A Learning Approach for Low-Complexity Optimization of Energy Efficiency in Multicarrier Wireless Networks - 2018


This Project proposes computationally efficient algorithms to maximise the energy efficiency in multicarrier 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 world energy potency subject to both maximum power and minimum rate constraints. This ends up in a difficult nonconvex fractional downside, that is tackled through an interplay of fractional programming, learning, and game theory. The proposed algorithmic framework is provably convergent and includes a complexity linear in each the number of users and subcarriers, whereas other accessible solutions will solely guarantee a polynomial complexity in the number of users and subcarriers. Numerical results show that the proposed methodology performs similarly as alternative, a lot of complicated, algorithms.

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