Anticipated to substantially enhance communication efficiency, semantic communication emerges as a novel communication paradigm. Considering the constraints of wireless resources, it becomes crucial to design resource allocation schemes that ensure efficient data transmission in a semantic communication system. This paper proposes a resource allocation scheme that maximizes the energy efficiency of the entire semantic network, ensuring the performance of semantic tasks within the confines of limited wireless resources. Specifically, we begin by defining the energy efficiency measurement metrics in semantic communication systems and subsequently optimize it through the joint allocation of semantic symbols, bandwidth, and power. This problem is formulated as an optimization problem. Given the absence of a mathematical closed-form expression for semantic similarity, an effective solution to the problem is proposed via a whale optimization algorithm with a penalty strategy, targeting joint semantic symbols assignment and resource allocation. Simulation results substantiate the effectiveness and feasibility of the proposed scheme.

Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of the existing cellular infrastructures of base carriers while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a nearly two-year measurement study towards understanding various key aspects of today's MVNO ecosystem, including its architecture, performance, economics, customers, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with \reviseabout 1 million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics and machine learning to optimize an MVNO's key businesses such as data plan reselling and customer churn mitigation. Our proposed techniques can help achieve %will lead to higher revenues and improved services for commercial MVNOs.