Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks (2001.10234v2)

Abstract: 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 frequency, and the offloading time and power are jointly optimized to maximize the computation efficiency under the max-min fairness criterion. Two iterative algorithms and two alternative optimization algorithms are respectively proposed to address the non-convex problems formulated in this paper. Simulation results show that the proposed resource allocation schemes outperform the benchmark schemes in terms of user fairness. Moreover, a tradeoff is elucidated between the achievable computation efficiency and the total number of computed bits. Furthermore, simulation results demonstrate that the partial computation offloading mode outperforms the binary computation offloading mode and NOMA outperforms TDMA in terms of computation efficiency.

• The paper formulates and addresses the problem of maximizing computation efficiency in wireless-powered mobile edge computing networks via optimal resource allocation.
• It defines computation efficiency as computed bits per energy and optimizes harvesting, local computing, and offloading parameters under max-min fairness criteria.
• Optimization algorithms proposed in the study achieve significant performance gains over benchmarks and reveal trade-offs between efficiency and computed bits.

Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks

The paper "Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks" focuses on the development of resource allocation strategies to enhance computation efficiency (CE) in wireless-powered mobile edge computing (MEC) systems. This research addresses the challenges of limited computing capabilities and finite battery capacities in mobile devices by leveraging wireless power transfer and computation offloading to mobile edge servers.

Key Points of the Study

1. Problem Formulation: The paper formulates computation efficiency maximization problems for wireless-powered MEC networks considering both partial and binary computation offloading modes. It involves a detailed analysis of resource allocation taking into account a practical non-linear energy harvesting model. The paper evaluates both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) protocols for task offloading.
2. Optimization Objectives: The researchers aim to maximize the CE under a max-min fairness criterion, optimizing parameters such as energy harvesting time, local computing frequency, offloading time, and power. The CE is defined as the ratio of computed bits to consumed energy, providing a metric for evaluating the efficiency of the system.
3. Iterative and Alternative Optimization Solutions: To handle the non-convex nature of the formulated problems, the paper proposes two iterative algorithms and two alternative optimization algorithms based on successive convex approximation (SCA), addressing different operation modes and multiple access schemes.
4. Numerical Results: The simulation results demonstrate that the proposed resource allocation strategies significantly outperform benchmark schemes in ensuring user fairness and improving CE. The paper elucidates the advantages of the partial computation offloading mode over the binary computation offloading mode and highlights the superior performance of NOMA over TDMA in terms of CE.
5. Trade-offs and Theoretical Insights: The research reveals a trade-off between achievable CE and the total number of computed bits. This insight emphasizes the importance of balanced resource management in sustainable and energy-efficient MEC networks.

Implications and Future Directions

The findings of this research have both practical and theoretical implications. Practically, they offer guidelines for designing energy-efficient MEC systems that can effectively manage the trade-off between computation capabilities and energy consumption. Theoretically, the work provides a robust framework for addressing non-convex optimization problems in wireless-powered networks, potentially influencing future studies on energy efficiency in edge computing and related fields.

Future research could explore the integration of more sophisticated machine learning algorithms for real-time resource allocation in dynamic environments. Additionally, further investigation into multi-antenna and other advanced communication techniques could provide additional enhancements in CE, especially in highly dense network scenarios.

Overall, this paper contributes significantly to the ongoing efforts to develop efficient, sustainable, and scalable MEC networks, a critical component of next-generation communication technologies.