解决随机网络中的连通性-吞吐量权衡问题
Resolving the Connectivity-Throughput Trade-Off in Random Networks
摘要：
分析了无线网络中吞吐量上界与随机网络中吞吐量缩放之间的差异，即连通性-吞吐量权衡。在一个随机网络与$λ$节点每单位面积，吞吐量被发现规模的$\sqrt{\logλ}$更糟糕的上限相比，这是由于在节点的位置的不确定性的一个因素。在本模型中，假设节点知道自己的地理位置，并采用功率控制，我们理解为一个额外的自由度，以提高网络性能。期望的吞吐量进度和期望的数据包延迟归一化到一跳进度被选为性能指标。这些指标的最近邻转发策略，通过降低传输功率，从而从功率控制的好处，空间竞争。它表明，可以解决的连接性吞吐量的权衡，如果节点采用最近邻转发策略，实现平均吞吐量上界也在随机网络，同时确保渐近连接。在这种情况下，最佳的吞吐量延迟缩放权衡也实现了。
英文摘要：
The discrepancy between the upper bound on throughput in wireless networks and the throughput scaling in random networks which is also known as the connectivity-throughput trade-off is analyzed. In a random network with $λ$ nodes per unit area, throughput is found to scale by a factor of $\sqrt{\logλ}$ worse compared to the upper bound which is due to the uncertainty in the nodes' location. In the present model, nodes are assumed to know their geographical location and to employ power control, which we understand as an additional degree of freedom to improve network performance. The expected throughput-progress and the expected packet delay normalized to the one-hop progress are chosen as performance metrics. These metrics are investigated for a nearest neighbor forwarding strategy, which benefits from power control by reducing transmission power and, hence spatial contention. It is shown that the connectivity-throughput trade-off can be resolved if nodes employ a nearest neighbor forwarding strategy, achieving the upper bound on throughput on average also in a random network while ensuring asymptotic connectivity. In this case, the optimal throughput-delay scaling trade-off is also achieved.
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。
