Mengyu Liu, Yuan Liu
Mobile-edge computing (MEC) is a promising technology to enable real-time information transmission and computing by offloading computation tasks from wireless devices to network edge.
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By allowing a mobile device to offload computation-intensive tasks to a base station, mobile edge computing (MEC) is a promising solution for saving the mobile device's energy. In real applications, the offloading may span multiple fading blocks. In this paper, we investigate energy-efficient offloading over multiple fading blocks with random channel gains. An optimization problem is formulated, which optimizes the amount of data for offloading to minimize the total expected energy consumption of the mobile device. Although the formulated optimization problem is non-convex, we prove that the o…
Mobile-edge computation offloading (MECO) is an emerging technology for enhancing mobiles' computation capabilities and prolonging their battery lives, by offloading intensive computation from mobiles to nearby servers such as base stations. In this paper, we study the energy-efficient resource-management policy for the asynchronous MECO system, where the mobiles have heterogeneous input-data arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival-deadline orders. Based on the monomial energy-consumption model for data transmission, an optimi…
Joint Task Offloading Scheduling and Transmit Power Allocation for Mobile-Edge Computing Systems
移动边缘计算（MEC）通过将计算密集型任务从移动设备迁移到附近的MEC服务器来提供具有高计算要求的移动服务，已经成为一种突出的技术。为了降低执行延迟和设备能耗，本文针对多任务MEC系统，联合优化任务卸载调度和发射功率分配。提出了一种基于交替最小化的低复杂度次优算法，以最小化执行延迟和设备能耗的加权和。具体地，给定发射功率分配，最优任务卸载调度，即，确定卸货顺序的方法。此外，最佳的发射功率分配与给定的任务卸载调度决策将使用凸优化技术确定。仿真结果表明，当MEC系统中可用的无线电和计算资源相对平衡时，任务卸载调度更为关键。此外，它表明，该算法实现了近最佳的执行延迟，以及大量的设备节能。
Multiuser Resource Allocation for Mobile-Edge Computation Offloading
移动边缘计算卸载（MECO）将密集的移动计算卸载到位于蜂窝网络边缘的云。因此，MECO被设想为延长电池寿命和增强移动设备的计算能力的有前途的技术。在本文中，我们考虑资源分配的MECO系统，包括多个用户的时间共享一个单一的边缘云，并有不同的计算负载。最优资源分配被公式化为凸优化问题，用于在计算延迟的约束下最小化加权和移动能量消耗，并且用于无限和有限边缘云计算能力的情况。最优策略被证明有一个基于阈值的结构相对于导出的卸载优先级函数，它产生的优先级用户根据他们的信道增益和本地计算能耗。因此，具有高于和低于给定阈值的优先级的用户分别执行完全卸载和最小卸载。计算阈值需要迭代计算。为了降低复杂度，提出了一种次优的资源分配算法，并通过仿真表明，具有接近最优的性能。
This paper studies a new mobile edge computing (MEC) setup where an unmanned aerial vehicle (UAV) is served by cellular ground base stations (GBSs) for computation offloading. The UAV flies between a give pair of initial and final locations, during which it needs to accomplish certain computation tasks by offloading them to some selected GBSs along its trajectory for parallel execution. Under this setup, we aim to minimize the UAV's mission completion time by optimizing its trajectory jointly with the computation offloading scheduling, subject to the maximum speed constraint of the UAV, and th…
Mobile-edge computation offloading (MECO) has been recognized as a promising solution to alleviate the burden of resource-limited Internet of Thing (IoT) devices by offloading computation tasks to the edge of cellular networks (also known as {\em cloudlet}). Specifically, latency-critical applications such as virtual reality (VR) and augmented reality (AR) have inherent collaborative properties since part of the input/output data are shared by different users in proximity. In this paper, we consider a multi-user fog computing system, in which multiple single-antenna mobile users running applic…
