> Entering new AgentExecutor chain...
Invoking: `search_by_similarity` with `{'query': 'equations to maximize system EE'}`
To maximize the system EE (Energy Efficiency), the optimization problem can be formulated as follows:
Objective:
Maximize the system EE, which is defined as the ratio of the sum achievable rate to the total power consumption. The EE of the considered system is given by:
EE = R({{w_k, α_i}, c_i}, Φ) / P_T({{w_k}})
where R({{w_k, α_i}, c_i}, Φ) represents the sum achievable rate, P_T({{w_k}}) represents the total power consumption, and Φ represents the system parameters.
Constraints:
1. The sum of the individual rates should be less than or equal to the minimum of the individual capacity requirements:
∑c_i ≤ min_i R_i^{(C)}({{w_k}}, Φ, α_i)
2. The sum of the individual rates and the power required for information communication should be greater than or equal to a certain threshold:
c_i + R_i^{(P)}({{w_k}}, Φ, α_i) ≥ ξ_i
3. The energy harvested at each user should be greater than or equal to a certain threshold:
Γ_i^{(EH)}({{w_k}}, Φ, α_i) ≥ μ_i
where c_i represents the individual rate, R_i^{(C)}({{w_k}}, Φ, α_i) represents the individual capacity requirement, R_i^{(P)}({{w_k}}, Φ, α_i) represents the power required for information communication, Γ_i^{(EH)}({{w_k}}, Φ, α_i) represents the energy harvested at each user, ξ_i represents the threshold for the sum of individual rates and power, and μ_i represents the threshold for energy harvesting.
The optimization problem can be solved by finding the values of {{w_k, α_i}, c_i} and Φ that maximize the system EE while satisfying the above constraints.
Please note that the specific equations and variables may vary depending on the context and details of your scenario.
> Finished chain.
