Edge Computing Aware NOMA for 5G Networks (1712.04980v1)

Abstract: With the fast development of Internet of things (IoT), the fifth generation (5G) wireless networks need to provide massive connectivity of IoT devices and meet the demand for low latency. To satisfy these requirements, Non-Orthogonal Multiple Access (NOMA) has been recognized as a promising solution for 5G networks to significantly improve the network capacity. In parallel with the development of NOMA techniques, Mobile Edge Computing (MEC) is becoming one of the key emerging technologies to reduce the latency and improve the Quality of Service (QoS) for 5G networks. In order to capture the potential gains of NOMA in the context of MEC, this paper proposes an edge computing aware NOMA technique which can enjoy the benefits of uplink NOMA in reducing MEC users' uplink energy consumption. To this end, we formulate a NOMA based optimization framework which minimizes the energy consumption of MEC users via optimizing the user clustering, computing and communication resource allocation, and transmit powers. In particular, similar to frequency Resource Blocks (RBs), we divide the computing capacity available at the cloudlet to computing RBs. Accordingly, we explore the joint allocation of the frequency and computing RBs to the users that are assigned to different order indices within the NOMA clusters. We also design an efficient heuristic algorithm for user clustering and RBs allocation, and formulate a convex optimization problem for the power control to be solved independently per NOMA cluster. The performance of the proposed NOMA scheme is evaluated via simulations.

• The paper introduces an energy-efficient NOMA-MEC framework that minimizes energy consumption through joint allocation of computing and frequency resources.
• It employs a two-phase solution combining heuristic clustering and convex optimization to tackle mixed-integer non-linear challenges in resource management.
• Simulation results show enhanced spectral efficiency and effective meeting of MEC deadline constraints, improving overall QoS in 5G networks.

An Analysis of Edge Computing Aware NOMA for 5G Networks

The proliferation of Internet of Things (IoT) devices and the rapid expansion of mobile data traffic necessitate advanced solutions in wireless communications to improve connectivity and user experience significantly. This paper addresses this need by proposing an innovative integration of Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) within 5G networks to optimize the resource allocation and energy consumption of MEC users.

Overview of NOMA and MEC

NOMA is a multiple access technique that has garnered attention for enhancing the spectral efficiency of 5G networks. It allows multiple users to share the same frequency resource blocks by differentiating users in the power domain. This approach increases network capacity but introduces challenges such as intra-cell interference, necessitating sophisticated Multi-User Detection (MUD) techniques like Successive Interference Cancellation (SIC).

Concurrently, Mobile Edge Computing is gaining traction as a pivotal enabler of low-latency and high-quality service. MEC brings computation capabilities closer to users, thereby reducing the latency associated with cloud-based processing and enhancing the overall Quality of Service (QoS).

Proposed Architecture

The paper presents an edge computing-aware NOMA framework that seeks to minimize the energy consumption of MEC users by optimizing user clustering, communication and computing resource allocation, and transmission power. The authors develop a novel concept of "computing resource blocks" akin to frequency resource blocks, facilitating the joint allocation of these resources to various user orders within NOMA clusters.

Methodologies

A NOMA-based optimization framework is formulated, emphasizing minimizing energy consumption while considering the data rate constraints imposed by users' deadline requirements. The problem is inherently complex, presented as a Mixed Integer Non-Linear Programming (MINLP) challenge. To mitigate this complexity, the authors propose a two-phase solution that employs:

1. Heuristic Algorithm: This algorithm efficiently clusters users and allocates both computing and frequency resource blocks, striking a balance between user demands and available resources.
2. Convex Optimization for Power Control: Post-clustering and resource allocation, the problem of transmission power control per NOMA cluster is handled through a convex optimization framework, simplified by focusing on high Signal-to-Interference-plus-Noise Ratio (SINR) approximations.

Simulation Results

The simulations demonstrate that the heuristic algorithm closely approximates the optimal solution regarding energy consumption while drastically reducing computation time. Key results include:

• Enhanced spectral efficiency and reduced energy consumption are achieved by increasing the number of users sharing frequency resource blocks.
• The efficient division of computing capacity into smaller blocks significantly improves fairness and overall energy consumption.
• The proposed method effectively addresses the deadline constraints of MEC tasks, ensuring timely and energy-efficient computing and communication.

Implications and Future Directions

This research underlines the potential of integrating NOMA with MEC to confront the challenges posed by the burgeoning mobile data traffic and IoT device connectivity requirements. By focusing on energy efficiency and resource allocation, the proposed model enhances the user experience and network capability in 5G environments.

Future research could delve into exploring more robust algorithms that can adapt to dynamic network conditions and scaling to larger network deployments with diverse user requirements. Additionally, advancing SIC techniques to further reduce complexity and improve interference management could significantly bolster the practical applicability of NOMA in 5G networks.

In conclusion, the paper presents a compelling case for leveraging edge-aware NOMA strategies to harness significant gains in energy efficiency and resource management within the evolving landscape of 5G networks.