| Resource management plays a vital role in wireless communications systems to efficiently utilize the limit radio resources while supplying required quality of service (QoS). However, the development of resource management scheme encounters issues of inaccurate and uncertain measurements because of channel impairments and user mobility. Fuzzy inference system can be used to solve issues related to the inaccuracy and uncertainty. In this thesis, novel resource management schemes by using a fuzzy inference approach are proposed for cellular code division multiple access (CDMA) systems. In specific, (1) an adaptive fuzzy inference system with a recursive least square (RLS) predictor is proposed to estimate and predict a mobile user's mobility; (2) an adaptive fuzzy inference system with a smoother is deployed to estimate a mobile station (MS)'s location; (3) a fuzzy call admission control (CAC) scheme which makes use of the MS's mobility and location information is proposed. The fuzzy CAC first estimates the newly arriving MS's effective bandwidth, then makes a decision whether to accept or reject the MS's connection request based on the effective bandwidth estimation, MS's mobility and location information, and system status. Numerical results are given to demonstrate the performance of the resource management schemes. It is shown that with the employment of adaptive fuzzy inference systems, an MS's mobility and location information can be obtained with small estimation errors, and the system can achieve high resource utilization, low new call blocking probability and handoff call dropping probability with QoS requirements of outage probability satisfied under the proposed fuzzy call admission control scheme. |
