Cognitive spectrum management in dynamic cellular environments: A case-based Q-learning approach. (October 2016)

• Record Type: Journal Article
• Authors: Morozs, N.; Clarke, T.; Grace, D.
• Is Part Of: Engineering applications of artificial intelligence. Volume 55(2016:Jul.)
• Page Start: 239
• Page End: 249
• Publication Date: 2016-10
• Subjects: Reinforcement learning -- Case-based reasoning -- Dynamic spectrum access -- Cellular networks; 2ON Second order neighbourhood -- CBR Case-based reasoning -- DSA Dynamic spectrum access -- eNB Evolved node B (LTE base station) -- ICIC inter-cell interference coordination -- LSA Licensed shared access -- RL Reinforcement learning -- UE User equipment -- UT User throughput -- WoLF Win-or-learn-fast; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.07.002
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper examines how novel cellular system architectures and intelligent spectrum management techniques can be used to play a key role in accommodating the exponentially increasing demand for mobile data capacity in the near future. A significant challenge faced by the artificial intelligence methods applied to such flexible wireless communication systems is their dynamic nature, e.g. network topologies that change over time. This paper proposes an intelligent case-based Q -learning method for dynamic spectrum access (DSA) which improves and stabilises the performance of cognitive cellular systems with dynamic topologies. The proposed approach is the combination of classical distributed Q -learning and a novel implementation of case-based reasoning which aims to facilitate a number of learning processes running in parallel. Large scale simulations of a stadium small cell network show that the proposed case-based Q- learning approach achieves a consistent improvement in the system quality of service (QoS) under dynamic and asymmetric network topology and traffic load conditions. Simulations of a secondary spectrum sharing scenario show that the cognitive cellular system that employs the proposed case-based Q- learning DSA scheme is able to accommodate a 28-fold increase in the total primary and secondary system throughput, but with no need for additional spectrum and with no degradation in the primary user QoS.