OPTIMAL RESOURCE ALLOCATION AND CROSS - LAYER CONTROL IN COGNITIVE AND COOPERATIVE WIRELESS NETWORKS by Rahul Urgaonkar

We investigate four problems on optimal resource allocation and cross-layer control in cognitive and cooperative wireless networks with time-varying channels. The first three problems consider different models and capabilities associated with cognition and cooperation in such networks. Specifically, the first problem focuses on the dynamic spectrum access model for cognitive radio networks and assumes no cooperation between the licensed (or “primary”) and unlicensed (or “secondary”) users. Here, the secondary users try to avoid interfering with the primary users while seeking transmission opportunities on vacant primary channels in frequency, time, or space. The second problem considers a relay-based fully cooperative wireless network. Here, cooperative communication techniques at the physical layer are used to improve the reliability and energy cost of data transmissions. The third problem considers a cooperative cognitive radio network where the secondary users can cooperatively transmit with the primary users to improve the latter’s effective transmission rate. In return, the secondary users get more opportunities for transmitting their own data when the primary users are idle. In all of these scenarios, our goal is to design optimal control algorithms that maximize time-average network utilities (such as throughput) subject to time-average constraints (such as power, reliability, etc.). To this end, we make use of the technique of Lyapunov x optimization to design online control algorithms that can operate without requiring any knowledge of the statistical description of network dynamics (such as fading channels, node mobility, and random packet arrivals) and are provably optimal. The algorithms for the first two problems use greedy decisions over one slot and two-slot frames, whereas the algorithm for the third problem involves a stochastic shortest path decision over a variable length frame, and this is explicitly solved, remarkably without requiring knowledge of the network arrival rates. Finally, in the fourth problem, we investigate optimal routing and scheduling in static wireless networks with rateless codes. Rateless codes allow each node of the network to accumulate mutual information with every packet transmission. This enables a significant performance gain over conventional shortest path routing. Further, it also outperforms cooperative communication techniques that are based on energy accumulation. However, it requires complex and combinatorial networking decisions concerning which nodes participate in transmission, and which decode ordering to use. We formulate the general problems as combinatorial optimization problems and identify several structural properties of the optimal solutions. This enables us to derive optimal greedy algorithms to solve these problems. This work uses a different set of tools and can be read independently of the other chapters.