In this paper, we propose a novel offloading learning approach to compromise energy consumption and latency in multi-tier network with mobile edge computing. In order to solve this integer programming problem, instead of using conventional optimization tools, we apply a cross entropy approach with iterative learning of the probability of elite solution samples. Compared to existing methods, the proposed one in this network permits a parallel computing architecture and is verified to be computationally very efficient. Specifically, it achieves performance close to the optimal and performs well …
This paper considers the coexistence of two important communication techniques, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC). Both NOMA uplink and downlink transmissions are applied to MEC, and analytical results are developed to demonstrate that the use of NOMA can efficiently reduce the latency and energy consumption of MEC offloading. In addition, various asymptotic studies are carried out to reveal the impact of the users' channel conditions and transmit powers on the application of NOMA to MEC is quite different to those in conventional NOMA scenarios. Computer si…
Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices
移动边缘计算（MEC）是一种新兴的范式，以满足不断增长的计算需求的移动应用。通过将计算密集型工作负载卸载到MEC服务器，计算体验的质量，例如，执行延迟可以大大改善。然而，由于设备上的电池容量有限，当电池能量耗尽时，计算将中断。为了提供令人满意的计算性能以及实现绿色计算，通过能量收集（EH）技术寻求可再生能源来为移动设备供电具有重要意义。在本文中，我们将研究一个绿色MEC系统与EH设备和开发一个有效的计算卸载策略。执行成本，它解决了执行延迟和任务失败，采用作为性能指标。提出了一种低复杂度的在线算法，即基于李雅普诺夫优化的动态计算卸载（LODCO）算法，该算法联合决定卸载决策、移动执行的CPU周期频率和计算卸载的发射功率。该算法的一个独特的优点是，决策只依赖于瞬时边信息，而不需要计算任务请求，无线信道和EH过程的分布信息。该算法的实现只需要在每个时隙内求解一个确定性问题，其最优解既可以以封闭形式求得，也可以通过对分搜索求得。此外，通过严格的分析表明，该算法是渐近最优的。最后通过实例仿真验证了理论分析的正确性和算法的有效性。
In this paper, we study a mobile edge computing (MEC) system in which the mobile device is assisted by a base station (BS) and a cooperative node. The mobile device has sequential tasks to complete, whereas the cooperative node assists the mobile device on both task offloading and task computation. In specific, two cases are investigated, which are 1) the cooperative node has no tasks to complete itself, and 2) the cooperative node has tasks to complete itself. Our target is to minimize the total energy consumption of the mobile device and the cooperative node through optimizing the transmit d…
Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading (Extended Version)
移动边缘计算卸载（MECO）将密集的移动计算卸载到位于蜂窝网络边缘的云。因此，MECO被设想为延长电池寿命和增强移动设备的计算能力的有前途的技术。本文研究了基于时分多址（TDMA）和正交频分多址（OFDMA）的多用户MECO系统的资源分配问题。首先，针对具有无限或有限计算能力的TDMA MECO系统，将最优资源分配问题转化为一个凸优化问题，即在计算时延约束下，最小化加权和移动能耗。最优策略被证明有一个基于阈值的结构相对于导出的卸载优先级函数，它产生的优先级用户根据他们的信道增益和本地计算能耗。因此，具有高于和低于给定阈值的优先级的用户分别执行完全卸载和最小卸载。此外，针对云计算容量有限的情况，提出了一种次优资源分配算法，降低了阈值计算的复杂度。接下来，我们考虑OFDMA MECO系统，其中最优资源分配被公式化为非凸混合整数问题。为了解决这个具有挑战性的问题，并描述其策略结构，一个次优的低复杂度算法，提出了通过转换OFDMA问题，其TDMA对应。相应的资源分配是通过定义一个平均卸载优先级函数，并通过仿真显示具有接近最优的性能。
Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading
通过将密集的计算任务卸载到位于蜂窝基站的边缘云，移动边缘计算卸载（MECO）已被视为实现第五代网络雄心勃勃的毫秒级端到端延迟要求的有前途的手段。在本文中，我们研究的延迟最小化问题，在多用户时分多址MECO系统与联合通信和计算资源分配。研究了三种不同的计算模型，即，局部压缩、边缘云压缩和部分压缩卸载。首先，推导了局部云压缩模型和边缘云压缩模型的最优资源分配和最小系统延迟的闭合表达式。然后，对于部分压缩卸载模型，我们提出了一个分段优化问题，并证明了最优数据分割策略具有分段结构。在此基础上，提出了一种通信与计算联合资源分配优化算法。为了获得更多的见解，我们还分析了一个特定的情况下，通信资源是足够的，而计算资源是有限的。在这种特殊情况下，可以导出分段优化问题的封闭形式解。最后通过数值实验验证了本文提出的算法的有效性，实验结果表明，新的部分压缩卸载模型能够显著降低端到端延迟。
This paper considers a scenario in which an access point (AP) is equipped with a mobile edge server of finite computing power, and serves multiple resource-hungry mobile users by charging users a price. Pricing provides users with incentives in offloading. However, existing works on pricing are based on abstract concave utility functions (e.g, the logarithm function), giving no dependence on physical layer parameters. To that end, we first introduce a novel utility function, which measures the cost reduction by offloading as compared with executing jobs locally. Based on this utility function …
