Control techniques in dynamic communication networks

Power control in the Physical Layer of a communication network is used to ensure that each link achieves its target signal-to-interference-plus-noise ratio (SINR) to effect communication in the reverse link (uplink) of a wireless cellular communication network. In cellular systems using direct-sequence code-division multiple access (CDMA), the SINR depends inversely on the power assigned to the other users in the system, creating a nonlinear control problem. Due to the spreading of bands in CDMA based cellular communication networks, the interference in the system is mitigated. The nonlinearity now arises by the uncertain random phenomena across the radio link, causing detrimental effects to the signal power that is desired at the base station. Mobility of the terminals, along with associated random shadowing and multi-path fading present in the radio link, results in uncertainty in the channel parameters. To quantify these effects, a nonlinear MIMO discrete differential equation is built with the SINR of the radio-link as the state to analyze the behavior of the network. Controllers are designed based on analysis of this networked system, and power updates are obtained from the control law. Analysis is also provided to examine how mobility and the desired SINR regulation range affects the choice of channel update times. Realistic wireless network mobility models are used for simulation and the power control algorithm formulated from the control development is verified on this mobility model for acceptable communication.;In the Medium Access Control (MAC) layer of a wireless network that uses Carrier Sense Multiple Access (CSMA), the performance is limited by collisions that occur because of carrier sensing delays associated with propagation and the sensing electronics, and hidden terminals in the network. A continuous-time Markov model is used to analyze and optimize the performance of a system using CSMA with collisions caused by sensing delays. The throughput of the network is quantified using the stationary distribution of the Markov model. An online algorithm is developed for the unconstrained throughput maximization problem. Further, a constrained problem is formulated and solved using a numerical algorithm. Simulations are provided to analyze and validate the solution to the unconstrained and constrained optimization problems.;Network traffic in the transport layer of end-to-end congestion networks plays a vital role in affecting the throughput in the MAC layer. Common queue length management techniques on nodes in such networks focus on servicing the packets based on their Quality of Service (QoS) requirements (e.g., Differentiated-Services, or DiffServ, networks). In Chapter 4, continuous control strategies are suggested for a DiffServ network to track the desired ensemble average queue length level in the Premium and Ordinary Service buffers specified by the network operator. A Lyapunov-based stability analysis is provided to illustrate global asymptotic tracking of the ensemble average queue length of the Premium Service buffer. In addition, arrival rate delays due to propagation and processing that affects the control input of the Ordinary Service buffer is addressed, and a Lyapunov-based stability analysis is provided to illustrate global asymptotic tracking of the ensemble average queue length of this service. Simulations demonstrate the performance and feasibility of the controller, along with showing global asymptotic tracking of the queue lengths in the Premium Service and Ordinary Service buffers.