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

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 (S. Shakkottai and A.L. Stolyar, 2001), 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 S. Kumar and S. Nanda (1999), 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.