9,979 research outputs found
A Learning Approach for Low-Complexity Optimization of Energy Efficiency in Multi-Carrier Wireless Networks
This paper proposes computationally efficient algorithms to maximize the
energy efficiency in multi-carrier wireless interference networks, by a
suitable allocation of the system radio resources, namely the transmit powers
and subcarrier assignment. The problem is formulated as the maximization of the
system Global Energy-Efficiency subject to both maximum power and minimum rate
constraints. This leads to a challenging non-convex fractional problem, which
is tackled through an interplay of fractional programming, learning, and game
theory. The proposed algorithmic framework is provably convergent and has a
complexity linear in both the number of users and subcarriers, whereas other
available solutions can only guarantee a polynomial complexity in the number of
users and subcarriers. Numerical results show that the proposed method performs
similarly as other, more complex, algorithms
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
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