A Lumpable Finite-State Markov Model for Channel Prediction and Resource Allocation in OFDMA Systems

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This paper presents novel closed-form solutions to the sub-channel and power allocation problems of an orthogonal frequency division multiple access (OFDMA) system. We model the Rayleigh fading channel as a finite-state Markov channel (FSMC) by partitioning the received signal-to-noise ratio (SNR) into several intervals. We use the sub-band formation and lumpability to reduce the size of channel state information (CSI) and to reliably predict the CSI with the corresponding state transition and steady-state probabilities. Simulation results show that the limited feedback scheme due to lumpable FSMC is not only experiencing less prediction error than the typical full feedback scheme but also achieving near-optimum capacity.
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