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DS-UWB의 스루풋을 증가시키기위한 가변길이의 training sequence 를 가진 Noble 등화기구조
정세명,임명섭,김은정,렌진 한국통신학회 2009 韓國通信學會論文誌 Vol.34 No.1
The training sequence with the appropriate length for equalization and initial synchronization is necessary before sending the pure data in the burst transmission type DS-UWB system. The length of the training sequence is one of the factors which make throughput decreased. The noble structure with the variable length of the training sequence whose length can be adaptively tailored according to the channel conditions (CM1,CM2,CM3,CM4) in the DS-USB systems is proposed. This structure can increase the throughput without sacrificing the performance than the method with fixed length of training sequence considering the worst case channel conditions. Simulation results under IEEE 802.15.3a channel model show that the proposed scheme can achieve higher throughput than a conventional one with the slight loss of BER performance. And this structure can reduce the computation complexity and power consumption with selecting the short length of the training sequence.
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Se-Myoung Chung(정세명),Eun-Jung Kim(김은정),Ren-Jin(렌진),Myoung-Seob Lim(임명섭) 한국통신학회 2009 韓國通信學會論文誌 Vol.34 No.1C
The training sequence with the appropriate length for equalization and initial synchronization is necessary before sending the pure data in the burst transmission type DS-UWB system. The length of the training sequence is one of the factors which make throughput decreased. The noble structure with the variable length of the training sequence whose length can be adaptively tailored according to the channel conditions (CM1,CM2,CM3,CM4) in the DS-USB systems is proposed. This structure can increase the throughput without sacrificing the performance than the method with fixed length of training sequence considering the worst case channel conditions. Simulation results under IEEE 802.15.3a channel model show that the proposed scheme can achieve higher throughput than a conventional one with the slight loss of BER performance. And this structure can reduce the computation complexity and power consumption with selecting the short length of the training sequence.
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