Abstract
Mobile edge computing (MEC) is fast becoming a key communication technique by enabling mobile users to offload their computation tasks to the edge servers. However, the computation resource of each MEC server is limited which may lead to a worse offloading experience of dense edge users. Besides, the communication and computation resources are usually unevenly distributed among different MEC servers which affect the computational efficiency of the MEC network. In this paper, we propose a non-orthogonal multiple access (NOMA) assisted MEC system with two near-far edge servers performing cooperative communication, i.e., the edge user employs NOMA to offload partial computation workloads to a nearer MEC server and a farther MEC server, then the nearer server decodes and forwards the farther server's task data by full-duplex relaying mode. Based on the above system model, we formulate an optimization problem of the total system energy consumption minimization by jointly optimizing the local CPU frequency, the power allocation for the user and nearer MEC server, the system time assignment and the task partition. Due to the optimization problem is non-convex, a joint communication and computation resource iterative optimization (JCCRIO) algorithm based on approximation and alternation is designed. Firstly, the local CPU frequency is optimized so as to transform the original problem into a simplified equivalent form. In this way, then the simplified minimization problem can be solved iteratively in two steps. Finally, we obtain the closed-form solutions to the optimization variables at each step. Numerical results show that the proposed NOMA-assisted cooperative MEC scheme is more effective against the terms of energy consumption reduction than comparable schemes.
Highlights
In recent years, the ever-increasing number of mobile terminals has led to the data traffic appearing a trend of exponential growth, as well as a variety of well-received application programs are widely used, e.g., augmented reality, face recognition and ultra-high-definition video streaming
To address the formulated problem, we present a joint communication and computation resource iterative optimization (JCCRIO) algorithm based on approximation and alternation that the original problem can be solved by two steps after local frequency optimization
NUMERAL RESULTS we investigate the performance of proposed non-orthogonal multiple access (NOMA)-assisted cooperative mobile edge computing (MEC) system by some simulation results with JCCRIO algorithm
Summary
The ever-increasing number of mobile terminals has led to the data traffic appearing a trend of exponential growth, as well as a variety of well-received application programs are widely used, e.g., augmented reality, face recognition and ultra-high-definition video streaming. These applications usually require a large amount of computation in an extremely short time, which put a heavy computing pressure on the mobile terminals with limited computation resources. Service scope of cellular network can cover the majority of user areas, the edge users may still suffer intolerable transmission delay and lower quality of network experience when they are confronted with a huge volume of data.
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