Routing and Scheduling Using Column Generation in IEEE 802.16j Wireless Relay Networks

Worldwide Interoperability for Microwave Access (WiMAX) has become an important standard in wireless telecommunication networks in recent years due to the increasing bandwidth requirements, as well as to customer demand for having ubiquitous access to the network. One of the most recent versions of WiMAX is IEEE 802.16-2009, but in this thesis we work with its 802.16j amendment. This amendment includes the use of relay stations (RS) to improve the network's throughput, with the RSs becoming intermediaries between the base station (BS) and the subscriber stations (SS). In the literature, there have been several authors claiming to perform joint routing and scheduling in wireless networks using the column generation technique. Nevertheless, these papers are not performing scheduling since they do not specify how time slots are allocated to each transmitting node over time (they only count the time slots it takes to transmit data). That is why we developed an optimization model (that is solved using column generation) having in mind the fact of performing real scheduling, not only counting time slots but taking into account the allocation of resources over a period of time. The model we developed chooses among a set of possible configurations (a set of transmitting links over a predetermined period of time slots) to calculate the time it takes to transmit data from end to end. After obtaining some simulation results with our model, we compared them with those of a model that does not perform real scheduling. The results show only minor differences in the total number of time slots that a transmission lasts since we can only assign a small number of time slots per configuration

Cross-layer Design for Wireless Mesh Networks with Advanced Physical and Network Layer Techniques

Cross-layer optimization is an essential tool for designing wireless network protocols. We present a cross-layer optimization framework for wireless networks where at each node, various smart antenna techniques such as beam-forming, spatial division multiple access and spatial division multiplexing are employed. These techniques provide interference suppression, capability for simultaneous communication with several nodes and transmission with higher data rates, respectively. By integrating different combinations of these multi-antenna techniques in physical layer with various constraints from MAC and network layers, three Mixed Integer Linear Programming models are presented to minimize the scheduling period. Since these optimization problems are combinatorially complex, the optimal solution is approached by a Column Generation (CG) decomposition method. Our numerical results show that the resulted directive, multiple access and multiplexing gains combined with scheduling, effectively increase both the spatial reuse and the capacity of the links and therefore enhance the achievable system throughput. The introduced cross-layer approach is also extended to consider heterogeneous networks where we present a multi-criteria optimization framework to model the design problem with an objective of jointly minimizing the cost of deployment and the scheduling period. Our results reveal the significant benefits of this joint design method. We also investigate the achievable performance gain that network coding (with opportunistic listening) when combined with Successive Interference Cancellation (SIC) brings to a multi-hop wireless network. We develop a cross-layer formulation in which SIC enables concurrent receptions from multiple transmitters and network coding reduces the transmission time-slot for minimizing the scheduling time. To solve this combinatorially complex non-linear problem, we decompose it to two linear sub-problems; namely opportunistic network coding aware routing, and scheduling sub-problems. Our results affirm our expectation for a remarkable performance improvement when both techniques are jointly used. Further, we develop an optimization model for combining SIC with power control (PC). Our model optimally adjusts the transmission power of nodes to avoid interference on unintended receivers and properly embraces undesired interference through SIC. Therefore, it provides a balance between usage of PC and SIC at the transmitting and receiving sides, respectively. Our results show considerable throughput improvement in dense and heavily loaded networks