Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems (1702.00606v4)

Abstract: Mobile-edge computing (MEC) and wireless power transfer (WPT) have been recognized as promising techniques in the Internet of Things (IoT) era to provide massive low-power wireless devices with enhanced computation capability and sustainable energy supply. In this paper, we propose a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where a multi-antenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of them to the AP based on a time division multiple access (TDMA) protocol. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the energy transmit beamformer at the AP, the central processing unit (CPU) frequencies and the numbers of offloaded bits at the users, as well as the time allocation among users. Under this framework, we address a practical scenario where latency-limited computation is required. In this case, we develop an optimal resource allocation scheme that minimizes the AP's total energy consumption subject to the users' individual computation latency constraints. Leveraging the state-of-the-art optimization techniques, we derive the optimal solution in a semi-closed form. Numerical results demonstrate the merits of the proposed design over alternative benchmark schemes.

• The paper presents a joint optimization framework that integrates energy beamforming, CPU frequency control, offloading bits, and time allocation for MEC systems.
• It derives semi-closed form solutions for optimal resource allocation, demonstrating significant energy savings over traditional benchmark models.
• The research ensures low-latency task execution in IoT deployments, supporting sustainable operations for high-density, low-power wireless devices.

Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems

In the ongoing evolution of the Internet of Things (IoT), the demand for efficient computation and energy solutions has led to significant research interest in Mobile-Edge Computing (MEC) integrated with Wireless Power Transfer (WPT). The paper, "Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems," explores an innovative approach to enhancing computation capabilities for wireless devices using MEC and WPT.

System Model and Problem Formulation

The authors propose a wireless powered multiuser MEC system where a multi-antenna access point (AP), integrated with an MEC server, transmits wireless power to various users. These users, utilizing the harvested energy, can either process tasks locally or offload them to the AP, following a time division multiple access (TDMA) protocol. This paper primarily focuses on minimizing the AP's energy consumption while adhering to the users' computation latency constraints.

The paper formulates a problem to jointly optimize several factors:

• Energy transmit beamforming at the AP
• Local computing CPU frequencies
• The number of offloaded bits
• Time allocation among users

Optimization Framework

The research introduces a unified design framework for the MEC-WPT system, aiming to balance the energy consumption while ensuring efficient task execution within each block's latency limit. By employing advanced optimization techniques, the authors derive semi-closed form solutions.

Key results include:

• An optimal resource allocation scheme that minimizes the total energy consumption,
• Deriving optimal conditions for local versus offloaded computation,
• Numerical validations demonstrating enhanced performance of the proposed framework over benchmark models.

Numerical Results

The results reveal significant reductions in the AP's energy consumption when applying the optimal design compared to scenarios without joint optimization. The paper carefully discusses the implications of various parameters such as block length, offloading spectrum bandwidth, and task size.

Implications and Future Developments

This research has practical implications for the deployment of self-sustainable MEC systems in IoT applications, involving latency-sensitive tasks. The findings suggest the potential for these systems to thrive in environments with a high density of low-power devices.

Theoretically, the paper adds value by showcasing the effectiveness of integrating beamforming and computational optimization. Future directions could investigate the inclusion of user cooperation strategies under varied network conditions or extend the framework to consider non-linear energy harvesting models for broader applicability.

Overall, this paper offers a comprehensive and insightful contribution to enhancing the operational efficiency of MEC systems through joint optimization in a wireless powered setting.