We consider a mobile cloud computing system with multiple users, a remote cloud server, and a computing access point (CAP). The CAP serves both as the network access gateway and a computation service provider to the mobile users. It can either process the received tasks from mobile users or offload them to the cloud. We jointly optimize the offloading decisions of all users, together with the allocation of computation and communication resources, to minimize the overall cost of energy consumption, computation, and maximum delay among users. The joint optimization problem is formulated as a mix…
In this paper, a salary-plus-bonus incentive mechanism is proposed to motivate WiFi Access Points (APs) to provide data offloading service for mobile network operators (MNOs). Under the proposed salary-plus-bonus scheme, WiFi APs are rewarded not only based on offloaded data volume but also based on the quality of their offloading service. The interactions between WiFi APs and the MNO under this incentive mechanism are then studied using Stackelberg game. By differentiating whether WiFi APs are of the same type (e.g. offloading cost and quality), two cases (homogeneous and heterogeneous) are s…
Mobile-edge computing (MEC) is an emerging technology for enhancing the computational capabilities of mobile devices and reducing their energy consumption via offloading complex computation tasks to the nearby servers. Multiuser MEC at servers is widely realized via parallel computing based on virtualization. Due to finite shared I/O resources, interference between virtual machines (VMs), called I/O interference, degrades the computation performance. In this paper, we study the problem of joint radio-and-computation resource allocation (RCRA) in multiuser MEC systems in the presence of I/O int…
Mobile-edge computing (MEC) offloads computational tasks from wireless devices to network edge, and enables real-time information transmission and computing. Most existing work concerns a small-scale synchronous MEC system. In this paper, we focus on a large-scale asynchronous MEC system with random task arrivals, distinct workloads, and diverse deadlines. We formulate the offloading policy design as a restless multi-armed bandit (RMAB) to maximize the total discounted reward over the time horizon. However, the formulated RMAB is related to a PSPACE-hard sequential decision-making problem, whi…
Mobile Data Offloading through A Third-Party WiFi Access Point: An Operator's Perspective
WiFi卸载由于其高数据传输速率和对设备的低要求而被认为是处理蜂窝网络中爆炸性数据增长的最有前途的技术之一。在本文中，我们调查的移动数据卸载问题，通过第三方WiFi接入点（AP）的蜂窝移动系统。从蜂窝运营商的角度来看，通过假设一个基于使用的收费模型，我们制定了一个效用最大化问题的问题。特别地，我们考虑三种场景：（i）连续干扰消除（SIC）在基站（BS）和AP两者处都可用;（ii）SIC既不在BS处也不在AP处可用;（iii）SIC仅在BS处可用。对于（i），我们证明了效用最大化问题可以通过考虑其松弛问题来解决，并且我们证明了我们提出的数据卸载方案在用户数量很大时接近最优。对于（ii），我们证明了最优解很可能是一对一关联，即，一个用户连接到BS，一个用户连接到AP。对于（iii），我们表明，最多有一个用户连接到AP的概率很高，所有其他用户连接到BS。通过比较这三种情况下，我们证明了SIC解码器帮助蜂窝运营商最大限度地提高其效用。为了减轻BS的计算负担，我们提出了一个基于阈值的分布式数据卸载方案。我们表明，建议的分布式方案执行良好，如果阈值选择适当。
Live Prefetching for Mobile Computation Offloading
移动计算卸载的传统设计在计算之前将用户特定的数据提取到云，称为离线预取。然而，这种方法可能会导致大量数据的过度提取，并导致无线电接入网络上的重负载。为了解决这个问题，本文提出了一种新的实时预取技术，该技术将任务级计算预测和预取无缝集成到具有大量任务的大型程序的云计算过程中。该技术避免了过多的抓取，但保留了利用预测的功能，以减少程序运行时间和移动传输能量。通过将卸载程序中的任务建模为随机序列，应用随机优化来设计获取策略，以在截止期限约束下最小化移动能耗。这些策略使得能够实时控制用于未来任务的候选者的预取数据大小。对于慢衰落，最优策略的推导和显示具有基于阈值的结构，选择候选任务进行预取和控制他们的预取数据的基础上，他们的可能性。结果扩展到设计接近最优的预取策略，快衰落信道。理论上，与不带预测的预取相比，实时预取总是能够降低移动能耗。
