Cognitive Radio Oriented Wireless Networks. 10th International Conference, CROWNCOM 2015, Doha, Qatar, April 21–23, 2015, Revised Selected Papers

Research Article

Power Control in Cognitive Radio Networks Using Cooperative Modulation and Coding Classification

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  • @INPROCEEDINGS{10.1007/978-3-319-24540-9_29,
        author={Anestis Tsakmalis and Symeon Chatzinotas and Bj\o{}rn Ottersten},
        title={Power Control in Cognitive Radio Networks Using Cooperative Modulation and Coding Classification},
        proceedings={Cognitive Radio Oriented Wireless Networks. 10th International Conference, CROWNCOM 2015, Doha, Qatar, April 21--23, 2015, Revised Selected Papers},
        proceedings_a={CROWNCOM},
        year={2015},
        month={10},
        keywords={Cognitive Radio Centralized power control Spectrum sensing Cooperative Modulation and Coding Classification Adaptive coding and modulation},
        doi={10.1007/978-3-319-24540-9_29}
    }
    
  • Anestis Tsakmalis
    Symeon Chatzinotas
    Björn Ottersten
    Year: 2015
    Power Control in Cognitive Radio Networks Using Cooperative Modulation and Coding Classification
    CROWNCOM
    Springer
    DOI: 10.1007/978-3-319-24540-9_29
Anestis Tsakmalis1,*, Symeon Chatzinotas1,*, Björn Ottersten1,*
  • 1: University of Luxembourg
*Contact email: anestis.tsakmalis@uni.lu, symeon.chatzinotas@uni.lu, bjorn.ottersten@uni.lu

Abstract

In this paper, a centralized Power Control (PC) scheme aided by interference channel gain learning is proposed to allow a Cognitive Radio (CR) network to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The main idea is the CR network to constantly probe the band of the PU with intelligently designed aggregated interference and sense whether the Modulation and Coding scheme (MCS) of the PU changes in order to learn the interference channels. The coordinated probing is engineered by the Cognitive Base Station (CBS), which assigns appropriate CR power levels in a binary search way. Subsequently, each CR applies a Modulation and Coding Classification (MCC) technique and sends the sensing information through a control channel to the CBS, where all the MCC information is combined using a fusion rule to acquire an MCS estimate of higher accuracy and monitor the probing impact to the PU MCS. After learning the normalized interference channel gains towards the PU, the CBS selects the CR power levels to maximize total CR network throughput while preserving the PU MCS and thus its QoS. The effectiveness of the proposed technique is demonstrated through numerical simulations.