Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Consumers all over the world are increasingly using their smartphones on the go and expect consistent, high quality connectivity at all times. A key network primitive that enables continuous connectivity in cellular networks is  handoff . Although handoffs are necessary for mobile devices to maintain connectivity, they can also cause short-term disruptions in application performance. Thus, applications could benefit from the ability to predict impending handoffs with reasonable accuracy, and modify their behavior to counter the performance degradation that accompanies handoffs. ,  study whether attributes relating to the cellular network conditions measured at handsets can accurately predict handoffs. In particular,  develop a machine learning framework to predict handoffs in the near future. An evaluation on handoff traces from a large US cellular carrier shows that our approach can achieve 80% accuracy -- 27% better than a naive predictor.
['Umar Javed', 'Dongsu Han', 'Ramón Cáceres', 'Jeffrey Pang', 'Srinivasan Seshan', 'Alexander Varshavsky']
I am writing a research paper on a idea Predicting handoffs in 3G networks
[{"from": "human", "value": "I am writing a research paper on a idea Predicting handoffs in 3G networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Consumers all over the world are increasingly using their smartphones on the go and expect consistent, high quality connectivity at all times. A key network primitive that enables continuous connectivity in cellular networks is  handoff . Although handoffs are necessary for mobile devices to maintain connectivity, they can also cause short-term disruptions in application performance. Thus, applications could benefit from the ability to predict impending handoffs with reasonable accuracy, and modify their behavior to counter the performance degradation that accompanies handoffs. ,  study whether attributes relating to the cellular network conditions measured at handsets can accurately predict handoffs. In particular,  develop a machine learning framework to predict handoffs in the near future. An evaluation on handoff traces from a large US cellular carrier shows that our approach can achieve 80% accuracy -- 27% better than a naive predictor."}]