411 research outputs found
Selection Relaying at Low Signal to Noise Ratios
Performance of cooperative diversity schemes at Low Signal to Noise Ratios
(LSNR) was recently studied by Avestimehr et. al. [1] who emphasized the
importance of diversity gain over multiplexing gain at low SNRs. It has also
been pointed out that continuous energy transfer to the channel is necessary
for achieving the max-flow min-cut bound at LSNR. Motivated by this we propose
the use of Selection Decode and Forward (SDF) at LSNR and analyze its
performance in terms of the outage probability. We also propose an energy
optimization scheme which further brings down the outage probability
Dominant block guided optimal cache size estimation to maximize IPC of embedded software
Embedded system software is highly constrained from performance, memory
footprint, energy consumption and implementing cost view point. It is always
desirable to obtain better Instructions per Cycle. Instruction cache has major
contribution in improving IPC. Cache memories are realized on the same chip
where the processor is running. This considerably increases the system cost as
well. Hence, it is required to maintain a trade off between cache sizes and
performance improvement offered. Determining the number of cache lines and size
of cache line are important parameters for cache designing. The design space
for cache is quite large. It is time taking to execute the given application
with different cache sizes on an instruction set simulator to figure out the
optimal cache size. In this paper, a technique is proposed to identify a number
of cache lines and cache line size for the L1 instruction cache that will offer
best or nearly best IPC. Cache size is derived, at a higher abstraction level,
from basic block analysis in the Low Level Virtual Machine environment. The
cache size estimated is cross validated by simulating the set of benchmark
applications with different cache sizes in simple scalar simulator. The
proposed method seems to be superior in terms of estimation accuracy and
estimation time as compared to the existing methods for estimation of optimal
cache size parameters like cache line size, number of cache lines.Comment: 10 Pages, 4 Figures, 5 Tables, International Journal of Embedded
Systems and Applications (IJESA).
http://airccse.org/journal/ijesa/current2013.htm
Dynamic Network Cartography
Communication networks have evolved from specialized, research and tactical
transmission systems to large-scale and highly complex interconnections of
intelligent devices, increasingly becoming more commercial, consumer-oriented,
and heterogeneous. Propelled by emergent social networking services and
high-definition streaming platforms, network traffic has grown explosively
thanks to the advances in processing speed and storage capacity of
state-of-the-art communication technologies. As "netizens" demand a seamless
networking experience that entails not only higher speeds, but also resilience
and robustness to failures and malicious cyber-attacks, ample opportunities for
signal processing (SP) research arise. The vision is for ubiquitous smart
network devices to enable data-driven statistical learning algorithms for
distributed, robust, and online network operation and management, adaptable to
the dynamically-evolving network landscape with minimal need for human
intervention. The present paper aims at delineating the analytical background
and the relevance of SP tools to dynamic network monitoring, introducing the SP
readership to the concept of dynamic network cartography -- a framework to
construct maps of the dynamic network state in an efficient and scalable manner
tailored to large-scale heterogeneous networks.Comment: To appear in the IEEE Signal Processing Magazine - Special Issue on
Adaptation and Learning over Complex Network
Particle-in-cell simulation of Buneman instability beyond quasilinear saturation
Spatio-temporal evolution of Buneman instability has been followed
numerically till its quasilinear quenching and beyond, using an in-house
developed electrostatic 1D particle-in-cell simulation code. For different
initial drift velocities and for
a wide range of electron to ion mass ratios (m/M), growth rate obtained from
simulation agrees well with the numerical solution of the fourth order
dispersion relation. Quasi-linear saturation of Buneman instability occurs when
ratio of electrostatic field energy density () to initial electron drift kinetic energy density () reaches up to a constant value, which as
predicted by Hirose [Plasma Physics 20, 481(1978)], is independent of initial
electron drift velocity but depends on electron to ion mass ratio m/M as
. This result
stands verified in our simulations. Growth of the instability beyond the first
saturation (quasilinear saturation ) till its final saturation [Ishihara et.
