Channel quality parameters are needed to achieve efficient utilization of radio resources. These parameters include channel amplitude gain, channel frequency offset, channel random phase, channel multipath as well as channel noise variance. Usually, signal-to-noise ratio (SNR) at the demodulator output is used as channel quality indicator to link bit error rate (BER). In applications such as power control, handoff, routing and adaptive transmission, SNR based channel quality is in practical success. Previous works are mainly for mobile or low speed vehicles, in the situation of slow varying channels. For airborne applications, wireless channel is highly dynamic. Block fading assumption may not be valid in this situation and channel parameters have to be estimated by a symbol by symbol fashion. In addition, incorporating convolution code may improve the system performance at the expense of more complexity. In this paper, we propose a channel quality estimation method to accurately indicate link BER. This method consists of two steps. The first step of the estimator is a channel amplitude gain and phase tracker. The second step of the estimator is a channel quality indicator based on signal distance between received signal and the maximum likelihood path along the decoder trellis. The distance measurements are generated by re-encoding the decoder output and comparing with the decoder input. A time-varying Wiener-Hopf equation is established in description of channel estimator, in which the solution is given by the correlation matrix of filter input and the cross correlation vector between filter input and desired response. A Maximum a Posteriori (MAP) estimate and a Minimum Mean Square Error (MMSE) estimate of receiver SNR given channel state information and signal distance measurements are derived.


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    Title :

    Channel quality estimation with MMSE filter and viterbi decoding for airborne communications


    Contributors:
    Tao Chen (author) / Bo Chen (author) / Dinghai Xu (author) / Yongfei Ding (author) / Ruifan Pang (author) / Cheng Gong (author) / Hengyang Zhang (author)


    Publication date :

    2014-04-01


    Size :

    372439 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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