Implementing caching to ultra-densely deployed small cells provides a promising solution for satisfying the stringent quality of service (QoS) requirements of delay-sensitive applications with limited backhaul capacity. With the rapidly increasing energy consumption, in this chapter, the authors investigate the NP-hard energy-efficient context-aware resource allocation problem and formulate it as a one-to-one matching problem. The preference lists in the matching are modeled based on the optimum energy efficiency (EE) under specified matching, which can be obtained by using an iterative power allocation algorithm based on nonlinear fractional programming and Lagrange dual decomposition. Next, on account of the Gale-Shapley algorithm, an energy-efficient matching algorithm is proposed. Some properties of the proposed algorithm are discussed and analyzed in detail. Moreover, the authors extend the algorithm to the matching with indifferent and incomplete preference lists. Finally, the significant performance gain of the proposed algorithm is demonstrated through simulation results.
Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT
