Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  study a resource efficient coordinated beamforming design for the downlink of heterogeneous multiuser system. The objective function is formulated as maximizing the ighted sum per-cell resource efficiencies subject to per-user quality of service demands and per-cell transmit por constraints, which is hard to tackle due to the ighted sum-of-ratios form and the non-convex nature of the user rate. To solve this problem,  first introduce some auxiliary variables and transform it into an equivalent problem, which can be derived by the fractional programming and convex approximation method. Then, a resource efficient optimization algorithm is proposed to achieve the solution by exploiting geometric programming and second order cone programming. Numerical results verify the advantage of the proposed algorithm, and show that it converges to a stable point within a limited number of iterations.
['Ying Lu', 'Lei Wei', 'Shiwen He', 'Yongming Huang', 'Luxi Yang']
I am writing a research paper on a idea Resource efficient beamforming design for heterogeneous multiuser systems
[{"from": "human", "value": "I am writing a research paper on a idea Resource efficient beamforming design for heterogeneous multiuser systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  study a resource efficient coordinated beamforming design for the downlink of heterogeneous multiuser system. The objective function is formulated as maximizing the ighted sum per-cell resource efficiencies subject to per-user quality of service demands and per-cell transmit por constraints, which is hard to tackle due to the ighted sum-of-ratios form and the non-convex nature of the user rate. To solve this problem,  first introduce some auxiliary variables and transform it into an equivalent problem, which can be derived by the fractional programming and convex approximation method. Then, a resource efficient optimization algorithm is proposed to achieve the solution by exploiting geometric programming and second order cone programming. Numerical results verify the advantage of the proposed algorithm, and show that it converges to a stable point within a limited number of iterations."}]