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Adaptive RTO for handshaking-based MAC protocols in underwater acoustic networks

机译：水下声学网络中基于握手的MAC协议的自适应RTO

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摘要

Underwater acoustic networks (UANs) are attracting interest in recent decades. The unique characteristics of the underwater acoustic channel, such as long propagation delay, delay variance, and high bit error rate, present challenges for the medium access control (MAC) protocol design in UANs. Most existing medium access control protocols ignore the delay variance which prevents the accurate estimation of round trip time (RTT). The expected RTT value can be used to compute the Retransmission Time-Out (RTO) or the waiting time in MAC. The estimation of RTT is also meaningful for Automatic Repeat re-Quest (ARQ) scheme because the system should ensure reliable data transmissions in the presence of high bit error rate in the underwater acoustic channel. By analyzing the impact of RTO on throughput under the effect of delay variance, we conclude that the fixed RTO is inefficient and RTO should be adaptively set to improve the throughput. We present a novel approach of predicting the RTT using a Bayesian dynamic linear model, and then adjust RTO adaptively according to the predicted values. Simulation results show that the predicted values can adapt quickly to the sample RTT values. Under the effect of RTT fluctuations, the Bayesian algorithm offers performance gains in terms of throughput and prediction performance, comparing with Karn’s algorithm. Our study highlights the value of predicting the RTT using Bayesian approach in underwater acoustic networks.
机译：近几十年来，水下声网络（UAN）引起了人们的兴趣。水下声通道的独特特性，例如长传播延迟，延迟差异和高误码率，对UAN中的媒体访问控制（MAC）协议设计提出了挑战。大多数现有的媒体访问控制协议都忽略了延迟差异，这妨碍了对往返时间（RTT）的准确估计。预期的RTT值可用于计算MAC中的重传超时（RTO）或等待时间。 RTT的估计对于自动重复请求（ARQ）方案也很有意义，因为在水下声信道中存在高误码率的情况下，系统应确保可靠的数据传输。通过分析在延迟方差影响下RTO对吞吐量的影响，我们得出结论，固定RTO效率低下，应该自适应设置RTO以提高吞吐量。我们提出一种使用贝叶斯动态线性模型预测RTT的新颖方法，然后根据预测值自适应地调整RTO。仿真结果表明，预测值可以快速适应样本RTT值。在RTT波动的影响下，与Karn算法相比，贝叶斯算法在吞吐量和预测性能方面提供了性能提升。我们的研究强调了在水下声学网络中使用贝叶斯方法预测RTT的价值。

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