Abstract
Contemporary wireless communication standards, such as the long term evolution (LTE) standard, exploit several techniques, including link adaptation and frequency selective scheduling (FSS), to offer high data rate services. The efficacy of these techniques rely on the evolved Node B (eNB) having accurate channel state information through the use of a high signaling overhead process whereby channel quality indicator (CQI) feedback reports are sent by the user equipment (UE) to the eNB. In this work, we exploit a machine learning technique to address this problem and propose a novel sub-band CQI feedback compression scheme based on support vector machines to reduce this signaling overhead. The proposed compression scheme was implemented and tested in an LTE system level simulator and has shown efficacy with an overall CQI feedback signaling reduction of up to 88.7% whilst maintaining stable sector throughput, when compared to the standard third generation partnership project (3GPP) CQI feedback mechanism.
Published Version
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