User mobility has become a key attribute in the design of optimal resource allocation policies for future wireless networks. This has become increasingly apparent in cognitive radio (CR) systems where the licensed, primary users (PU) of the network must be protected from harmful interference by the network's opportunistic, secondary users (SU): here, unpredictability due to mobility requires the implementation of safety net mechanisms that are provably capable of adapting to changes in the users' wireless environment. In this context, we propose a distributed learning algorithm that allows SUs to adjust their power allocation profile (over the available frequency carriers) ``on the fly'', relying only on strictly causal channel state information. To account for the interference caused to the network's PUs, we incorporate a penalty function in the rate-driven objectives of the SUs, and we show that the proposed scheme matches asymptotically the performance of the best fixed power allocation policy in hindsight. Specifically, in a system with S orthogonal subcarriers and transmission horizon T, this performance gap (known as the algorithm's average regret) is bounded from above as ο(T^{-1} log(S)). We also validate our theoretical analysis with numerical simulations which confirm that the network's SUs rapidly achieve a ``no-regret'' state under realistic wireless cellular conditions. Moreover, by finetuning the choice of penalty function, the interference induced by the SUs can be kept at a sufficiently low level, thus guaranteeing the PUs' requirements.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Online Power Allocation for Opportunistic Radio Access in Dynamic OFDM Networks




    Publication date :

    2016-09-01


    Size :

    158411 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Power Allocation Using Geometric Water Filling for OFDM-Based Cognitive Radio Networks

    Sultana, Ajmery / Zhao, Lian / Fernando, Xavier | IEEE | 2016


    Joint Clusterization and Power Allocation for Cloud Radio Access Networks

    Tsou, Yao-Chun / Li, Pei-Rong / Chu, Jui-Hung et al. | IEEE | 2015



    Perron-Frobenius Theory Based Power Allocation in Heterogeneous Cloud Radio Access Networks

    Zhang, Kecheng / Peng, Mugen / Wang, Chonggang et al. | IEEE | 2015


    Resource Allocation and Power Control for Power Minimization in OFDM Networks

    Yang, Zhaohui / Pan, Cunhua / Chen, Ming et al. | IEEE | 2017