Resource Allocation in Full-Duplex Mobile-Edge Computation Systems with NOMA and Energy Harvesting

Yang, Zhaohui; Hou, Jiancao; Shikh-Bahaei, Mohammad

doi:10.1109/icc.2019.8761939

Cited by 16 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If all the mobile tasks are offloaded to the edge server for processing, the amount of data transmission will be too large, the service time of WIT is too long and the service time of WPT is too short, which will eventually cause the mobile device to run out of power and interrupt user services. At the same time, if a large number of mobile tasks are collectively offloaded to a small number of edge servers for processing, considering the limited computing performance and network bandwidth of the edge servers, it will cause serious congestion of computing tasks and significant delays in user services [5]. Therefore, in order to make full use of MEC's computing resources and ensure that mobile devices can provide continuous services, it needs to design a strategy which can determine the amount of offloaded data and the target server for mobile tasks, so that the edge server and mobile devices can perform collaborative processing to effectively improve the user's service quality.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

Luo

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Combined with wireless power transfer (WPT) technology, mobile edge computing can provide continuous energy supply and computing resources for mobile devices, and improve their battery life and business application scenarios. This paper first designs the mobile edge computing (MEC) model of mobile devices with random mobility and hybrid access point (HAP) with data transmission and energy transmission. On this basis, the selection of target server and the amount of data offloading are taken as the learning objectives, and the task offloading strategy based on multi-agent deep reinforcement learning is constructed. Then combined with MADDPG algorithm and SAC algorithm, the problems of multi-agent environment instability and the difficulty of convergence are solved. The final experimental results show that the improved algorithm based on MADDPG and SAC has good stability and convergence. Compared with other algorithms, it has achieved good results in energy consumption, delay and task failure rate. INDEX TERMS Mobile edge computing, task offloading, wireless power transfer, multi-agent, deep reinforcement learning.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

Luo

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In [15], the resource allocation problem among different groups of users transmitting via NOMA was studied. In [16], a full-duplex MEC system with NOMA and energy harvesting was proposed, where the base station can receive the computation tasks offloaded by users via NOMA and broadcast energy signals to users simultaneously. In [17], NOMA is exploited for both task offloading and result downloading in a MEC system.…”

Section: Introductionmentioning

confidence: 99%

System Delay Minimization for NOMA-Based Cognitive Mobile Edge Computing

et al. 2020

View full text Add to dashboard Cite

In this paper, we first employ non-orthogonal multiple access (NOMA) in a cognitive radio (CR) based mobile edge computing (MEC) network to reduce the system delay. In particular, each secondary user has two computation tasks which can be offloaded to the MEC servers via NOMA, while the transmissions of different secondary users are orthogonal by exploiting different spectrum bands. Then, we investigate the system delay minimization problem by jointly optimizing the offloading policy, the transmit power for offloading, the computing capabilities for local secondary users and MEC servers. Since the formulated problem is non-convex and difficult to be solved straightforwardly, we first analyze its characteristics and decompose it into four sub-problems, which can be solved independently. After that, we propose a low-complexity algorithm to obtain the sub-optimal solution. Simulation results prove the superiority of the proposed scheme in terms of the system delay compared with the equal allocation scheme. Our proposed scheme can achieve a sub-optimal solution which is very close to the optimal solution with low complexity compared with the Branch and Bound method. INDEX TERMS NOMA, cognitive radio, mobile edge computing, resource allocation.

show abstract

“…In [8], energy consumption is reduced by jointly optimizing power and time allocation by formulating the problem to a form of geometric programming. [9] studied energy harvesting for full duplex NOMA-MEC, where total energy consumption is minimized by efficient power allocation, time scheduling and computing resources allocation.…”

Section: Introductionmentioning

confidence: 99%

Minimizing the Transaction Time Difference for NOMA-Based Mobile Edge Computing

Kiani

Hassan

et al. 2020

IEEE Commun. Lett.

View full text Add to dashboard Cite

Non orthogonal multiple access (NOMA) and mobile edge computing (MEC) are evolving as key enablers for fifth generation (5G) networks as this combination can provide high spectral efficiency, improved quality-of-service (QoS), and lower latency. This letter aims to minimize the transaction time difference of two NOMA paired users offloading data to MEC servers by optimizing their transmission powers and computational resources of severs using a successive convex approximation method. The equalization of transaction time for paired users reduces the wastage of both frequency and computational resources, and improves effective throughput of the system to 19% on average.

show abstract

Resource Allocation in Full-Duplex Mobile-Edge Computation Systems with NOMA and Energy Harvesting

Cited by 16 publications

References 35 publications

Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

System Delay Minimization for NOMA-Based Cognitive Mobile Edge Computing

Minimizing the Transaction Time Difference for NOMA-Based Mobile Edge Computing

Contact Info

Product

Resources

About