A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a non-cooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is proved that the stationary optimal transmission scheduling policy is a randomized mixture of two policies that are threshold in the buffer occupancy state. A stochastic approximation algorithm and a Q-learning based algorithm that can adaptively estimate the optimal transmission scheduling policies are then proposed.

A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a non-cooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is proved that the stationary optimal transmission scheduling policy is a randomized mixture of two policies that are threshold in the buffer occupancy state. A stochastic approximation algorithm and a Q-learning based algorithm that can adaptively estimate the optimal transmission scheduling policies are then proposed.

A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a non-cooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is proved that the stationary optimal transmission scheduling policy is a randomized mixture of two policies that are threshold in the buffer occupancy state. A stochastic approximation algorithm and a Q-learning based algorithm that can adaptively estimate the optimal transmission scheduling policies are then proposed. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Wireless operators adopting third-generation (3G) technologies and those migrating from second-generation (2G) to 3G face a number of challenges related to traffic modeling, demand characterization, and performance analysis, which are key elements in the processes of designing, dimensioning and optimizing their network infrastructure. Traditional traffic modeling assumptions used for circuit-switched voice traffic no longer hold true with the convergence of voice and data over packet-switched infrastructures. Self-similar models need to be explored to appropriately account for the burstiness that packet traffic is expected to exhibit in all time scales. The task of demand characterization must include an accurate description of the multiple user profiles and service classes the network is expected to support, with their distinct geographical distributions, as well as forecasts of how the market should evolve over near and medium terms. The appropriate assessment of the quality of service becomes a more complex issue as new metrics and more intricate dependencies have to be considered when providing a varying range of services and applications that include voice, real-time, and non-real time data. All those points have to be considered by the operator to obtain a proper dimensioning, resource allocation, and rollout plan for system deployment. Additionally, any practical optimization strategy has to rely on accurate estimates of expected demand and growth in demand. In this research, we propose a practical framework to characterize the traffic offered to multimedia wireless systems that allows proper dimensioning and optimization of the system for a particular demand scenario. The framework proposed includes a methodology to quantitatively and qualitatively describe the traffic offered to multimedia wireless systems, solutions to model that traffic as practical inputs for simulation analysis, and investigation of demand-sensitive techniques for system dimensioning and performance optimization. We consider both theoretical and practical aspects related to the dimensioning of hybrid traffic (voice and data) for mobile wireless networks. We start by discussing wireless systems and traffic theory, with characterization of the main metrics and models that describe the users’ voice and data demand, presenting a review of the most recent developments in the area. The concept of service class is used to specify parameters that depend on the application type, performance requirements and traffic characteristics for a given service. Then we present the concept of “user profile,“ which ties together a given combination of service class, propagation environment and terminal type. Next, we propose a practical approach to explore the dynamics of user geographical distribution in creating multi-service, multi-class traffic layers that serve as input for network traffic simulation algorithms. The concept of quality-of-service (QoS) is also discussed, focusing on the physical layer for 3G systems. We explore system simulation as a way to dimension a system given its traffic demand characterization. In that context, we propose techniques to translate geographical distributions of user profiles into the actual number of active users of each layer, which is the key parameter to be used as input in simulations. System level simulations are executed for UMTS systems, with the purpose of validating the methodology proposed here. We complete the proposed framework by applying all elements together in the process of dimensioning and optimization of 3G wireless networks using the demand characterization for the system as input. We investigate the effects of modifying some elements in the system configuration such as network topology, radio-frequency (RF) configuration, and radio resource management (RRM) parameters, using strategies that are sensitive to traffic geographical distribution. Case study simulations are performed for Universal Mobile Telecommunications System (UMTS) networks, and multiple system variables (such as antenna tilts, pilot powers, and RRM parameters) are optimized using traffic sensitive strategies, which result in significant improvements in the overall system capacity and performance. Results obtained in the case studies, allied to a generic discussion of the trade-offs involved in the proposed framework, demonstrate the close dependence between the processes of system dimensioning and optimization with the accurate modeling of traffic demand offered to the system. / Ph. D.