Reinforcement‐learning‐based self‐organisation for cell configuration in multimedia mobile networks

Abstract

SUMMARYIn future wireless code division multiple access (WCDMA) cellular networks, random user mobility and time-varying multimedia traffic activity make the system design of coverage and capacity become a challenging issue. To utilise radio resource efficiently, it is crucial for cellular networks to have the capability of self-organisation for cell configuration, which can configure service coverage and system capacity dynamically to balance traffic loads among cells by being aware of the system situation. This paper proposes a reinforcement-learning-based self-organisation scheme for cell configuration in multimedia mobile networks, which takes into account both pilot power allocation and call admission control mechanisms. Simulation results show that the proposed scheme improves system performance significantly compared to the conventional fixed pilot power allocation scheme and the scheme in which only pilot power is adjusted dynamically but the criterion of the call admission control is not coupled to it.

On achieving self-organization in mobile WiMAX network

“…Operators are able to add a new BS for the better performance of adjacent cells or areas. Liao and et al [12] also proposed a self-organization scheme for cell configuration. They used reinforcement-learning technique to dynamically adjust pilot frequency and data frequency allocation ratio.…”

Learning‐based resource allocation in D2D communications with QoS and fairness considerations

“…In the works of Nie and Haykin, authors proposed a Q‐learning–based approach for dynamic channel allocation in cellular networks. In the work of Liao et al, the author proposed a learning‐based self‐organization scheme for cell configuration in multimedia mobile networks, and the performance of system is improved compared with conventional fixed pilot power allocation schemes without the prior knowledge requirement of state transition probabilities associated with the cellular networks, which are very difficult to estimate.…”