In emergency communications constrained by bandwidth limitations, unmanned aerial vehicle (UAV)-based coded caching presents a promising approach for the efficient dissemination of high-bandwidth-demanding services. This paper focuses on content download and content repair in aerial caching networks, where UAVs deliver contents to both ground users and invalid UAVs. To address potential data loss due to limited power and high mobility, fault-tolerant codes are utilized to maintain data availability and reliability. Initially, we derive the expressions of communication cost and success rate for content download and content repair. The size of coded fragments, determined by the coding design, affects both the success rate and transmission cost, while the resource allocation, which influences the cooperative relationships, also impacts these two aspects. The interplay between coding design and resource allocation is thus established to jointly optimize the overall performance. Then, we design a joint optimization problem of erasure coding schemes, coding parameters, matching relations, and UAV trajectories to maximize the overall success rate. Moreover, we propose a hierarchical multi-agent parameterized deep Q-network (H-MA-PDQN) algorithm integrating a dual-component structure for long-term coding and immediate resource allocation to solve the mixed integer nonlinear programming (MINLP), and each agent employs a PDQN with hybrid discrete-continuous action space. Simulation results demonstrate that our proposed H-MA-PDQN algorithm increases the success probability by 26.7% and 66.7% and reduces the transmission cost by 27.3% and 42.9% compared with the DQN and greedy-based strategies, respectively.

Next-generation mobile networks are expected to facilitate fast AI model deployment on end devices (EDs). By enabling collaborative model caching across EDs, mobile networks can efficiently support distributed AI inference services through device-to-device (D2D) cooperation. In this paper, we investigate a D2D-assisted model caching and collaborative computing framework that aims to balance the trade-off among inference delay, accuracy, and energy consumption by managing model caching, data offloading, and computation resources efficiently during the provisioning of diverse AI services. Specifically, considering the AI performance is constrained by multi-dimensional resources, a new metric named Service Hit Rate (SHR) is proposed to decouple the joint impacts of computation, communication, and caching resources on service success. Aiming to maximize the SHR, we propose a matching-aided multi-agent reinforcement learning (MARL) framework. First, a hierarchical bipartite matching algorithm is utilized for model deployment and helper assignment. Then, an attention-based MARL algorithm is employed to allocate computation resource for models cached on the same EDs. Simulation results demonstrate that the proposed algorithm significantly improves the system SHR for AI services.

With bandwidth constraints in emergency communications, device-to-device (D2D)-based coded caching emerges as a solution for efficiently transmitting high-bandwidth-demanding services. In this article, we investigate content sharing between emergency vehicles and mobile users via D2D communications in the emergency networks by exploiting coded caching schemes. The joint optimization of coding schemes, coding parameters, and matching relations is proposed to maximize the overall success probability of content sharing while minimizing the overall transmission cost. The interplay between coding parameters optimization and resource allocation is investigated by both download and repair process. Moreover, we propose a multi-agent parameterized deep Q-network (MA-PDQN) algorithm to solve the mixed integer nonlinear programming (MINLP), with each agent employing a PDQN with hybrid discrete-continuous action space. Simulation results show the effectiveness of the proposed algorithm in improving success probability and reducing transmission cost.

In emergency scenarios, strong mobility and serious interference cause unstable transmission of on-site information such as close-up photos and high resolution videos, which requires a robust temporary communication network. In this paper, we focus on a UAV-assisted wireless cooperative communication and coded caching network, where emergency command vehicles and a UAV serve as content providers (CPs) to cache and transmit coded fragments or complete files for rescuers regarded as content requesters (CRs). The delivery success probability and content hit ratio are theoretically derived by incorporating the physical connectivity and social relationship between CPs and CRs. Aiming at maximizing the overall content hit ratio, we propose a multiagent two-timescale deep reinforcement learning (MA2T-DRL) algorithm to jointly optimize the transmission power and caching strategies for CPs. Specifically, we develop a two tier deep-Q networks (DQNs) framework integrating a slow-timescale DQN (ST-DQN) and a fast-timescale DQN (FT-DQN) for caching decision-making and power decision-making respectively, and then the QMIX framework is leveraged to aggregate all the outputs from local ST-DQNs. Considering the cooperative characteristics of coded caching, we further propose a novel clustering method for CPs such that CPs in the same cluster have the same willingness to serve CRs, and each cluster is regarded as the agent for training which further reduces the aggregation scale of the mixing network. Simulation results show that the proposed MA2T-DRL algorithm is efficient in model training, and presents the advantages in performance and complexity compared with the single-agent centralized training and the multiagent independent distributed training.

The advances of communication technology and edge intelligence are deriving new computation offloading modes in the energy Internet by integrating computing capabilities of cloud servers, edge gateways (EGs), and terminal nodes into forecasting the renewable energy generation. However, the largely dispersed data generated by abundant photovoltaic (PV) stations and limited transmission capacity will degrade the collaboration of clouds, edges, and end nodes and, as a result, fail to satisfy the delay requirements of tasks. Owing to the similarity of power data generated by PV stations with akin geographical positions and weather conditions, we can reuse and offload the selected and representative power data so as to reduce transmission costs and overloads. In this article, we propose a similarity-aware stable matching approach (SASMA) to efficiently offload prediction tasks to EGs or cloud platforms with reusing the computing results. Specifically, we analyze task similarity and build the reuse strategy for the power data, and propose a similarity graph algorithm (SGA) to select representative PV stations and derive reuse relations. We also propose a similarity-based Gale–Shapley algorithm to match reused PV stations, computing nodes with prediction models. The objective is to maximize the prediction accuracy with a stable match. Simulation results show the effectiveness of the proposed approach while examining the tradeoff between the prediction accuracy and the system delay.

Coded caching can improve the robustness of wireless caching networks. This paper investigates the joint caching and communication optimization in terminal based wireless coded caching networks. Social characteristics of private terminals are considered to further harvest more performance gain in terms of hit ratio. To tackle the problem of unknown popularity distribution, we adopt deep reinforcement learning. Since the joint caching and communication optimization of all Content Providers (CP) results in larger-scale action and state space, we propose a novel cluster based deep reinforcement learning (CB-DRL) scheme. We present simulation results to demonstrate complexity reduction and effectiveness of the proposed algorithm.