Search

This paper is concerned with the efficient movement control of multiple drone cells for communication coverage. Although many works have been developed to cope with this problem, but only few of them have investigated the three-dimensional (3-D) continuous movement control of multiple drone cells. In this paper, a problem of 3-D continuous movement control of multiple drone cells is formulated with an objective of maximizing the energy-efficient communication coverage of drone-cell networks while preserving the network connectivity. To mitigate this challenging problem, an energy-efficiency and continuous-movement-control algorithm (E $^2$ CMC), which is based on an emerging deep reinforcement learning method, is proposed. In E $^2$ CMC, an energy-efficiency reward function considering the energy consumption, the sum of quality-of-service (QoS) requirements of users as well as the coverage fairness is first designed. Next, E $^2$ CMC learns to adjust drone cells’ locations continuously by interacting with an environment in a sequence of observations, actions, and rewards. Furthermore, E $^2$ CMC will reduce the reward drastically as long as the networks are disconnected. Simulation results verify the superiority of the proposed learning algorithm on deploying multiple drone cells to provide the communication coverage. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an unmanned aerial vehicle (UAV) communication system, where the trajectories of multi-UAVs are designed for the data collection mission of IoT nodes. We aim at minimizing the mission time with constraints of UAV’s maximum speed and acceleration, the collision avoidance, and communication interference among UAVs. We propose a three-step approach to solve this problem, which is based on the K-means algorithm, and Deep Reinforcement Learning (DRL) with a distributed manner and a centralized manner. The mutual influences like collision avoidance and interference among UAVs are explicitly expressed in our algorithm. Numerical results show the advantage of our proposed approach. [ABSTRACT FROM AUTHOR]

Currently, self-driving, emerging as a key automatic application, has brought a huge potential for the provision of in-vehicle services (e.g., automatic path planning) to mitigate urban traffic congestion and enhance travel safety. To provide high-quality vehicular services with stringent delay constraints, edge computing (EC) enables resource-hungry self-driving vehicles (SDVs) to offload computation-intensive tasks to the edge servers (ESs). In addition, caching highly reusable contents decreases the redundant transmission time and improves the quality of services (QoS) of SDVs, which is envisioned as a supplement to the computation offloading. However, the high mobility and time-varying requests of SDVs make it challenging to provide reliable offloading decisions while guaranteeing the resource utilization of content caching. To this end, in this paper we propose a collaborative computation offloading and content caching method, named CoPace, by leveraging deep reinforcement learning (DRL) in EC for self-driving system. Specifically, we first introduce OSTP to predict the future time-varying content popularity, taking into account the temporal-spatial attributes of requests. Moreover, a DRL-based algorithm is developed to jointly optimize the offloading and caching decisions, as well as the resource allocation (i.e., computing and communication resources) strategies. Extensive experiments with real-world datasets in Shanghai, China, are conducted to evaluate the performance, which demonstrates that CoPace is both effective and well-performed. [ABSTRACT FROM AUTHOR]

In this paper, we study the handover (HO), and power allocation problem in a two-tier heterogeneous network (HetNet), which consists of a macro base station, and some millimeter-wave (mmWave) small base stations. We establish an HO management, and power allocation scheme to maximize the overall throughput while reducing the HO frequency. In particular, considering the interrelationship among decisions made by different user equipments (UEs), we first model the HO, and power allocation problem as a fully cooperative multi-agent task, in which all agents, i.e., UEs, have the same target. Then, to solve the multi-agent task, and get decentralized policies for each UE, we develop a multi-agent reinforcement learning (MARL) algorithm based on the proximal policy optimization (PPO) method, by introducing the centralized training with decentralized execution framework. That is, we use global information to train policies for each UE, and after the training is finished, each UE obtains a decentralized policy, which can be implemented only based on each UE's local observation. Specially, we introduce the counterfactual baseline to address the credit assignment problem in centralized learning. Due to the centralized training, the decentralized polices learned by multi-agent PPO (MAPPO) can work more cooperatively. Finally, the simulation results demonstrate that our method can achieve better performance comparing with other existing works. [ABSTRACT FROM AUTHOR]

