Situation-aware data access manager using fuzzy Q-learning technique for multi-cell WCDMA systems

Abstract

This paper proposes a novel situation-aware data access manager using fuzzy Q-learning technique (FQ-SDAM) for multi-cell WCDMA systems. The FQ-SDAM contains a fuzzy Q-learning-based residual capacity estimator (FQ-RCE) and a data rate scheduler (DRS). The FQ-RCE can accurately estimate the situation-dependent residual system capacity, and appropriately chooses the received interference powers from the home-cell and adjacent-cell as input linguistic variables, which simplifies the multi-cell environment into a single-cell environment by applying a perceptual coordination mechanism. The DRS can effectively allocate the resource for non-real-time terminals by modifying the exponential rule [10], which considers the effect of interference on adjacent cells. Simulation results show that, compared to the link and interference-based demand assignment (LIDA) scheme proposed in [7], FQ-SDAM can effectively reduce the packet error probability and improve aggregate throughput of the non-real-time services in both the homogeneous and non-homogeneous multi-cell WCDMA environments. Additionally, the modified exponential rule achieves better system performance than the original exponential rule.