Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The discrepancy beten the upper bound on throughput scaling in wireless networks and the throughput scaling in random networks is due to the connectivity-throughput trade-off. In a random network with λ nodes, throughput scales by a factor of √logλ worse compared to the upper bound because of the uncertainty in the nodes' location. In this work, nodes know their location and employ por control. The expected throughput-progress is analyzed for a nearest neighbor forwarding strategy, which benefits from por control by reducing spatial contention. By this, the connectivity-throughput trade-off is resolved and the upper bound is achieved.
['Ralph Tanbourgi']
I am writing a research paper on a idea Resolving the connectivity-throughput trade-off in random networks
[{"from": "human", "value": "I am writing a research paper on a idea Resolving the connectivity-throughput trade-off in random networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The discrepancy beten the upper bound on throughput scaling in wireless networks and the throughput scaling in random networks is due to the connectivity-throughput trade-off. In a random network with \u03bb nodes, throughput scales by a factor of \u221alog\u03bb worse compared to the upper bound because of the uncertainty in the nodes' location. In this work, nodes know their location and employ por control. The expected throughput-progress is analyzed for a nearest neighbor forwarding strategy, which benefits from por control by reducing spatial contention. By this, the connectivity-throughput trade-off is resolved and the upper bound is achieved."}]