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Throughput Guaranteed Handoff for SDN-Based WLAN in Distinctive Signal Coverage

机译:信号覆盖范围内基于SDN的WLAN的吞吐量保证切换

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In SDN-based WLAN, controller needs to collect the state info of Mobile Nodes (MNs) like received signal strength indicator (RSSI) for handoff. In such scenarios, high sampling rate facilitates handoff, but it also easily leads to system overhead thus limits access scale of MN. Besides, dynamical adjustment of transmit power of access point (AP) leads to the distinctive signal coverage. Existing handoff algorithms that directly use the uplink RSSI as handoff condition would result in significant throughput decay of MN. Also in indoor deployment RSSI may varies much, large variation of RSSI result in unstable handoff. To address the issues, we design a variable sampling rate mechanism, then filter sampling RSSI and propose a handoff algorithm for distinctive signal coverage scenarios. Our sampling mechanism uses a finite state machine (FSM) to adjust the sampling rate by MN on all nearby APs. Our handoff algorithm uses Kalman filter to achieve stable and trend-reflecting uplink RSSI estimation, then estimate downlink signal noise ratio (SNR) difference between potential and current AP. We implement our algorithm and deployed a test-bed for extensive experiments. Results show our sampling mechanism could achieve sampling quantity decrease by 60%; compared to mean filter based approach, our handoff algorithm improves throughput by 10-50% in distinctive signal coverage scenarios. Besides, handoff frequency decreased by about 60%, indicating a more stable handoff decision.
机译:在基于SDN的WLAN中,控制器需要收集移动节点(MN)的状态信息,例如接收信号强度指示器(RSSI)以便进行切换。在这种情况下,高采样率有助于切换,但也容易导致系统开销,从而限制了MN的接入规模。此外,动态调整接入点(AP)的发射功率会导致独特的信号覆盖范围。直接使用上行链路RSSI作为切换条件的现有切换算法将导致MN的吞吐量显着下降。同样在室内部署中,RSSI可能变化很大,RSSI的大变化会导致切换不稳定。为了解决这些问题,我们设计了一种可变采样率机制,然后对采样RSSI进行滤波,并针对特殊的信号覆盖场景提出了一种切换算法。我们的采样机制使用有限状态机(FSM)来调整MN在所有附近AP上的采样率。我们的切换算法使用卡尔曼滤波器来实现稳定且反映趋势的上行链路RSSI估计,然后估计电势和当前AP之间的下行链路信号噪声比(SNR)差。我们实施我们的算法,并为广泛的实验部署了一个试验台。结果表明,我们的抽样机制可以使抽样数量减少60%;与基于均值滤波器的方法相比,我们的切换算法在独特的信号覆盖情况下将吞吐量提高了10-50%。此外,切换频率降低了约60%,表明切换决策更加稳定。

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