Mobile traffic classification and prediction are key tasks for network optimization. Most of the works in this area present two main drawbacks. First, they treat the two tasks separately, thus requiring high computational capabilities. Second, they perform data mining on the information collected from the data plane, which is unsuitable for the mobile edge. To bridge this gap, this paper properly tailors a Multi-Task Learning model running directly at the edge of the network to anticipate information on the type of traffic to be served and the resource allocation pattern requested by each service during its execution. Our study exploits data mining from the control channel of an operative mobile network to also reduce storage and monitoring processing. Different configurations of neural networks, which adopt autoencoders (i.e. Undercomplete Autoencoder or Sequence to Sequence Autoencoder) as key building blocks of the proposed Multi-Task Learning methodology for common feature representations, are investigated to evaluate the impact of the observation window of traffic profiles on the classification accuracy, prediction loss, complexity, and convergence. The comparison with respect to conventional single-task learning approaches, that do not use autoencoders and tackle classification and prediction tasks separately, clearly demonstrates the effectiveness of the proposed Multi-Task Learning approach under different system configurations. [ABSTRACT FROM AUTHOR]

The smart vehicles construct Internet of Vehicle (IoV), which can execute various intelligent services. Although the computation capability of a vehicle is limited, multi-type of edge computing nodes provide heterogeneous resources for intelligent vehicular services. When offloading the complex service to the vehicular edge computing node, the decision for its destination should be considered according to numerous factors. This paper mostly formulate the offloading decision as a resource scheduling problem with single or multiple objective function and constraints, where some customized heuristics algorithms are explored. However, offloading multiple data dependence tasks in a complex service is a difficult decision, as an optimal solution must understand the resource requirement, the access network, the user mobility, and importantly the data dependence. Inspired by recent advances in machine learning, we propose a knowledge driven (KD) service offloading decision framework for IoV, which provides the optimal policy directly from the environment. We formulate the offloading decision for the multiple tasks as a long-term planning problem, and explore the recent deep reinforcement learning to obtain the optimal solution. It can scruple the future data dependence of the following tasks when making decision for a current task from the learned offloading knowledge. Moreover, the framework supports the pre-training at the powerful edge computing node and continually online learning when the vehicular service is executed, so that it can adapt the environment changes and can learn policy that are sensible in foresight. The simulation results show that KD service offloading decision converges quickly, adapts to different conditions, and outperforms a greedy offloading decision algorithm. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) has been expected as a promising scheme that can increase the computational capability of vehicles without relying on servers. Comparing with accessing the remote cloud, VFC is suitable for delay-sensitive tasks because of its low-latency vehicle-to-vehicle (V2V) transmission. However, due to the dynamic vehicular environment, how to motivate vehicles to share their idle computing resource while simultaneously evaluating the service availability of vehicles in terms of vehicle mobility and vehicular computational capability in heterogeneous vehicular networks is a main challenge. Meanwhile, tasks with different priorities of a vehicle should be processed with different efficiencies. In this work, we propose a task offloading scheme in the context of VFC, where vehicles are incentivized to share their idle computing resource by dynamic pricing, which comprehensively considers the mobility of vehicles, the task priority, and the service availability of vehicles. Given that the policy of task offloading depends on the state of the dynamic vehicular environment, we formulate the task offloading problem as a Markov decision process (MDP) aiming at maximizing the mean latency-aware utility of tasks in a period. To solve this problem, we develop a soft actor-critic (SAC) based deep reinforcement learning (DRL) algorithm for the sake of maximizing both the expected reward and the entropy of policy. Finally, extensive simulation results validate the effectiveness and superiority of our proposed scheme benchmarked with traditional algorithms. [ABSTRACT FROM AUTHOR]