Characterizing the next generation wireless networks: Capacity gain, backlog and delay

The next generation wireless networks target at providing better quality of service for ubiquitous network access than nowaday wireless networks. Various technologies from the physical layer to the transport layer are proposed to realize this goal. A fundamental question is how to characterize the impact of a new technology on the performance of wireless networks, e.g., network capacity, backlog and delay. We propose to apply optimization theory for the network capacity characterization and apply stochastic network calculus for the backlog and delay characterization. However, the detailed characterization procedure depends on different problems. In this thesis, we first formally define network capacity, the (optimal) network capacity gain of a new technology, backlog and delay. Then we carry out systematic characterizations on the following three important issues in designing the next generation wireless networks.;First, we give the first formal study on stream control scheduling in wireless mesh networks with Multi-Input-Multi-Output (MIMO) antennas and study how much it can improve network capacity. We derive the upper bound of the optimal network capacity gain of stream control. We also propose an efficient stream control scheduling algorithm, GreedySC. Simulations show the network capacity of GreedySC is much larger than that of a previously proposed stream control scheduling algorithm, SCMA.;Second, we consider leveraging transmission power to improve the network capacity of wireless mesh networks. It is well-known that power control can improve network capacity significantly. However, recent works show conflicting results: network capacity may increase or decrease with higher transmission power under different scenarios. In this work, we give the first systematic study on this paradox. We prove that the the optimal network capacity is a non-decreasing function of higher transmission power. We also derive the upper bound of the optimal network capacity gain of power control. Finally, we give the reasons why network capacity may increase or decrease with higher transmission power in practice. Simulations verify our arguments.;Third, we take the first step to apply stochastic network calculus for the backlog and delay analysis of 802.11 wireless local networks. We prove the general stability condition of deriving stable backlog and delay for a wireless node. From this, we derive the specific stability condition of an 802.11 wireless node. Then we derive the stochastic service curve of an 802.11 node. Based on the service curve, we derive the backlog and delay bounds of the node. Simulations verify our analysis.