Resource Allocation for Hybrid NOMA MEC Offloading

Abstract

Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been recognized as promising technologies for the beyond fifth generation networks to achieve significant capacity improvement and delay reduction. In this paper, the technologies of hybrid NOMA and MEC are integrated. In the hybrid NOMA MEC system, multiple users are classified into different groups and each group is allocated a dedicated time slot. In each group, a user first offloads its task by sharing a time slot with another user, and then solely offloads during a time interval. To reduce the delay and save the energy consumption, we consider jointly optimizing the power and time allocation in each group as well as the user grouping. As the main contribution, the optimal power and time allocation is characterized in closed form. In addition, by incorporating the matching algorithm with the optimal power and time allocation, we propose a low complexity method to efficiently optimize user grouping. Simulation results demonstrate that the proposed resource allocation method in the hybrid NOMA MEC systems not only yields better performance than the conventional OMA scheme but also achieves quite close performance as global optimal solution.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

“…Subsequently, the delay minimization was investigated for the hybrid NOMA-MEC system [78]. The work in [80] defined the objective function balancing the tradeoff between energy consumption and completion delay in hybrid NOMA-MEC systems. A joint power allocation and user clustering problem was investigated in [80], from which power allocation is provided in closed form and user clustering is solved by using a matching theory approach.…”

Joint Task Offloading and Resource Management in NOMA-Based MEC Systems: A Swarm Intelligence Approach

“…The problem can be now understood as maximizing the number of offloading UEs, while minimizing the extra overhead. From (11), (12), (24), W (X, P , F ) can be further decomposed as…”

“…The optimization schemes of offloading decision and resources allocation in NOMA-based MEC systems can be solved by different approaches such as network optimization [11], [7], [12], game theory [6], and machine/deep learning [13]. As an attractive alternative, swarm intelligence has showed its potential in optimizing and analyzing the network performance for years.…”

Decentralized computation offloading for multi-user mobile edge computing: a deep reinforcement learning approach

“…To achieve higher energy efficiency or better computation experience, computation offloading strategies for MEC have been widely investigated recently. For short-term optimization over quasi-static channels, some algorithms have been studied in [6][7][8][9][10][11][12]. In [6], optimal offloading selection and radio resource allocation for mobile tasks was studied to minimize the overall execution time.…”

“…Besides, energy-latency tradeoff has been discussed in [8] with jointly optimized communication and computation resource allocation under the limited energy and sensitive latency. Also, performance of MEC have been further improved with adopting some other emerging technologies such as wireless power transfer [9] and non-orthogonal multiple access (NOMA) [10][11][12]. Particularly, physical layer security is studied in NOMA-based MEC networks in [11], where security of computation offloading is improved in terms of secrecy outage probability and user connectivity is also enhanced.…”