| The information age makes communication technology widely applied."The Internet of Things" has become a reality.With the expansion of communication range and frequent usage of terminals,energy has become a problem for operators and users.Reducing the energy consumption and the cost of terminals,reducing the cost of users,and realizing the self-production and self-use of energy of terminals have become the focus of scholars’ research.Mobile terminals have facilitated people’s lives,but they also bring a lot of data processing work.Most of the terminals’ computing power cannot satisfy the needs of users promptly.To solve the above problems,thesis combines the Simultaneous Wireless Information and Power Transfer(SWIPT)technology and Mobile Edge Computing(MEC)technology to reduce computing cost and increase computing rate.Considering the wider wireless communication coverage,thesis adds relays for collaborative communication.It effectively solves communication problem caused by deep fading.The specific research of thesis is as follows:1.We design a SWIPT collaborative system model with direct link and relay link.Multiple capacity formulas of SWIPT collaborative system are analyzed.The ergodic capacity and capacity with outage are compared when Channel State Information(CSI)is known at the receiver,and the performance of the capacity with outage is analyzed by setting different thresholds.The threshold value of maximum outage capacity is analyzed according to the different cases of channels and thresholds.Using the simulations to obtain the performance comparison of ergodic capacity,shannon capacity getting by the power adaptation policy named as water-filling,maximum outage capacity and zero-outage capacity.Simulation results show that SWIPT technology can improve the efficiency of utilizing energy and the capacity of system.2.The above content is analyzed for the single terminal system.Most of the actual scenarios have multiple terminals.Therefore,the thesis combines SWIPT technology and MEC technology with the scenario model of multi-terminal,multi-relay,and multiserver.Meanwhile,we also analyzed the computing cost.The thesis uses the Actor-Critic(AC)algorithm and a convex optimization algorithm to improve the computing rate,allocate time resources,and reduce the computing cost.Then,we obtain the AC offloading scheme that maximizes the computing rate and the AC selection scheme that minimizes the computing cost.The simulation results verify that the AC scheme not only have optimal computing rate and computing cost,but also reduces the program execution delay significantly.3.To further improve the system performance,the thesis introduces the edge caching technique to analyze the system time delay and required payment funds.The thesis adopts the Deep Deterministic Policy Gradient Algorithm(DDPG)algorithm to solve the problem of difficult convergence of the critic network.The thesis combines SWIPT technology and edge caching technology.In the environment of the random channel,the random price of servers and the random tasks,the thesis uses the DDPG algorithm to minimize the total system consumption Then we obtain the optimal DDPG partial offloading scheme.The simulation results show that the DDPG partial offloading scheme works well and can reduce the system consumption significantly. |