We consider a general multi-user Mobile Cloud Computing (MCC) system where each mobile user has multiple independent tasks. These mobile users share the computation and communication resources while offloading tasks to the cloud. We study both the conventional MCC where tasks are offloaded to the cloud through a wireless access point, and MCC with a computing access point (CAP), where the CAP serves both as the network access gateway and a computation service provider to the mobile users. We aim to jointly optimize the offloading decisions of all users as well as the allocation of computation …
Resource allocation is investigated for offloading computational-intensive tasks in multi-hop mobile edge computing (MEC) system. The envisioned system has both the cooperative access points (AP) with the computing capability and the MEC servers. A user-device (UD) therefore first uploads a computing task to the nearest AP, and the AP can either locally process the received task or offload to MEC server. In order to utilize the radio resource blocks (RRBs) in the APs efficiently, we exploit the non-orthogonal multiple access for offloading the tasks from the UDs to the AP(s). For the considere…
Due to the spectrum reuse in small cell network, the inter-cell interference has great effect on MEC's performance. In this paper, for reducing the energy consumption and latency of MEC, we propose a game theory based jointing offloading decision and resource allocation algorithm for multi-user MEC. In this algorithm, the transmission power, offloading decision, and mobile user's CPU capability are determined jointly. We prove that this game is an exact potential game and the NE of this game exists and is unique. For reaching the NE, the best response dynamic is applied. We calculate the best …
This letter considers a multi-access mobile edge computing (MEC) network consisting of multiple users, multiple base stations, and a malicious eavesdropper. Specifically, the users adopt the partial offloading strategy by partitioning the computation task into several parts. One is executed locally and the others are securely offloaded to multiple MEC servers integrated into the base stations by leveraging the physical layer security to combat the eavesdropping. We jointly optimize power allocation, task partition, subcarrier allocation, and computation resource to maximize the secrecy offload…
Multi-Access edge computing (MEC) is an emerging paradigm where users offload computationally intensive jobs to the Access Point (AP). Given that the AP's resources are shared by selfish users, pricing is a useful tool for incentivising users to internalize the negative externality of delay they cause to other users. Nevertheless, different users have different negative valuations towards delay as some are more delay sensitive. To serve heterogeneous users, we propose a priority pricing scheme where users can get served first for a higher price. Our goal is to find the prices such that in deci…
Mobile-edge computing (MEC) and wireless power transfer are technologies that can assist in the implementation of next generation wireless networks, which will deploy a large number of computational and energy limited devices. In this letter, we consider a point-to-point MEC system, where the device harvests energy from the access point's (AP's) transmitted signal to power the offloading and/or the local computation of a task. By taking into account the non-linearities of energy harvesting, we provide analytical expressions for the probability of successful computation and for the average numb…
