Two-stage stochastic non-convex optimization: Due to the stochastic nature of the two-timescale optimization, there are usually expectation operators in the objective/constraint functions and the argument of the expectation operators involve the optimal short-term precoding solutions, which do not have closed form expressions.
However, there are several technical challenges to achieve these good features of two-timescale resource optimization for C-RAN.
For example, similar two-timescale resource optimization schemes have been proposed in [130 132] for massive MIMO systems or heterogeneous networks and they are shown to achieve much better tradeoff between performance and various implementation costs.
But if the timescale for velocity optimization has shifted from days or weeks to months or years, it is worth taking a close look at going serverless.
One interesting future work in this direction is to develop an optimization based two-timescale joint RRH clustering and precoding scheme, where the long-term RRH clustering is adaptive to the channel statistics and the short-term precoding is adaptive to the instantaneous partial CSI from the active RRHs to the users.
For these large systems, simply entering primary sequence data into a simulation provides too many parameters for optimization on realistic timescales.
The optimization strategy can be implemented in the timescale of ms to ms.
The optimization is focused on accounting for short timescale variation of the absorption.
The transition rates k 0 were obtained using a stochastic optimization algorithm that promotes a coherent oscillatory behavior at a timescale significantly slower than the rest of the dynamics, based on criteria regarding the spectrum of M and with concentration of TFs set in the physiological range for eukaryotes [ 52, 83, 84], ie. 3 nM (cf table S4 in Additional file 1 for details).
For any further optimization and enhancement in photovoltaic parameters, one needs to address the timescale and charge carrier kinetics occurring at different interfaces in PSCs.
A linearization technique is applied to make the optimization problem convex and able to be solved at operational timescales.
