2016/04/16 by Medhat Elsayed, Elsayed, Medhat H. M., Amr Mohamed +1
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Technologies #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI)
paper · pdf · doi:10.48550/arxiv.1604.04699
openalex publication_date 2016/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Femtocell networks have become a promising solution in supporting high data\nrates for 5G systems, where cell densification is performed using the small\nfemtocells. However, femtocell networks have many challenges. One of the major\nchallenges of femtocell networks is the interference management problem, where\ndeployment of femtocells in the range of macro-cells may degrade the\nperformance of the macrocell. In this paper, we develop a new platform for\nstudying interference management in distributed femtocell networks using\nreinforcement learning approach. We design a complete MAC protocol to perform\ndistributed power allocation using Q-Learning algorithm, where both independent\nand cooperative learning approaches are applied across network nodes. The\nobjective of the Q-Learning algorithms is to maximize aggregate femtocells\ncapacity, while maintaining the QoS for the Macrocell users. Furthermore, we\npresent the realization of the algorithms using GNURadio and USRP platforms.\nPerformance evaluation are conducted in terms of macrocell capacity convergence\nto a target capacity and improvement of aggregate femtocells capacity.\n