This paper studies a multiuser mobile edge computing (MEC) system, in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancellation (SIC) to efficiently decode all users' offloaded tasks for remote execution. We aim to minimize the weighted sum-energy consumption…
Wireless network optimization has been becoming very challenging as the problem size and complexity increase tremendously, due to close couplings among network entities with heterogeneous service and resource requirements. By continuously interacting with the environment, deep reinforcement learning (DRL) provides a mechanism for different network entities to build knowledge and make autonomous decisions to improve network performance. In this article, we first review typical DRL approaches and recent enhancements. We then discuss the applications of DRL for mobile edge computing (MEC), which …
Ultra-Reliable Cloud Mobile Computing with Service Composition and Superposition Coding
5G系统的一个新兴需求是能够为基于云的应用提供接近完全可用的无线超可靠通信（URC）服务。在这些应用中，移动云计算（MCC），即通过将计算密集型任务从移动设备卸载到云，预计将发挥突出的作用。MCC允许电池有限的设备运行复杂的应用程序，如游戏或“触觉”互联网。本文提出了应用可靠的服务组合的框架，在衰落信道的MCC的最佳任务卸载的问题，提供分层的，或可组合的，在不同的可靠性水平的服务的目的。层间优化问题，包括卸载决策和通信资源，制定和解决的逐次凸逼近方法。数值结果表明，可以通过计算和通信资源的联合分配，以及分层编码的优点，在物理层和信道条件的影响上的卸载决定，可以获得的节能。
In this paper, the problem of joint radio and computation resource management over multi-channel is investigated for multi-user partial offloading mobile edge computing (MEC) system. The target is to minimize the weighted sum of energy consumption by jointly optimizing transmission time, local and edge computation capacity allocation, bandwidth allocation and data partition. An optimization problem is formulated, which is nonconvex and can not be solved directly. Then, we transform the origin optimization problem into an equivalent convex optimization problem. For general case of multi-user mu…
Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer (extended version)
实现长电池寿命甚至自我可持续性一直是设计移动设备的长期挑战。本文提出了一种新型解决方案，无缝集成了移动云计算和微波功率传输（MPT）两种技术，以实现传感器和可穿戴计算设备等无源低复杂性设备中的计算。具体而言，考虑到单用户系统，基站（BS）将功率传输到移动设备或将计算从移动设备卸载到云;移动设备使用收集的能量在本地或通过卸载来计算给定的数据。提出了一种节能计算的框架，该框架包括一组用于控制本地计算模式的CPU周期的策略、MPT和卸载之间的时间划分以及模式选择。给定CPU周期统计信息和信道状态信息（CSI），该策略的目标是在能量收集和截止期限约束下，最大化成功计算给定数据的概率（称为计算概率）.将策略优化转化为最小化本地计算移动能耗和最大化卸载移动节能的等效问题，并使用凸优化理论求解。由此产生的政策的结构的特点是在封闭的形式。此外，给定非因果CSI，所述分析框架被进一步发展以支持多个信道实现上的计算负载分配，这进一步增加了计算概率。最后通过仿真验证了无线供电移动云计算的可行性及其最优控制的增益。
Mobile Edge Computing: A Survey on Architecture and Computation Offloading
移动用户设备（UE）（诸如智能电话或膝上型计算机）的技术演进与新的移动应用的演进齐头并进。然而，在UE处运行计算要求高的应用受到UE的有限电池容量和能量消耗的约束。延长UE电池寿命的合适解决方案是将需要大量处理的应用程序卸载到传统的集中式云（CC）。然而，该选项引入了显著的执行延迟，包括将卸载的应用程序交付到云和返回加上云计算的时间。这种延迟是不方便的，并且使得卸载不适合于实时应用。为了解决延迟问题，引入了一种新的新兴概念，称为移动边缘计算（MEC）。MEC将计算和存储资源带到移动网络的边缘，使得能够在UE处运行高要求的应用，同时满足严格的延迟要求。MEC计算资源也可以被运营商和第三方用于特定目的。在本文中，我们首先描述MEC适用的主要用例和参考场景。在此之后，我们调查现有的概念，将MEC功能的移动网络，并讨论目前的进展，在MEC的标准化。这项调查的核心是，然后，集中在MEC中面向用户的用例，即，计算卸载。在这方面，我们将计算卸载的研究分为三个关键领域：i）计算卸载的决策，ii）MEC内的计算资源分配，iii）移动性管理。最后，我们强调了MEC领域的经验教训，并讨论了尚未解决的开放研究挑战，以充分享受MEC提供的潜力。
This letter investigates a cache-enabled multiuser mobile edge computing (MEC) system with dynamic task arrivals, taking into account the impact of proactive cache placement on the system's overall energy consumption. We consider that an access point (AP) schedules a wireless device (WD) to offload computational tasks while executing the tasks of a finite library in the \emph{task caching} phase, such that the nearby WDs with the same task request arriving later can directly download the task results in the \emph{task arrival and execution} phase. We aim for minimizing the system's weighted-su…
Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependencies and brings new technical challenges. We model the computa…
Deploying mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of MEC server. To address this problem, we first propose an untrusted MEC proof of work scheme in mobile blockchain network where plenty of nonce hash computing demands can be offloaded to MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the u…
Mobile edge computing (MEC) can enhance the computing capability of mobile devices, and non-orthogonal multiple access (NOMA) can provide high data rates. Combining these two technologies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task completion time minimization in NOMA multiuser MEC networks, where multiple users can offload their tasks simultaneously via the same frequency band. We adopt the \emph{partial} offloading, in which each user can partition its computation task into offloading computing and locally computing parts. W…
With the emergence of diverse mobile applications (such as augmented reality), the quality of experience of mobile users is greatly limited by their computation capacity and finite battery lifetime. Mobile edge computing (MEC) and wireless power transfer are promising to address this issue. However, these two techniques are susceptible to propagation delay and loss. Motivated by the chance of short-distance line-of-sight achieved by leveraging unmanned aerial vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is studied. A power minimization problem is formulated subject …
This paper considers non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC), where the power and time allocation is jointly optimized to reduce the energy consumption of offloading. Closed-form expressions for the optimal power and time allocation solutions are obtained and used to establish the conditions for determining whether conventional orthogonal multiple access (OMA), pure NOMA or hybrid NOMA should be used for MEC offloading.
Joint Task Offloading and Resource Allocation for Streaming Application in Cooperative Mobile Edge Computing
针对MEC系统中部分用户因信道遮挡无法直连基站的问题，提出流式任务下联合优化卸载比例、带宽与各步骤时长的低复杂度算法，降低用户及协作节点功耗。
Energy harvesting aided mobile edge computing (MEC) has gained much attention for its widespread application in the computation-intensive, latency-sensitive and energy-hungry scenario. In this paper, computation offloading from multi-MD to multi-MEC-s in heterogeneous MEC systems with energy harvesting is investigated from a game theoretic perspective. The objective is to minimize the average response time of an MD that consists of communication time, waiting time and processing time. M/G/1 queueing models are established for MDs and MEC-ss. The interference among MDs, the randomness in comput…
In this paper, we consider a multi-user mobile edge computing (MEC) network powered by wireless power transfer (WPT), where each energy-harvesting WD follows a binary computation offloading policy, i.e., data set of a task has to be executed as a whole either locally or remotely at the MEC server via task offloading. In particular, we are interested in maximizing the (weighted) sum computation rate of all the WDs in the network by jointly optimizing the individual computing mode selection (i.e., local computing or offloading) and the system transmission time allocation (on WPT and task offload…
