Knowledge of interacting channels is essential for characterizing the performance of a cognitive radio system in terms of interference power received by a primary receiver and throughput at a secondary receiver. Baseline models considered for the performance characterization assume perfect knowledge of the interacting channels. Recently, an analytical framework has been proposed that incorporates channel estimation and subsequently characterizes the performance of cognitive Interweave Systems (ISs). However, the analysis was pertained to the deterministic behaviour of the interacting channels. In this paper, we extend the characterization of the aforementioned framework to investigate the influence of channel fading on the performance of the IS. Our analysis indicate that an inappropriate choice of estimation time can severely degrade the performance of the IS in terms of achievable secondary throughput.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Performance Analysis of Interweave Cognitive Radio Systems with Imperfect Channel Knowledge over Nakagami Fading Channels


    Beteiligte:


    Erscheinungsdatum :

    2016-09-01


    Format / Umfang :

    184260 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Performance Analysis of Physical Layer Security over Rician/Nakagami-m Fading Channels

    Iwata, Shunya / Ohtsuki, Tomoaki / Kam, P.-Y. | IEEE | 2017


    Performance Analysis of PLC over Fading Channels with Colored Nakagami-m Background Noise

    Ai, Yun / Ohtsuki, Tomoaki / Cheffena, Michael | IEEE | 2017


    Co-channel interference of microcellular systems in shadowed Nakagami fading channels

    Ho,M.J. / Stueber,G.L. / Georgia Inst.of Technol.,School of Electrical Engng.,US | Kraftfahrwesen | 1993


    Outage Performance of Active RIS in NOMA Networks over Nakagami-m Fading Channels

    Song, Meiqi / Yue, Xinwei / Ouyang, Chongjun et al. | IEEE | 2023