Abstract
To significantly promote Internet of Things (IoT) development, 5G network is enabled for supporting IoT communications without the limitation of distance and location. This paper investigates the channel allocation problem for IoT uplink communications in the 5G network, with the aim of improving the quality of experience (QoE) of smart objects (SOs). To begin with, we define a mean opinion score (MOS) function of transmission delay to measure QoE of each SO. For the sum-MOS maximization problem, we leverage a game-theoretic learning approach to solve it. Specifically, the original optimization problem is equivalently transformed into a tractable form. Then, we formulate the converted problem as a game-theoretical framework and define a potential function which has a near-optimum as the optimization objective. To optimize the potential function, a distributed channel allocation algorithm is proposed to converge to the best Nash equilibrium solution which is the global optimum of maximizing the potential function. Finally, numerical results verify the effectiveness of the proposed scheme.
Highlights
The Internet of Things (IoT) is a system of human-toobject or object-to-object connection that sensors, controller, mechanical and digital machines, objects, animals, or people are interrelated and transfer data over a network by using information technology [1, 2]
The concept of IoT is first mentioned by Kevin Ashton in a presentation he made to Procter Gamble in 1999
We study the channel allocation problem by applying game theory to analyze the distributed decisions made by smart objects (SOs), and perform the learning algorithm to maximize the sum-mean opinion score (MOS) of SOs in the 5G network
Summary
The Internet of Things (IoT) is a system of human-toobject or object-to-object connection that sensors, controller, mechanical and digital machines, objects, animals, or people are interrelated and transfer data over a network by using information technology [1, 2]. Motivated by achieving a real-time and reliable transmission of IoT, a QoE-driven resource allocation scheme is proposed in this paper. Device-to-device communication underlaying cellular networks was investigated in [24] to improve spectral efficiency, and a game-theoretic resource allocation scheme was designed by exploring the inherent competition of spectrum resource among users. We assume that there are some SOs that access to 5G network, and it is looking forward to achieving the effective deployment of IoT without considering the limitation of distance and location We study the channel allocation problem by applying game theory to analyze the distributed decisions made by SOs, and perform the learning algorithm to maximize the sum-MOS of SOs in the 5G network.
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