al., PRL 44, 1404(1980)] follows an algebraic scaling with time. In contrast to
the quasilinear saturation, the ratio of final saturated electrostatic field
energy density to initial kinetic energy density, is relatively independent of
electron to ion mass ratio and is found to depend only on the initial drift
velocity. Beyond the final saturation, electron phase space holes coupled to
large amplitude ion solitary waves, a state known as coupled hole-soliton , are
seen in our simulations. The propagation characteristics ( amplitude - speed
relation ) of these coherent modes is found to be consistent with the theory of
Saeki et. al. [PRL 80, 1224(1998)]
Asynchronous Incremental Stochastic Dual Descent Algorithm for Network Resource Allocation
Stochastic network optimization problems entail finding resource allocation
policies that are optimum on an average but must be designed in an online
fashion. Such problems are ubiquitous in communication networks, where
resources such as energy and bandwidth are divided among nodes to satisfy
certain long-term objectives. This paper proposes an asynchronous incremental
dual decent resource allocation algorithm that utilizes delayed stochastic
{gradients} for carrying out its updates. The proposed algorithm is well-suited
to heterogeneous networks as it allows the computationally-challenged or
energy-starved nodes to, at times, postpone the updates. The asymptotic
analysis of the proposed algorithm is carried out, establishing dual
convergence under both, constant and diminishing step sizes. It is also shown
that with constant step size, the proposed resource allocation policy is
asymptotically near-optimal. An application involving multi-cell coordinated
beamforming is detailed, demonstrating the usefulness of the proposed
algorithm
Asynchronous Optimization Over Heterogeneous Networks via Consensus ADMM
This paper considers the distributed optimization of a sum of locally
observable, non-convex functions. The optimization is performed over a
multi-agent networked system, and each local function depends only on a subset
of the variables. An asynchronous and distributed alternating directions method
of multipliers (ADMM) method that allows the nodes to defer or skip the
computation and transmission of updates is proposed in the paper. The proposed
algorithm utilizes different approximations in the update step, resulting in
proximal and majorized ADMM variants. Both variants are shown to converge to a
local minimum, under certain regularity conditions. The proposed asynchronous
algorithms are also applied to the problem of cooperative localization in
wireless ad hoc networks, where it is shown to outperform the other
state-of-the-art localization algorithms.Comment: Submitted to Transactions on signal and information processing over
Network
Network Resource Allocation via Stochastic Subgradient Descent: Convergence Rate
This paper considers a general stochastic resource allocation problem that
arises widely in wireless networks, cognitive radio, networks, smart-grid
communications, and cross-layer design. The problem formulation involves
expectations with respect to a collection of random variables with unknown
distributions, representing exogenous quantities such as channel gain, user
density, or spectrum occupancy. We consider the constant step-size stochastic
dual subgradient descent (SDSD) method that has been widely used for online
resource allocation in networks. The problem is solved in dual domain which
results in a primal resource allocation subproblem at each time instant. The
goal here is to characterize the non-asymptotic behavior of such stochastic
resource allocations in an almost sure sense.
It is well known that with a step size of , {SDSD} converges to an
-sized neighborhood of the optimum. In practice however,
there exists a trade-off between the rate of convergence and the choice of
. This paper establishes a convergence rate result for the SDSD
algorithm that precisely characterizes this trade-off. {Towards this end, a
novel stochastic bound on the gap between the objective function and the
optimum is developed. The asymptotic behavior of the stochastic term is
characterized in an almost sure sense, thereby generalizing the existing
results for the {stochastic subgradient} methods.} For the stochastic resource
allocation problem at hand, the result explicates the rate with which the
allocated resources become near-optimal. As an application, the power and
user-allocation problem in device-to-device networks is formulated and solved
using the {SDSD} algorithm. Further intuition on the rate results is obtained
from the verification of the regularity conditions and accompanying simulation
results
Decentralized Multi-Antenna Coded Caching with Cyclic Exchanges
This paper considers a single cell multi-antenna base station delivering
content to multiple cache enabled single-antenna users. Coding strategies are
developed that allow for decentralized placement in the wireless setting. Three
different cases namely, max-min multicasting, linear combinations in the
complex field, and linear combinations in the finite field, are considered and
closed-form rate expressions are provided that hold with high probability. For
the case of max-min fair multicasting delivery, we propose a new coding scheme
that is capable of working with only two-user broadcasts. A cyclic-exchange
protocol for efficient content delivery is proposed and shown to perform almost
as well as the original multi-user broadcast scheme.Comment: Accepted in 56th Annual Allerton Conference 2018 on Communication,
Control, and Computing at UIUC, IL,US
Dynamic Network Delay Cartography
Path delays in IP networks are important metrics, required by network
operators for assessment, planning, and fault diagnosis. Monitoring delays of
all source-destination pairs in a large network is however challenging and
wasteful of resources. The present paper advocates a spatio-temporal Kalman
filtering approach to construct network-wide delay maps using measurements on
only a few paths. The proposed network cartography framework allows efficient
tracking and prediction of delays by relying on both topological as well as
historical data. Optimal paths for delay measurement are selected in an online
fashion by leveraging the notion of submodularity. The resulting predictor is
optimal in the class of linear predictors, and outperforms competing
alternatives on real-world datasets.Comment: Part of this paper has been published in the \emph{IEEE Statistical
Signal Processing Workshop}, Ann Arbor, MI, Aug. 201
Asynchronous Decentralized Stochastic Optimization in Heterogeneous Networks
We consider expected risk minimization in multi-agent systems comprised of
distinct subsets of agents operating without a common time-scale. Each
individual in the network is charged with minimizing the global objective
function, which is an average of sum of the statistical average loss function
of each agent in the network. Since agents are not assumed to observe data from
identical distributions, the hypothesis that all agents seek a common action is
violated, and thus the hypothesis upon which consensus constraints are
formulated is violated. Thus, we consider nonlinear network proximity
constraints which incentivize nearby nodes to make decisions which are close to
one another but not necessarily coincide. Moreover, agents are not assumed to
receive their sequentially arriving observations on a common time index, and
thus seek to learn in an asynchronous manner. An asynchronous stochastic
variant of the Arrow-Hurwicz saddle point method is proposed to solve this
problem which operates by alternating primal stochastic descent steps and
Lagrange multiplier updates which penalize the discrepancies between agents.
This tool leads to an implementation that allows for each agent to operate
asynchronously with local information only and message passing with neighbors.
Our main result establishes that the proposed method yields convergence in
expectation both in terms of the primal sub-optimality and constraint violation
to radii of sizes and ,
respectively. Empirical evaluation on an asynchronously operating wireless
network that manages user channel interference through an adaptive
communications pricing mechanism demonstrates that our theoretical results
translates well to practice
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