Self-Optimization of Software Defined Radios Through Evolutionary
Algorithms
28th International Teletraffic Congress (ITC 28), Würzburg, Germany, (September 2016)Abstract
This paper presents a framework for building software-defined radios that
are able to self-optimize their parameters using evolutionary algorithms.
The framework has been implemented using the DEAP library for Python, which
is based on the Genetic Algorithms (GAs). The paper discusses the overall
system architecture and presents a system prototype that has been employed
to optimize radio transmission parameters in an unknown radio environment
in order to maximize the achievable throughput. Although GAs have been used
before for optimizing the radio parameters of Software Defined Radios
(SDRs), they have been limited to the number of parameters given as an
input to the GA. The proposed algorithm is much more generic and
comprehensive to utilize the advantages of genetic algorithms, by providing
the flexibility to include any of the parameters of the configuration of
the SDR, which needs to be optimized through the GA. Moreover, the entire
project is based on open-source solutions. The current prototype targets
Iris-based SDRs. However, as the entire software employs standard
components for interfacing the SDR, it can easily be ported to GNU Radio or
other SDR frameworks. We will also present preliminary results that have
been obtained through over-the-air experiments in which we optimized
different power parameters, modulation, coding schemes, etc., in an unknown
radio environment.