Adaptive Link Adaptation Algorithm in High Speed Downlink Packet Access (HSDPA) System
Date Issued
2006
Date
2006
Author(s)
Zeng, Ming-xiang
DOI
zh-TW
Abstract
In HSDPA system, a set of enhanced technique have been proposed to improve the service performance in proportion to the UMTS (Universal
Mobile Telecommunication System), and the primary reason that HSDPA could improve the peak rate of packet transmission up to 10Mbps is that the HSDPA utilize the AMC (Adaptive Modulation and Coding) as its link adaptation. The function of AMC is that Node B selects the variable MCSs (Modulation and Coding Schemes) to transmit the packet according to the changed channel conditions, if the packet transmits with the correct MCS then it will come out with a consequence that the throughput will be upgraded and the packet error rate and the delay requirement will be degraded, so the threshold of the MCSs is one of the most important factors that would affect the performance in HSDPA system. The optimal threshold depends on the user’s wireless channel environments. However, the channel condition is time-varying and it is difficult to decide optimal threshold in advance. If the user is in the worse channel condition but utilizes the thresholds suited to the better channel condition, then its throughput will be degraded, vice versa. In this thesis, we tackle this problem by a context-aware learning-based optimization approach. A context-aware framework is designed to learn the channel conditions on-line and optimize the threshold adaptively with the goal to maximize the overall throughput. In our approach, first, the node B collects the knowledge of the channel conditions and the information of the packet successful received or not those are feedback from users and uses these obtained knowledge and the capability of neural networks to learn the complex nonlinear function among the throughput, the threshold and the SNR (signal to noise rations), after acquiring the complex nonlinear function, we take advantage of it to get the aggregated throughput in certain SNR region. Then we use the capability of the neural network again to learn the complex nonlinear function between the aggregated throughput and the threshold and adjust the threshold adaptively to reach an optimal value according to the gradient which is derived from the aggregated throughput and the threshold. The contribution of this thesis is that we propose an approach that is applicable to the real situations to adaptively regulate the threshold to reach the optimal value and maximize the overall throughputs. The simulation results show that our approach can deal with the time-varying wireless channel conditions and improve the throughputs.
Subjects
適應性調變編碼
連結調整
類神經網路
HSDPA
AMC
Link Adaptation
Neural Network
Type
thesis
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