In this paper, a multi-vehicle multi-task nonorthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system with passive eavesdropping vehicles is investigated. To heighten the performance of edge vehicles, we propose a vehicle grouping pairing method, which utilizes vehicles near the MEC as full-duplex relays to assist edge vehicles. For promoting transmission security, we employ artificial noise to interrupt eavesdropping vehicles. Furthermore, we derive the approximate expression of secrecy outage probability of the system. The combined optimization of vehicle task division, …
Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency maximization problems are formulated in wireless-powered MEC networks under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated for offloading. The energy harvesting time, the local computing …
A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
本文针对多用户移动边缘计算中的去中心化计算卸载问题，提出基于Twin Delayed DDPG的深度强化学习方法，克服传统DDPG的固有缺陷，使移动用户能自主学得策略并超越传统功率控制策略的性能。
Data compression has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This joint optimization problem is studied in the current paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where data compression is performed only at the mobile users. We prove that the optimal offloading decisions have a thr…
This paper studies a wireless powered mobile edge computing (MEC) system with fluctuating channels and dynamic task arrivals over time. We jointly optimize the transmission energy allocation at the energy transmitter (ET) for WPT and the task allocation at the user for local computing and offloading over a particular finite horizon, with the objective of minimizing the total transmission energy consumption at the ET while ensuring the user's successful task execution. First, in order to characterize the fundamental performance limit, we consider the offline optimization by assuming that the pe…
Epidemic-like Proximity-based Traffic Offloading
蜂窝网络由于移动业务激增而过载，承载信息流的移动社交网络（MSNet）可以帮助降低蜂窝业务负载。如果地理上邻近的用户直接采用WiFi或蓝牙技术（即，利用基于邻近度的通信），可以从蜂窝网络卸载一部分移动业务。对于许多延迟容忍应用，对于流量卸载来说，挑选一些种子用户作为信息源是有益的，这有助于使用基于邻近度的通信以类似于广播的方式将信息进一步传播给其他用户。在本文中，我们开发了一个理论框架来研究只选择k个种子用户的问题，以最大限度地减少移动通信流量从蜂窝网络通过基于邻近的通信。我们引入了一个八卦式的社会级联（GSC）模型来模拟信息扩散过程，它捕捉了基于邻近的通信的类似于网络的性质，并描述了用户的社会参与以及。静态网络作为一个特殊的案例研究和移动网络，我们建立了一个等价的视图和时间映射的信息扩散过程中，分别利用虚拟优惠券收集器。我们进一步证明了子模块的信息扩散，并提出了一个贪心算法选择种子用户的邻近为基础的流量卸载，产生的流量卸载最大化（TOM）问题的最优值的63%内的解决方案。实验结果表明，基于邻近度的通信可以在种子用户数较少的情况下卸载60%以上的蜂窝业务，且贪心算法的性能明显优于启发式算法和随机算法.
Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing
在数量庞大的移动设备上清理空闲的计算资源可以为本地移动云计算提供强大的平台。这一愿景可以通过边缘设备之间的点对点协作计算（称为协同计算）来实现。本文考虑了一个协同计算系统，其中用户将输入数据的计算卸载到助手。帮助者基于预测的帮助者的CPU空闲简档来控制卸载过程，以最小化用户的能量消耗，该预测的帮助者的CPU空闲简档指定用于协同计算的可用计算资源的量。考虑这样一种情况：用户有一次输入数据到达，帮助程序缓冲卸载的位。将节能协同计算问题分解为两个子问题：自适应卸载的从问题和数据划分的主问题。给定固定的卸载数据大小，自适应卸载的目的是通过在截止期限和缓冲区约束下控制卸载速率来最小化用于卸载的能量消耗。通过推导最优解的充分必要条件，我们描述了最优策略的结构，并提出了计算策略的算法。此外，我们表明，最佳的数据分区卸载和本地计算在用户的问题是凸的，承认一个简单的解决方案，使用次梯度法。最后，开发的协同计算的设计方法扩展到突发数据到达用户帐户的数据因果关系约束的情况下。仿真结果验证了所提算法的有效性。
Mobile edge computing (MEC) has been envisaged as a promising technique in the next-generation wireless networks. In order to improve the security of computation tasks offloading and enhance user connectivity, physical layer security and non-orthogonal multiple access (NOMA) are studied in MEC-aware networks. The secrecy outage probability is adopted to measure the secrecy performance of computation offloading by considering a practically passive eavesdropping scenario. The weighted sum-energy consumption minimization problem is firstly investigated subject to the secrecy offloading rate const…
