支持型两层网络的延迟-延迟权衡
Delay-Throughput Tradeoff for Supportive Two-Tier Networks
摘要：
考虑一个静态无线网络，它有两个具有不同优先级的层：主层和辅助层。主层由密度为$n$的随机分布的传统节点组成，其具有接入频谱的绝对优先级。次级层由随机分布的具有$β\geq 2$的密度$m=n^β$的认知节点组成，其只能机会性地接入频谱以限制对初级层的干扰。通过允许辅助层路由的主要层的数据包，我们表明，主要层可以实现每个节点的$λ_p(n)=Θ(1/\log n)$的吞吐量缩放和$D_p(n)=Θ(\sqrt{n^β\log n}λ_p(n))$的延迟吞吐量的权衡为$λ_p(n)=O(1/\log n)$，而辅助层仍然实现了相同的最佳延迟吞吐量的权衡作为一个独立的网络。
英文摘要：
Consider a static wireless network that has two tiers with different priorities: a primary tier vs. a secondary tier. The primary tier consists of randomly distributed legacy nodes of density $n$, which have an absolute priority to access the spectrum. The secondary tier consists of randomly distributed cognitive nodes of density $m=n^β$ with $β\geq 2$, which can only access the spectrum opportunistically to limit the interference to the primary tier. By allowing the secondary tier to route the packets for the primary tier, we show that the primary tier can achieve a throughput scaling of $λ_p(n)=Θ(1/\log n)$ per node and a delay-throughput tradeoff of $D_p(n)=Θ(\sqrt{n^β\log n}λ_p(n))$ for $λ_p(n)=O(1/\log n)$, while the secondary tier still achieves the same optimal delay-throughput tradeoff as a stand-alone network.
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。
