154,692 research outputs found
Minimizing memory effects in OFDM transmitters using adaptive baseband equalization
This paper presents a simple and effective approach for eliminating memory effects in OFDM transmitters. It uses advantages of OFDM systems to provide pre-compensation of the frequency-dependent distortions, which are results of the power amplifiers (PA) memory effects. The process of memory effects quantification is carried out in this paper by obtaining a frequency-dependent PA gain, phase shift and intermodulation products. The memory effects are eliminated at baseband using equalization of the IDFT signal. Implementation of the equalization procedure at baseband makes the process of minimizing memory effects simple and effective, because no additional RF components or feedback loops are used. Memory effects are compensated in DSP part using simple multiplication of the frequency-domain digital signal by coefficients, which are calculated adaptively for each OFDM sub-carrier frequency and input power. The approach is tested with Motorola MOSFET MRF9742 power amplifier model in Advanced Design System (ADS). Simulations show significant improvement in minimizing memory effects. Received constellation of the 16-QAM OFDM signal after implementing baseband pre-compensation technique looks alike ideal one, whereas without pre-compensation it shows high dispersion due to the presence of PA memory
Beyond the socket: NUMA-aware GPUs
GPUs achieve high throughput and power efficiency by employing many small single instruction multiple thread (SIMT) cores. To minimize scheduling logic and performance variance they utilize a uniform memory system and leverage strong data parallelism exposed via the programming model. With Moore's law slowing, for GPUs to continue scaling performance (which largely depends on SIMT core count) they are likely to embrace multi-socket designs where transistors are more readily available. However when moving to such designs, maintaining the illusion of a uniform memory system is increasingly difficult. In this work we investigate multi-socket non-uniform memory access (NUMA) GPU designs and show that significant changes are needed to both the GPU interconnect and cache architectures to achieve performance scalability. We show that application phase effects can be exploited allowing GPU sockets to dynamically optimize their individual interconnect and cache policies, minimizing the impact of NUMA effects. Our NUMA-aware GPU outperforms a single GPU by 1.5×, 2.3×, and 3.2× while achieving 89%, 84%, and 76% of theoretical application scalability in 2, 4, and 8 sockets designs respectively. Implementable today, NUMA-aware multi-socket GPUs may be a promising candidate for scaling GPU performance beyond a single socket.We would like to thank anonymous reviewers and Steve Keckler for their help in improving this paper. The first author is supported by
the Ministry of Economy and Competitiveness of Spain (TIN2012-34557, TIN2015-65316-P, and BES-2013-063925)Peer ReviewedPostprint (published version
An efficient genetic algorithm for large-scale planning of robust industrial wireless networks
An industrial indoor environment is harsh for wireless communications
compared to an office environment, because the prevalent metal easily causes
shadowing effects and affects the availability of an industrial wireless local
area network (IWLAN). On the one hand, it is costly, time-consuming, and
ineffective to perform trial-and-error manual deployment of wireless nodes. On
the other hand, the existing wireless planning tools only focus on office
environments such that it is hard to plan IWLANs due to the larger problem size
and the deployed IWLANs are vulnerable to prevalent shadowing effects in harsh
industrial indoor environments. To fill this gap, this paper proposes an
overdimensioning model and a genetic algorithm based over-dimensioning (GAOD)
algorithm for deploying large-scale robust IWLANs. As a progress beyond the
state-of-the-art wireless planning, two full coverage layers are created. The
second coverage layer serves as redundancy in case of shadowing. Meanwhile, the
deployment cost is reduced by minimizing the number of access points (APs); the
hard constraint of minimal inter-AP spatial paration avoids multiple APs
covering the same area to be simultaneously shadowed by the same obstacle. The
computation time and occupied memory are dedicatedly considered in the design
of GAOD for large-scale optimization. A greedy heuristic based
over-dimensioning (GHOD) algorithm and a random OD algorithm are taken as
benchmarks. In two vehicle manufacturers with a small and large indoor
environment, GAOD outperformed GHOD with up to 20% less APs, while GHOD
outputted up to 25% less APs than a random OD algorithm. Furthermore, the
effectiveness of this model and GAOD was experimentally validated with a real
deployment system
Memory-based adaptive sliding mode load frequency control in interconnected power systems with energy storage
