# RF Energy Harvesting Wireless Communications:
## 4 Power Management in RF-EHWC
### Discussions on Online Power Management  
The offline power management is based on an assumption that the number and arriving time of energy/data packets in the future are pre-known [54], which is unrealistic. A viable solution to the online power management is to predict the future energy arrivals.  
The prediction-based approach is based on two facts: (1) the time and the size of energy packets that will arrive in the near future are predictable to some extent; and (2) this prediction is sufficient to produce an online sub-optimal solution. As investigated in Section 2.2, the RF power density is quite stable over time in an outdoor environment. Therefore, the intensity of incident power is highly predictable, which makes the energy prediction feasible in the real world. Although the offline optimal transmission power is affected by the arrivals of all future energy packets, the energy packets to arrive in the near future has a much heavier impact on the power scheduling than the ones that will arrive in a far future. Therefore, if an energy harvester can predict the arrivals of energy packets in the near future, the performance of online power management can be close to the performance of the offline optimal solution.  
_The statistics-based approach_ will be a better fit in energy harvesting wireless systems when the RF power density in an indoor environment has considerable fluctuation due to human activity. It exploits the statistical characteristics of RF energy to develop online power management methods. In [72], the energy arrivals are modeled an independent and identically distributed Bernoulli process; the data transmission is modeled as MDP and each data has an associated importance value. The online transmission scheduling can be solved numerically by the policy iteration (PI), a dynamic programming algorithm. The device is scheduled to transmit the data if the importance value is above a dynamic threshold, which is a decreasing function of the battery's energy level and discard the data otherwise. [PERSON] et al. [73] studied the situation when the transition probabilities of the Markov chain is unknown in the MDP model. In this work, the Q-learning algorithm is recommended to recursively estimate the expected discounted reward. It is shown that the performance of an online decision can be optimized after a reasonable learning period. Other similar online policies that use the MDP model for power management can refer to [74].  
_The correlation-based approach_ utilizes the correlation among energy packets for online power management. In [75], the energy harvesting process is modeled as a two-state Markov chain, where a good state represents that one energy unit is harvested in a time slot and a bad state means there is no energy received in that slot. If the length of each time slot is much shorter than the temporal variation of the RF energy, the energy strength between two neighboring time slots will become highly correlated. In the above model, the device decides to send or drop a data packet using the PI algorithm similar to [72; 73]. A simplified suboptimal strategy, called the non-adaptive balanced policy (NABP), is available [75]. In NABP, the RF-EHWC devices compares the importance value of a data with a threshold to decide to send or to withdraw the current packet. The threshold is determined by the importance value of a data packet, the amount of harvested energy, and the residual energy in the battery. It aims to make the probability of data transmission equal to that of a good energy state in the current time slot.