Topics: Area 3: Mobile, wireless and 5G; Area 7: Network measurements and analysis
Authors: Muhammed Zubair Basha Shaik (Technische Universität Ilmenau, Germany); Andre Puschmann and Andreas Mitschele-Thiel (Ilmenau University of Technology, Germany)
Presenter bio: Muhammed Zubair Basha is a Master's student of Computer Science at Technische Universitat Ilmenau.
He has six years of experience in WLAN R&D at Motorola, Qualcomm and Redpine Signals.
Research interests are wireless communications(LTE/5G), MAC layer protocols and Software Defined Radios.
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.