This paper presents a memory-based adaptive sliding mode load frequency control (LFC) strategy aimed at minimizing the impacts of exogenous power disturbances and parameter uncertainties on frequency deviations in interconnected power systems with energy storage. First, the dynamic model of the system is constructed by considering the participation of the energy storage system (ESS) in the conventional decentralized LFC model of a multiarea power system. A disturbance observer (DOB) is proposed to generate an online approximation of the lumped disturbance. In order to enhance the transient performance of the system and effectively mitigate the adverse effects of power fluctuations on grid frequency, a novel memory-based sliding surface is developed. This sliding surface incorporates the estimation of the lumped disturbance, as well as the past and present information of the state variables. The conservative assumption about the lumped disturbance is eased by considering the unknown upper bound of the disturbance and its first derivative. Based on the output of the proposed DOB, an adaptive continuous sliding mode controller with prescribed H performance index is introduced. This controller ensures that the sliding surface is reachable and guarantees asymptotic stability of the closed-loop system. The controller design utilizes strict linear matrix inequalities (LMIs) to achieve these objectives. Finally, the applicability and efficacy of the proposed scheme are verified through comparative simulation cases. Autho
Best-fit quasi-equilibrium ensembles: a general approach to statistical closure of underresolved Hamiltonian dynamics
A new method of deriving reduced models of Hamiltonian dynamical systems is
developed using techniques from optimization and statistical estimation. Given
a set of resolved variables that define a model reduction, the
quasi-equilibrium ensembles associated with the resolved variables are employed
as a family of trial probability densities on phase space. The residual that
results from submitting these trial densities to the Liouville equation is
quantified by an ensemble-averaged cost function related to the information
loss rate of the reduction. From an initial nonequilibrium state, the
statistical state of the system at any later time is estimated by minimizing
the time integral of the cost function over paths of trial densities.
Statistical closure of the underresolved dynamics is obtained at the level of
the value function, which equals the optimal cost of reduction with respect to
the resolved variables, and the evolution of the estimated statistical state is
deduced from the Hamilton-Jacobi equation satisfied by the value function. In
the near-equilibrium regime, or under a local quadratic approximation in the
far-from-equilibrium regime, this best-fit closure is governed by a
differential equation for the estimated state vector coupled to a Riccati
differential equation for the Hessian matrix of the value function. Since
memory effects are not explicitly included in the trial densities, a single
adjustable parameter is introduced into the cost function to capture a
time-scale ratio between resolved and unresolved motions. Apart from this
parameter, the closed equations for the resolved variables are completely
determined by the underlying deterministic dynamics
Stochastic Digital Backpropagation with Residual Memory Compensation
Stochastic digital backpropagation (SDBP) is an extension of digital
backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP
takes into account noise from the optical amplifiers in addition to handling
deterministic linear and nonlinear impairments. The decisions in SDBP are taken
on a symbol-by-symbol (SBS) basis, ignoring any residual memory, which may be
present due to non-optimal processing in SDBP. In this paper, we extend SDBP to
account for memory between symbols. In particular, two different methods are
proposed: a Viterbi algorithm (VA) and a decision directed approach. Symbol
error rate (SER) for memory-based SDBP is significantly lower than the
previously proposed SBS-SDBP. For inline dispersion-managed links, the VA-SDBP
has up to 10 and 14 times lower SER than DBP for QPSK and 16-QAM, respectively.Comment: 7 pages, accepted to publication in 'Journal of Lightwave Technology
(JLT)
Empirical Evaluation of the Parallel Distribution Sweeping Framework on Multicore Architectures
In this paper, we perform an empirical evaluation of the Parallel External
Memory (PEM) model in the context of geometric problems. In particular, we
implement the parallel distribution sweeping framework of Ajwani, Sitchinava
and Zeh to solve batched 1-dimensional stabbing max problem. While modern
processors consist of sophisticated memory systems (multiple levels of caches,
set associativity, TLB, prefetching), we empirically show that algorithms
designed in simple models, that focus on minimizing the I/O transfers between
shared memory and single level cache, can lead to efficient software on current
multicore architectures. Our implementation exhibits significantly fewer
accesses to slow DRAM and, therefore, outperforms traditional approaches based
on plane sweep and two-way divide and conquer.Comment: Longer version of ESA'13 pape
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
Effective equilibrium picture in model with exponentially correlated noise
We study the effect of exponentially correlated noise on model in the
limit of small correlation time discussing the order-disorder transition in
mean-field and the topological transition in two dimensions. We map the steady
states of the non-equilibrium dynamics into an effective equilibrium theory. In
mean-field, the critical temperature increases with the noise correlation time
indicating that memory effects promote ordering. This finding is
confirmed by numerical simulations. The topological transition temperature in
two dimensions remains untouched. However, finite size effects induce a
crossover in the vortices proliferation that is confirmed by numerical
simulations
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