基于局部领导选举的高效可扩展分布式自治空间Aloha网络
Efficient and Scalable Distributed Autonomous Spatial Aloha Networks via Local Leader Election
摘要：
本文使用空间Aloha模型来描述一个分布式自治无线网络，其中一组发送-接收对（用户）共享一个共同的碰撞信道通过时隙Aloha类随机接入。本研究的目的是开发一种智能算法嵌入到收发器，使所有用户知道如何自我调整他们的媒体访问概率（MAP），以实现整体帕累托最优的网络吞吐量下空间复用，同时保持网络稳定性。虽然最优解要求每个用户都有关于网络的完整信息，但我们提出的算法只要求用户有本地信息。该算法的基本思想是，用户首先自组织成若干个互不重叠的邻域，每个邻域中节点度最大的用户被选为局部领导者（LL）。然后，每个LL根据指示其相邻区域中的无线电强度水平的参数R来调整其MAP，而邻域中的剩余用户简单地遵循相同的MAP值。我们表明，通过确保R小于或等于2在LL，整个网络的稳定性可以得到保证，即使每个用户只有部分网络信息。对于实际实现，我们建议每个LL使用R=2作为其内置的比例和积分控制器的恒定参考信号。讨论了控制参数的设置，并通过仿真验证了所提出的方法能够实现接近帕累托前沿的吞吐量。
英文摘要：
This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit-receive pairs (users) shares a common collision channel via slotted-Aloha-like random access. The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability. While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information. The fundamental of our algorithm is that the users will first self-organize into a number of non-overlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL). Each LL then adjusts its MAP according to a parameter R which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value. We show that by ensuring R less than or equal to 2 at the LLs, the stability of the entire network can be assured even when each user only has partial network information. For practical implementation, we propose each LL to use R=2 as the constant reference signal to its built-in proportional and integral controller. The settings of the control parameters are discussed and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
