Research On Network Management And Resource Scheduling Algorithm In Wireless Data And Energy Integrated Network

With the deployment of 5G,B5G and in-depth research on 6G,the Internet of Things is entering a new era.On the one hand,a large number of low-power communication devices are widely deployed and applied to realize the blueprint of the Internet of Everything.However,the Internet of Things devices generally powered by embedded batteries are limited in environment perception,data processing,and information communication by their battery capacity.Wireless radio frequency signals are invoked to remotely charge low-power IoT devices because they are controllable,flexible and reliable,which alleviates many energy issues brought about by the IoT era.At the same time,traditional wireless information transmission also exists in the radio frequency band,thus forming a wireless data and energy integrated network.On the other hand,the ever-increasing low-latency application requirements of access users have prompted the function of service providers in the access network to sink,and more and more access points with enhanced functions have begun to move closer to the user end to provide access and application services.The reduction of the physical transmission distance and the dense deployment of transmission equipment further improve the practicability and maneuverability of the wireless data and energy integrated network.But,enabling wireless energy transmission in the same frequency band will lead to a decrease in the performance of wireless data transmission,and the sinking and densely deployed enhanced base stations also lead to more complex access network and interference between users.In order to improve the performance of wireless data and energy transmission,network management and resource allocation in the new generation access network are particularly important.This paper introduces the integrated transmission of wireless data and energy into the new generation of wireless access network architecture-fog wireless access network and Cell-Free network,and studies the network management and resource scheduling in the wireless data and energy integrated network mainly.The mainly contents are listed as follow.The integrated transmission of wireless data and energy is introduced into the fog wireless access network,and computing task offloading is introduced,and the frequency domain resources,computing domain resources and space domain resources in the network architecture are managed and allocated in multiple dimensions.The operational frame structure of network transmission is constructed to synchronize the access,communication and energy supply of multiple users.Specifically,by decoupling the complex multi-domain resource allocation problem,this paper proposes a low-complexity iterative algorithm based on a greedy strategy,which is used to optimize the network’s uplink bandwidth,computing resources,downlink beamforming,and user computing task offloading strategy in a joint manner.The effectiveness of the multi-domain resource allocation design is illustrated through the simulation of the model.The integrated transmission of wireless data and energy is introduced into the CellFree network to construct a self-sustainable Cell-Free network in energy consumption.We invoke the wireless energy transmission in the downlink phase to provide support for the pilot estimation of access users,while ensuring their minimum throughput requirements.In this paper,by optimizing the downlink power allocation on the base station side,a compromise is made between user pilot estimation and throughput performance.Through sequential parametric convex approximation to the original problem,a low-complexity iterative algorithm is proposed.Through the simulation of the model,the convergence of the algorithm is verified,and the energy self-sustainable performance improvement brought about by the downlink power allocation is explained.The integrated transmission of wireless data and energy is introduced into the CellFree network,and wireless data and energy transmission services are provided for access users on a mixed time scale.We consider the AP classification strategy on a large time scale and the downlink beamforming design on a small time scale.In the sense of longterm system performance,the requirements of data users and energy users are guaranteed,and the overall energy consumption of the system is minimized.energy consumption.We model the problem as a Markov decision process,and introduce a digital twins based reinforcement learning algorithm,which greatly avoids the performance instability in the learning phase of the reinforcement learning algorithm and improves the convergence of the algorithm.Through the simulation of the model,it illustrates the long-term performance improvement of the system and the effectiveness of the algorithm design.