications. Second, we introduce a framework for enabling scientific applications to dynamically adapt their memory usage according to current availability of main memory. An application-specific caching policy is used to keep as much of the data set as possible in main memory, while the remainder of the data are accessed in an out-of-core fashion.;We have developed modular code libraries to facilitate implementation of our techniques, and have deployed them in a variety of scientific application kernels. Experimental evaluation of their performance indicates that our techniques provide some important classes of scientific applications with robust and low-overhead means for mitigating the effects of fluctuations in CPU and memory availability

Optimized Surface Code Communication in Superconducting Quantum Computers

Quantum computing (QC) is at the cusp of a revolution. Machines with 100 quantum bits (qubits) are anticipated to be operational by 2020 [googlemachine,gambetta2015building], and several-hundred-qubit machines are around the corner. Machines of this scale have the capacity to demonstrate quantum supremacy, the tipping point where QC is faster than the fastest classical alternative for a particular problem. Because error correction techniques will be central to QC and will be the most expensive component of quantum computation, choosing the lowest-overhead error correction scheme is critical to overall QC success. This paper evaluates two established quantum error correction codes---planar and double-defect surface codes---using a set of compilation, scheduling and network simulation tools. In considering scalable methods for optimizing both codes, we do so in the context of a full microarchitectural and compiler analysis. Contrary to previous predictions, we find that the simpler planar codes are sometimes more favorable for implementation on superconducting quantum computers, especially under conditions of high communication congestion.Comment: 14 pages, 9 figures, The 50th Annual IEEE/ACM International Symposium on Microarchitectur

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

A Methodology for Transforming Java Applications Towards Real-Time Performance

The development of real-time systems has traditionally been based on low-level programming languages, such as C and C++, as these provide a fine-grained control of the applications temporal behavior. However, the usage of such programming languages suffers from increased complexity and high error rates compared to high-level languages such as Java. The Java programming language provides many benefits to software development such as automatic memory management and platform independence. However, Java is unable to provide any real-time guarantees, as the high-level benefits come at the cost of unpredictable temporal behavior.This thesis investigates the temporal characteristics of the Java language and analyses several possibilities for introducing real-time guarantees, including official language extensions and commercial runtime environments. Based on this analysis a new methodology is proposed for Transforming Java Applications towards Real-time Performance (TJARP). This method motivates a clear definition of timing requirements, followed by an analysis of the system through use of the formal modeling languageVDM-RT. Finally, the method provides a set of structured guidelines to facilitate the choice of strategy for obtaining real-time performance using Java. To further support this choice, an analysis is presented of available solutions, supported by a simple case study and a series of benchmarks.Furthermore, this thesis applies the TJARP method to a complex industrialcase study provided by a leading supplier of mission critical systems. Thecase study proves how the TJARP method is able to analyze an existing and complex system, and successfully introduce hard real-time guaranteesin critical sub-components