1,199 research outputs found
Distributed stochastic optimization via matrix exponential learning
In this paper, we investigate a distributed learning scheme for a broad class
of stochastic optimization problems and games that arise in signal processing
and wireless communications. The proposed algorithm relies on the method of
matrix exponential learning (MXL) and only requires locally computable gradient
observations that are possibly imperfect and/or obsolete. To analyze it, we
introduce the notion of a stable Nash equilibrium and we show that the
algorithm is globally convergent to such equilibria - or locally convergent
when an equilibrium is only locally stable. We also derive an explicit linear
bound for the algorithm's convergence speed, which remains valid under
measurement errors and uncertainty of arbitrarily high variance. To validate
our theoretical analysis, we test the algorithm in realistic
multi-carrier/multiple-antenna wireless scenarios where several users seek to
maximize their energy efficiency. Our results show that learning allows users
to attain a net increase between 100% and 500% in energy efficiency, even under
very high uncertainty.Comment: 31 pages, 3 figure
Interference Coordination via Power Domain Channel Estimation
A novel technique is proposed which enables each transmitter to acquire
global channel state information (CSI) from the sole knowledge of individual
received signal power measurements, which makes dedicated feedback or
inter-transmitter signaling channels unnecessary. To make this possible, we
resort to a completely new technique whose key idea is to exploit the transmit
power levels as symbols to embed information and the observed interference as a
communication channel the transmitters can use to exchange coordination
information. Although the used technique allows any kind of {low-rate}
information to be exchanged among the transmitters, the focus here is to
exchange local CSI. The proposed procedure also comprises a phase which allows
local CSI to be estimated. Once an estimate of global CSI is acquired by the
transmitters, it can be used to optimize any utility function which depends on
it. While algorithms which use the same type of measurements such as the
iterative water-filling algorithm (IWFA) implement the sequential best-response
dynamics (BRD) applied to individual utilities, here, thanks to the
availability of global CSI, the BRD can be applied to the sum-utility.
Extensive numerical results show that significant gains can be obtained and,
this, by requiring no additional online signaling
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