Optimal resource management in wireless access networks

This thesis presents several simple, robust, and optimal resource management schemes for multihop wireless access networks with the main focus on multi-channel wireless mesh networks (MC WMNs). In this regard, various resource management optimization problems are formulated arid efficient algorithms are proposed to solve each problem. First, we consider the channel as signment problem in MC-WMNs and formulate different resource management problems within the general framework of network utility maximization (NUM). Unlike most of the previously proposed channel assignment schemes, our algorithms can not only assign the orthogonal (i.e., non-overlapped) channels, but also partially overlapped channels. This better utilizes the avail able frequency spectrum as a critical resource in MC-WMNs. Second, we propose two distributed random medium access control (MAC) algorithms to solve a non-convex NUM problem at the MAC layer. The first algorithm is fast, optimal, and robust to message loss and delay. It also only requires a limited message passing among the wireless nodes. Using distributed learning techniques, we then propose another NUM-based MAC algorithm which achieves the optimal performance without frequent message exchange. Third, based on our results on random MAC, we develop a distributed multi-interface multi-channel random access algorithm to solve the NUM problem in MC-WMNs. Different from most of the previous channel assignment schemes in the literature, where channel assignment is intuitively modeled in the form of combinatorial and discrete optimization problems, our scheme is based on formulating a novel continuous optimization model. This makes the analysis and implementation significantly easier. Finally, we consider the problem of pricing and monetary exchange in multi-hop wireless access networks, where each intermediate node receives a payment to compensate for its offered packet forwarding service. In this regard, we propose a market-based wireless access network model with two-fold pricing. It uses relay-pricing to encourage collaboration among the access points. It also uses interference pricing to leverage optimal resource management. In general, this thesis widely benefits from several mathematical techniques as both modeling and solution tools to achieve simple, robust, optimal, and practical resource management strategies for future wireless access networks.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

Optimal SINR-based Random Access

Abstract — Random access protocols, such as Aloha, are commonly modeled in wireless ad-hoc networks by using the protocol model. However, it is well-known that the protocol model is not accurate and particularly it cannot account for aggregate interference from multiple interference sources. In this paper, we use the more accurate physical model, which is based on the signal-to-interference-plus-noise-ratio (SINR), to study optimization-based design in wireless random access systems, where the optimization variables are the transmission probabilities of the users. We focus on throughput maximization, fair resource allocation, and network utility maximization, and show that they entail non-convex optimization problems if the physical model is adopted. We propose two schemes to solve these problems. The first design is centralized and leads to the global optimal solution using a sum-of-squares technique. However, due to its complexity, this approach is only applicable to small-scale networks. The second design is distributed and leads to a closeto-optimal solution using the coordinate ascent method. This approach is applicable to medium-size and large-scale networks. Based on various simulations, we show that it is highly preferable to use the physical model for optimization-based random access design. In this regard, even a sub-optimal design based on the physical model can achieve a significantly better performance than an optimal design based on the inaccurate protocol model. I

1 Optimal and Autonomous Incentive-based Energy Consumption Scheduling Algorithm for Smart Grid

Abstract — In this paper, we consider deployment of energy consumption scheduling (ECS) devices in smart meters for autonomous demand side management within a neighborhood, where several buildings share an energy source. The ECS devices are assumed to be built inside smart meters and to be connected to not only the power grid, but also to a local area network which is essential for handling two-way communications in a smart grid infrastructure. They interact automatically by running a distributed algorithm to find the optimal energy consumption schedule for each subscriber, with an aim at reducing the total energy cost as well as the peak-to-average-ratio (PAR) in load demand in the system. Incentives are also provided for the subscribers to actually use the ECS devices via a novel pricing model, derived from a game-theoretic analysis. Simulation results confirm that our proposed distributed algorithm significantly reduces the PAR and the total cost in the system. I