TRIAL Workshop on Cognitive Radio Testbeds

Research Article

Digital Post-Processing Based Wideband Receiver Linearization for Enhanced Spectrum Sensing and Access

Download821 downloads
  • @INPROCEEDINGS{10.4108/icst.crowncom.2014.255428,
        author={Markus Allen and Jaakko Marttila and Mikko Valkama and Michael Grimm and Reiner Thom\aa{}},
        title={Digital Post-Processing Based Wideband Receiver Linearization for Enhanced Spectrum Sensing and Access},
        proceedings={TRIAL Workshop on Cognitive Radio Testbeds},
        publisher={IEEE},
        proceedings_a={TRIAL WORKSHOP},
        year={2014},
        month={7},
        keywords={cognitive radio interference cancellation inverse modeling nonlinear distortion spectrum access spectrum sensing},
        doi={10.4108/icst.crowncom.2014.255428}
    }
    
  • Markus Allen
    Jaakko Marttila
    Mikko Valkama
    Michael Grimm
    Reiner Thomä
    Year: 2014
    Digital Post-Processing Based Wideband Receiver Linearization for Enhanced Spectrum Sensing and Access
    TRIAL WORKSHOP
    ICST
    DOI: 10.4108/icst.crowncom.2014.255428
Markus Allen1, Jaakko Marttila1, Mikko Valkama1,*, Michael Grimm2, Reiner Thomä2
  • 1: Tampere University of Technology, Finland
  • 2: Ilmenau University of Technology, Germany
*Contact email: mikko.e.valkama@tut.fi

Abstract

Wideband radio receivers provide the flexibility desired in many communications applications and are the key element in cognitive radios and software-defined radios in general. However, multi-channel reception scenarios tend to have high dynamic range which set hard-to-reach requirements for receiver linearity. This paper proposes a calibration-based digital postinverse model for wideband receiver linearization and compares it with adaptive interference cancellation. Their advantages and disadvantages are highlighted together with numerical performance results in challenging non-contiguous spectrum access scenario. Both methods are waveform-independent, which make them applicable to many systems, but they have different tradeoffs when linearization accuracy, computational complexity and real-time capability are compared. Therefore selecting the best method is highly system-specific matter.