Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  investigate the optimal por allocation problem in dispersed cognitive radios with carrier aggregation. To this end,  model the carrier aggregation using an i — th frequency diversity policy, wherein each component carrier is provided with a maximal ratio combining. An ascending order is employed for the aggregation using maximal ratio combining receiver. Additionally, due to the dispersed nature of the channels,  assume an independent but non-identically distributed (i.n.i.d.) η — μ distribution for each channel branch. Having defined this model,  formulate the maximization problem over the transmit por of each CC with frequency diversity provision over the heterogeneous fading channels. The problem is solved using alternating direction method of multipliers (ADMM) as a dual decomposition application providing robustness. Distributed (without consensus) and centralized (with consensus) ADMM algorithms are devised and the corresponding simulation results are discussed. Simulation results corroborates the performance gain of the proposed ADMM solution both in terms of iterations and rate gain per CC.
['Fotis Foukalas', 'Tamer Khattab']
I am writing a research paper on a idea Optimal power allocation in dispersed cognitive radio systems with carrier aggregation
[{"from": "human", "value": "I am writing a research paper on a idea Optimal power allocation in dispersed cognitive radio systems with carrier aggregation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  investigate the optimal por allocation problem in dispersed cognitive radios with carrier aggregation. To this end,  model the carrier aggregation using an i \u2014 th frequency diversity policy, wherein each component carrier is provided with a maximal ratio combining. An ascending order is employed for the aggregation using maximal ratio combining receiver. Additionally, due to the dispersed nature of the channels,  assume an independent but non-identically distributed (i.n.i.d.) \u03b7 \u2014 \u03bc distribution for each channel branch. Having defined this model,  formulate the maximization problem over the transmit por of each CC with frequency diversity provision over the heterogeneous fading channels. The problem is solved using alternating direction method of multipliers (ADMM) as a dual decomposition application providing robustness. Distributed (without consensus) and centralized (with consensus) ADMM algorithms are devised and the corresponding simulation results are discussed. Simulation results corroborates the performance gain of the proposed ADMM solution both in terms of iterations and rate gain per CC."}]