Outage capacity and throughput maximization using theoretical and learning-based approaches

This thesis explores two research problems in wireless communications: the optimal channel switching and randomization problem in flat-fading Gaussian noise channels, and channel selection and switching approaches based on the upper confidence bound (UCB) bandit algorithm. In the first part of the thesis, the optimal channel switching and randomization problem is formulated and its solution is characterized for flat-fading Gaussian noise channels with the aim of outage capacity maximization under average power and outage probability constraints. For the single user scenario, it is proved that the optimal solution can always be realized by performing one of the following strategies: (1) Transmission over a single channel with no randomization. (2) Channel switching between two channels with no randomization. (3) Randomization between two parameter sets over a single channel. Hence, the solution can easily be obtained by considering only these three strategies. However, for the multiuser scenario, obtaining the optimal solution can have very high computational complexity. Therefore, an algorithm is proposed to calculate an approximately optimal channel switching and randomization solution (with adjustable approximation accuracy) based on the solution of a linearly constrained linear optimization problem. In the second part of the thesis, we consider the case of unknown channel statistics at the transmitter, and propose channel selection and channel switching approaches based on the UCB bandit algorithm for communications between a transmitter and a receiver over a block fading channel. In the absence of channel switching in a block, we propose a UCB bandit algorithm for selecting the best channel among the possible set of channels for maximizing the number of correctly received symbols per unit of time. In the presence of channel switching, we first define a set of virtual channels by considering all possible channel pairs with various power levels and timesharing factors. Then, a UCB bandit algorithm is utilized to determine the best virtual channel; hence, to find the optimal channel switching strategy. Also, a low complexity version of this algorithm is proposed for efficient convergence to the optimal solution when a high number of virtual channels exists. In addition, for comparison purposes, theoretical limits are presented when the channel statistics are available at the transmitter. Simulation results indicate that the proposed UCB bandit algorithms can achieve very close performance to theoretical limits over a sufficiently large number of blocks, and make benefits of channel switching be realized.