Caching and D2D Assisted Wireless Emergency Communications Networks with Statistical QoS Provisioning

Along with natural disasters,the destruction of communication infrastructures leads to the congestion or failure of communication networks.Unmanned aerial vehicles(UAVs),which are with a high flexibility,can be employed as temporary base stations to establish emergency networks.To relieve the backhaul burden of UAVs,some imperative contents can be cached by terrestrial cache-enabled rescuers(CERs)and provide for victims with device-to-device(D2D)transmissions.To support the effectiveness and timeliness of emergency communication,the delay-bounded quality-of-service(QoS)requirement and network throughput are desired to be comprehensively considered,which imposes a new challenge for caching placement and CER deployment.In this paper,we focus on joint caching placement and CER deployment to maximize the effective capacity subject to delay-bounded QoS requirement.The overall non-convex problem is transformed into the caching placement and the CER deployment sub-problems.Then,we develop the QoS-aware caching placement scheme with fixed CER deployment density and obtain the QoS-aware CER deployment density with fixed caching placement.Based on the block-coordinate descent method,we also propose the joint caching placement and CER deployment scheme,which can not only effectively enhance average effective capacity but also guarantee the delay-bounded QoS requirement.Also,numerical simulations are conducted to show the performances of the proposed schemes.

RIS-SWIPT for Batteryless Users in Disaster Areas

For trapped users in disaster areas,the available energy of affected user equipment(UE)is limited due to the breakdown of the ground power system.When complex geographical condition prevents ground emergency vehicles from reaching disaster-stricken areas,unmanned aerial vehicle(UAV)can effectively work as a temporary aerial base station for serving terrestrial trapped users.Simultaneous wireless information and power transfer(SWIPT)system is intriguing for distributed batteryless users(BUs)by transferring data and energy simultaneously.However,how to achieve the maximum energy efficiency(EE)and energy transfer efficiency(ETE)for distributed BUs in UAV-enabled SWIPT systems is not very clear.In this paper,we develop three novel reconfigurable intelligent surface(RIS)-based SWIPT algorithms to solve this nonconvex joint optimization problem using deep reinforcement learning(RL)algorithms.Through the deployment of RIS-assisted UAVs,we aim to maximize the EE along with the ETE via jointly designing the UAV trajectory,the phase matrix,and the power splitting ratio within strict time and energy constraints.The obtained numerical results show that our developed RL-based algorithms can effectively improve the cost time,the average charging rate,data rate,and the EE/ETE performance of the RIS-assisted SWIPT systems as compared with benchmark solutions.