58 research outputs found
Laplace Functional Ordering of Point Processes in Large-scale Wireless Networks
Stochastic orders on point processes are partial orders which capture notions
like being larger or more variable. Laplace functional ordering of point
processes is a useful stochastic order for comparing spatial deployments of
wireless networks. It is shown that the ordering of point processes is
preserved under independent operations such as marking, thinning, clustering,
superposition, and random translation. Laplace functional ordering can be used
to establish comparisons of several performance metrics such as coverage
probability, achievable rate, and resource allocation even when closed form
expressions of such metrics are unavailable. Applications in several network
scenarios are also provided where tradeoffs between coverage and interference
as well as fairness and peakyness are studied. Monte-Carlo simulations are used
to supplement our analytical results.Comment: 30 pages, 5 figures, Submitted to Hindawi Wireless Communications and
Mobile Computin
Dynamic Scheduling for Delay Guarantees for Heterogeneous Cognitive Radio Users
We study an uplink multi secondary user (SU) system having statistical delay
constraints, and an average interference constraint to the primary user (PU).
SUs with heterogeneous interference channel statistics, to the PU, experience
heterogeneous delay performances since SUs causing low interference are
scheduled more frequently than those causing high interference. We propose a
scheduling algorithm that can provide arbitrary average delay guarantees to SUs
irrespective of their statistical channel qualities. We derive the algorithm
using the Lyapunov technique and show that it yields bounded queues and satisfy
the interference constraints. Using simulations, we show its superiority over
the Max-Weight algorithm.Comment: Asilomar 2015. arXiv admin note: text overlap with arXiv:1602.0801
Optimal Power Control and Scheduling under Hard Deadline Constraints for Continuous Fading Channels
We consider a joint scheduling-and-power-allocation problem of a downlink
cellular system. The system consists of two groups of users: real-time (RT) and
non-real-time (NRT) users. Given an average power constraint on the base
station, the problem is to find an algorithm that satisfies the RT hard
deadline constraint and NRT queue stability constraint. We propose a
sum-rate-maximizing algorithm that satisfies these constraints. We also show,
through simulations, that the proposed algorithm has an average complexity that
is close-to-linear in the number of RT users. The power allocation policy in
the proposed algorithm has a closed-form expression for the two groups of
users. However, interestingly, the power policy of the RT users differ in
structure from that of the NRT users. We also show the superiority of the
proposed algorithms over existing approaches using extensive simulations.Comment: Submitted to Asilomar 2017. arXiv admin note: text overlap with
arXiv:1612.0832
Distributed Detection over Gaussian Multiple Access Channels with Constant Modulus Signaling
A distributed detection scheme where the sensors transmit with constant
modulus signals over a Gaussian multiple access channel is considered. The
deflection coefficient of the proposed scheme is shown to depend on the
characteristic function of the sensing noise and the error exponent for the
system is derived using large deviation theory. Optimization of the deflection
coefficient and error exponent are considered with respect to a transmission
phase parameter for a variety of sensing noise distributions including
impulsive ones. The proposed scheme is also favorably compared with existing
amplify-and-forward and detect-and-forward schemes. The effect of fading is
shown to be detrimental to the detection performance through a reduction in the
deflection coefficient depending on the fading statistics. Simulations
corroborate that the deflection coefficient and error exponent can be
effectively used to optimize the error probability for a wide variety of
sensing noise distributions.Comment: 30 pages, 12 figure
Robust Distributed Estimation over Multiple Access Channels with Constant Modulus Signaling
A distributed estimation scheme where the sensors transmit with constant
modulus signals over a multiple access channel is considered. The proposed
estimator is shown to be strongly consistent for any sensing noise distribution
in the i.i.d. case both for a per-sensor power constraint, and a total power
constraint. When the distributions of the sensing noise are not identical, a
bound on the variances is shown to establish strong consistency. The estimator
is shown to be asymptotically normal with a variance (AsV) that depends on the
characteristic function of the sensing noise. Optimization of the AsV is
considered with respect to a transmission phase parameter for a variety of
noise distributions exhibiting differing levels of impulsive behavior. The
robustness of the estimator to impulsive sensing noise distributions such as
those with positive excess kurtosis, or those that do not have finite moments
is shown. The proposed estimator is favorably compared with the amplify and
forward scheme under an impulsive noise scenario. The effect of fading is shown
to not affect the consistency of the estimator, but to scale the asymptotic
variance by a constant fading penalty depending on the fading statistics.
Simulations corroborate our analytical results.Comment: 28 pages, 10 figures, submitted to IEEE Transactions on Signal
Processing for consideratio
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