In this article, we consider the problem of relay assisted computation offloading (RACO), in which user A aims to share the results of computational tasks with another user B through wireless exchange over a relay platform equipped with mobile edge computing capabilities, referred to as a mobile edge relay server (MERS). To support the computation offloading, we propose a hybrid relaying (HR) approach employing two orthogonal frequency bands, where the amplify-and-forward scheme is used in one band to exchange computational results, while the decode-and-forward scheme is used in the other band…
Joint Trajectory, RIS, and Computation Offloading Optimization via Decentralized Model-Based PPO in Urban Multi-UAV Mobile Edge Computing
本文提出去中心化模型基于PPO方法，解决密集城市区域多无人机边缘计算中计算卸载、轨迹和RIS相位配置的联合优化问题，提升吞吐量和能效。
A Max-Min Task Offloading Algorithm for Mobile Edge Computing Using Non-Orthogonal Multiple Access
本文提出一种基于NOMA的MEC任务卸载算法，通过最大化最差用户卸载比特数，解决非凸优化问题，相比OMA显著提升性能，适用于延迟敏感应用。
In this paper, we investigate rate splitting multiple access (RSMA) aided mobile edge computing (MEC) in a cognitive radio network. We propose a RSMA scheme that enables the secondary user to offload tasks to the MEC server utilizing dynamic rate splitting without deteriorating the primary user's offloading. The expressions for the optimal rate splitting parameters that maximize the achievable rate for the secondary user and successful computation probability of the proposed RSMA scheme are derived in closed-form. We formulate a problem to maximize successful computation probability by jointly…
Computation offloading becomes useful for users of limited computing power and mobile edge computing (MEC) can help mobile users perform their tasks more effectively. In this paper, we consider MEC when users perform tasks with local data as well as data at cloud storage servers, since users can often keep their data at cloud storage servers or have tasks that need to use public datasets. An optimization problem to minimize the total consumed energy of users as well as a base station (BS) is formulated with a total transmission time constraint, which is solved by converting into multiple binar…
Joint Scheduling and Power Allocations for Traffic Offloading via Dual-Connectivity
随着移动业务需求的快速增长，缓解蜂窝网络拥塞的一种有前途的方法是将用户的业务卸载到小小区网络。在本文中，我们研究了如何移动用户（MU）可以有效地卸载流量，利用双连接的能力，使MU同时与宏基站（BS）和小小区接入点（AP）通过两个无线电接口进行通信。将业务卸载到AP通常降低MU的移动数据成本，但通常以遭受来自同一AP处的其他MU的增加的干扰为代价。因此，我们制定了一个优化问题，共同确定每个MU的业务调度（BS和AP之间）和功率控制（两个无线电接口之间）。系统的目标是最小化所有MU的总成本，同时通过适当的干扰控制满足每个MU的发射功率约束。尽管问题的非凸性，我们设计了一个集中式算法和分布式算法来解决联合优化问题。数值计算结果表明，与商用优化软件LINGO相比，该算法能获得接近最优的结果，但计算复杂性明显降低.结果还表明，所提出的自适应卸载可以显著降低MU的成本，即，在不卸载流量的情况下节省75%以上的成本，在固定卸载的情况下节省65%以上的成本。
This paper studies a wireless powered mobile edge computing (MEC) system with device-to-device (D2D)-enabled task offloading. In this system, a set of distributed multi-antenna energy transmitters (ETs) use collaborative energy beamforming to wirelessly charge multiple users. By using the harvested energy, the actively computing user nodes can offload their computation tasks to nearby idle users (as helper nodes) via D2D communication links for self-sustainable remote computing. We consider the frequency division multiple access (FDMA) protocol, such that the D2D communications of different us…
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