Radio Resource Allocation Schemes for Mobile Communication Networks Using Neural/Fuzzy Techniques

• Main Authors: Yih-Shen Chen, 陳義昇
• Other Authors: Chung-Ju Chang
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/46xb65

博士 === 國立交通大學 === 電信工程系所 === 92 === To support bursty-transmission and heterogeneous quality of services (QoS) requirements for multimedia services, a well-designed sophisticated radio resource allocation scheme is required to effectively enhance the system utilization. Research has shown that the non-stationarity of work-loads, together with heterogeneous traffic characteristics and QoS constraints of multimedia services, constitute the necessity for applying intelligent techniques in future mobile multimedia networks. In this dissertation, the radio resource allocation schemes by using neural/fuzzy techniques for mobile communication networks are studied. Firstly, the radio resource allocation scheme for TDMA-based mobile communication networks is investigated. The adaptive-network-based fuzzy inference system (ANFIS) is applied to propose a fuzzy resource allocation controller (FRAC). The FRAC is designed in a two-layer architecture and properly selects the capacity requirement of new call request, the capacity reservation for future handoffs, and the air interface performance as input linguistic variables. Therefore, the statistical multiplexing gain of mobile multimedia networks can be maximized in FRAC. Simulation results indicate that FRAC can keep the handoff call blocking rate low without jeopardizing the new call blocking rate. Also, compared to the conventional schemes, FRAC can indeed guarantee QoS contracts and achieve higher system performance. And then, the multi-rate transmission control scheme for WCDMA communication systems is studied. The multi-rate transmission control problem is modelled as a Markov decision process (MDP), where the transmission cost is defined in terms of the QoS parameters for enhancing spectrum utilization subject to QoS constraints. The Q-learning reinforcement algorithm is adopted to accurately estimate the transmission cost and propose a Q-learning-based multi-rate transmission control (Q-MRTC) scheme. In the meanwhile, the feature extraction method and RBFN network are successfully employed for the $Q$-function approximation. The state space and memory storage requirement are then reduced, and the convergence property of $Q$-learning algorithm is improved. Simulation results show that, for a multimedia WCDMA system, the Q-MRTC can achieve higher system throughput and better users' satisfaction while the QoS requirements are guaranteed. Finally, the data access control scheme for multi-cell WCDMA systems is investigated. By using fuzzy Q-learning technique, a novel situation-aware data access manager (FQ-SDAM) is proposed. 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; it appropriately chooses the received interferences from home-cell and adjacent-cell as input linguistic variables and simplifies the multi-cell environment into a single-cell one by applying a perceptual coordination mechanism. Also, the DRS can effectively allocate the resource for non-real-time terminals by adopting a modified exponential rule which takes the interference influence on adjacent cells into consideration. Simulation results show that the 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 environment.