Design of cooperative networking protocols in wireless networks through stochastic optimization techniques

Cooperation among nodes of a wireless ad hoc network has been shown to be effective in improving the efficiency of resource usage, e.g., increasing the network throughput or reducing the energy consumption. In recent years, cooperation has been widely studied both from an information theoretic point of view and from an implementation perspective. However, there are different scenario that have still not been addressed. In this thesis, we consider wireless cooperative multi-hop networks, where nodes cooperate to deliver messages from sources to destinations. The term cooperation assumes different connotations throughout the thesis. We consider situations in which nodes cooperate in the transmission of a message, realizing a distributed space-time coding scheme or using the recent concept of “spectrum leasing via cooperation”, and the case of distributed data gathering, where source nodes reduce their acquisition rates (and costs) taking advantage of the spatial and temporal correlation between measures. The first scenario considers wireless cooperative multi-hop networks, where nodes that have decoded the message at the previous hop cooperate in the transmission towards the next hop, realizing a distributed space-time coding scheme. Our objective is to find optimal cooperator selection policies for arbitrary topologies with a single source-destination pair, with links affected by path loss and multipath fading. To this end, we model the network behavior through a suitable Markov chain and we formulate the cooperator selection process as a stochastic shortest path problem (SSP). Further, we reduce the complexity of the SSP through a novel pruning technique that, starting from the original problem, obtains a reduced Markov chain which is finally embedded into a solver based on focused real time dynamic programming (FRTDP). Our algorithm can find cooperator selection policies for large state spaces and has a bounded (and small) additional cost with respect to that of optimal solutions and allows to obtain performance bounds that can be useful for the design of practical protocols. Starting from the results of the centralized solution, we looked at the problem from a different angle, devising three online and fully distributed algorithms which only exploit local interactions for the selection of the cooperators. The proposed techniques are numerically compared against the optimal centralized strategy and competing algorithms from the literature, showing their improvement upon existing distributed approaches and achieving close-to-optimal performance. The positive results obtained for the single source-destination scenario, lead us to study the behavior of wireless networks in the presence of multi-user interference and cooperative transmissions. In this case, our focus is to assess the impact of interference among distinct data flows on optimal routing paths and related transmission schedules. In our reference scenario, all nodes have a single antenna and can cooperate in the transmission of packets. Given that, we first model the cooperative transmission problem using linear programming (LP). Thus, for an efficient solution, we reformulate the joint routing and scheduling problem as a single-pair shortest path problem, which is solved using the A∗ search algorithm through specialized heuristics. Simulation results of the obtained optimal policies confirm the importance of avoiding interfering paths and that interference-aware routing can substantially improve the network performance in terms of throughput and energy consumption, even when the number of interfering paths is small. Once again, our models provide useful performance bounds for the design of distributed cooperative transmission protocols in ad hoc networks. We then move our attention to a cognitive radio scenario and we consider a spectrum leasing strategy for the coexistence of a licensed multihop network and a set of unlicensed nodes. The primary network consists of a source, a destination and a set of additional primary nodes that can act as relays. In addition, the secondary nodes can be used as extra relays and hence potential next hops following the principle of opportunistic routing. Secondary cooperation is guaranteed via the “spectrum leasing via cooperation” mechanism, whereby a cooperating node is granted spectral resources subject to a Quality of Service (QoS) constraint. The objective of this work is to find optimal as well as efficient heuristic routing policies based on the idea outlined above of spectrum leasing via cooperative opportunistic routing. To this end, we start by formulating the problem as a Markov decision process (MDP) and we show that, in particular, the problem can be casted in the framework of stochastic routing. Based on the structure of the optimal policies we derive two heuristic routing schemes that we then numerically compare with the optimal policies. The two proposed heuristic routing policies are shown to perform close to optimal solutions and they are as well tunable in terms of end-to-end throughput vs primary energy consumption. Finally, we address the problem of distributed data gathering in a wireless sensor network powered by energy harvesting. In particular, we consider a scenario in which wireless nodes cooperatively acquire spatial correlated measurements and route the information through the network in order to reach a sink node. Before the transmission, the acquired data is compressed via adaptive lossy source coding by leveraging the spatial correlation of the measurements. By assuming that the acquisition/compression, as well as the transmission, entails energy consumption, we propose an algorithm that minimizes the global distortion level introduced by the distributed source coding technique. At the same time, the proposed algorithm achieves network data queues stability and consumes energy, either for acquisition/compression or transmission, only if it is available. By approaching the problem using the Lyapunov optimization technique, we show that the proposed algorithm determines, in an online fashion, efficient acquisition/compression and routing policies with bounded performance guarantees with respect to the optimal performance.