An adaptive rate controller (ARC) based on an adaptive neural fuzzy inference system (ANFIS) is designed to autonomously adjust the data rate of a mobile heterogeneous network to adapt to the changing traffic load and the user speed for multimedia call services. The effect of user speed on the handoff rate is considered. Through simulations, it has been demonstrated that the ANFIS-ARC is able to maintain new call blocking probability and handoff failure probability of the mobile heterogeneous network below a prescribed low level over different user speeds and new call origination rates while optimizing the average throughput. It has also been shown that the mobile cognitive wireless network with the proposed CS-ANFIS-ARC protocol can support more traffic load than neural fuzzy call-admission and rate controller (NFCRC) protocol.

A finite buffer shared by multiple packet queues is considered. Partitioning the buffer to maximize total throughput is formulated as a resource allocation problem, the solution is shown to be achieved by a greedy incremental algorithm in polynomial time. The optimal buffer allocation strategy is applied to different models for a wireless downlink. First, a set of parallel M/M/1/mi queues, corresponding to a downlink with orthogonal channels is considered. It is verified that at high load, optimal buffer partitioning can boost the throughput significantly with respect to complete sharing of the buffer. Next, the problem of optimal combined buffer allocation and channel assignment problems are shown to be separable in an outage scenario. Motivated by this observation, buffer allocation is considered in a system where users need to be multiplexed and scheduled based on channel state. It is observed that under finite buffers in the high load regime, scheduling simply with respect to channel state with a simply partitioned buffer achieves comparable throughput to combined channel and queue-aware scheduling.

An adaptive service framework is expected to support real-time multimedia services in wirless/mobile cellular networks with various classes of traffic and diverse bandwidth requirements. Quality of service (QoS) provisioning in an adaptive framework is another challenging consideration, such as quantifying the level of bandwidth degradation of an ongoing calls and guaranteeing stable QoS levels. Considering both the period and the depth of degradation, the degradation area ratio (DAR) represents the average ratio of a call's degradation and is one of the meaningful measures for adaptive service in call level analysis. In this paper, analytical models for estimating the DAR and finding the optimal control parameters are presented in multi-class traffic call management situations. In complete partitioning capacity based threshold-type call admission control (CAC), a one-dimensional Markov chain with an absorbing state is proposed for estimating the DAR in each traffic class. We formulate a two-leveled optimization problem minimizing the total blocking probabilities subject to QoS requirements and present the procedures required in finding the optimal capacities and threshold values by using modified dynamic programming. In complete sharing capacity based threshold-type CAC, the multidimensional Markov model is approximately reduced to a one-dimensional model in order to reduce complexity and hence calculation time. The reduced model is compared with multidimensional Markov model in numerical examples. The optimization problem is formulated minimizing the total blocking probabilities subject to QoS requirements and the optimal threshold parameters are found by using a genetic algorithm. Performance of two adopted admission policies in adaptive framework situations is illustrated by numerical results.