We develop randomized distributed algorithms for many of the most fundamental communication problems in wireless networks under the Signal to Interference and Noise Ratio (SINR) model of communication, including (multi-message) broadcast, local broadcast, coloring, Maximal Independent Set, and aggregation. The complexity of our algorithms is optimal up to polylogarithmic preprocessing time. It shows—contrary to expectation—that the plain vanilla SINR model is just as powerful and fast (modulo the preprocessing) as various extensions studied, including power control, carrier sense, collision detection, free acknowledgements, and geolocation knowledge. Central to these results is an efficient construction of a constant-density backbone structure over the network, which is of independent interest. This is achieved using an indirect sensing technique, where message non-reception is used to deduce information about relative node-distances.

中文翻译：

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稀疏骨干​​网和最优分布式 SINR 算法

我们在信号干扰和噪声比 (SINR) 通信模型下为无线网络中许多最基本的通信问题开发随机分布式算法，包括（多消息）广播、本地广播、着色、最大独立集和聚合. 我们算法的复杂性在多对数预处理时间内是最优的。它表明 - 与预期相反 - 普通 SINR 模型与所研究的各种扩展一样强大和快速（以预处理为模），包括功率控制、载波侦听、碰撞检测、免费确认和地理定位知识。这些结果的核心是在网络上有效构建恒定密度的骨干结构，这具有独立的兴趣。这是通过使用间接感应技术，其中消息未接收用于推断有关相对节点距离的信息。