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In response to the rapid growth of future business and data traffic, the widespread deployment of small base stations (BSs) in 5G networks has emerged as a promising solution, albeit intensifying the network’s energy consumption. Additionally, traditional BSs lack adaptive adjustment of parameter information, posing challenges in delivering satisfactory quality of service (QoS), particularly in the context of highly uneven business distribution. Reducing energy consumption while ensuring that QoS represents a critical challenge. To address this issue, this paper first comprehensively considers the interests of both supply and demand, proposing a service utility measurement method in communication networks to achieve a balance between energy consumption and QoS. Furthermore, this paper integrates cell zooming and sleeping strategies for small BSs, designing a dynamic game algorithm aimed at optimizing service utility in a two-tier heterogeneous network. Through ten distinct scenario simulations, our proposed algorithm demonstrates significant enhancements in service utility while achieving near-optimal optimization results more expeditiously compared to the genetic algorithm (GA).
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