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Using convex relaxation and optimization methods, outer-bounds of the consistent parameters or states can be determined by solving the constraint satisfaction problem.
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It has been shown in [25] that by solving the constraints (7) and (8), the following inequality must be satisfied if there exists a feasible power assignment that meets the QoS requirements: (13).
γ is a Lagrange multiplier and can be found by solving the normalization constraint ∑ b π b =1.
This is because our resolution algorithm retains additional slack at both the coordinator node and monitoring nodes and reduces vast communication cost by solving the violated constraints detected at monitoring nodes successfully.
To reduce idling time at signalized intersection, an intersection passing decision is presented with model prediction to forecast the arrival time with downstream queue discharge time under consideration, and an eco-driving model with speed reduction strategy is proposed by solving the combined constraints of the signal phase and timing (SPaT) and the vehicle status.
We present a new series of distributed constraint satisfaction algorithms, the distributed breakout algorithms, which is inspired by local search algorithms for solving the constraint satisfaction problem (CSP).
Moreover, by solving the inequality above, the constraint on the threshold can be derived γ ≤ σ 0 2 + σ x 2 ln Δ C PK Δ C PKϕ.
It is possible to obtain the minimum transmit power of both CUEs and DUEs by strictly binding and solving the constraints in Equation 15.
The watermarked signal was obtained in closed form, which was derived by solving the minimization problem with the constraints on watermark decoding and detection.
The unknown parameter of time domain data signal (hat {x}(m,n)) which corresponds to the transmitted time domain data given in (1) can be estimated by solving the following ML equation under the constraint for minimizing the difference between the actual received signal y D (m,n 2) in (12) and the expected received signal (hat {y}_{D}(m,n_{2})) in (15).
The power allocation is to find p k such that the SER in (24) is maximized subject to the power constraint by solving the following optimization problem L ( p 1, …, p U ) = ∑ k = 1 U - 1 SER k ′ - λ ∑ k = 0 U - 1 p k - Y e + U c. (25).
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