Mutual Information Accumulation over Wireless Networks: Fundamentals and Applications

Abstract

Future wireless networks will face a compound challenge of supporting large traffic volumes, providing ultra-reliable and low latency connections to ultra-dense mobile devices. To meet this challenge, various new technologies have been introduced among which mutual-information accumulation (MIA), an advanced physical (PHY) layer coding technique, has been shown to significantly improve the network performance. Since the PHY layer is the fundamental layer, MIA could potentially impact various network layers of a wireless network. Accordingly, the understanding of improving network design based on MIA is far from being fully developed. The purpose of this dissertation is to study the fundamental performance improvement of mutual information accumulation over wireless networks and to apply these fundamental results to guide the design of practical systems, such as cognitive radio networks and massive machine type communication networks. This dissertation includes three parts. The first part of this dissertation presents the fundamental analysis of the performance of mutual information accumulation over wireless networks. To begin with, we first analyze the asymptotic performance of mutual information accumulation in an infinite 2-dimensional(2-D) grid network. Then, we investigate the optimal routing in cognitive radio networks with mutual information accumulation and derive the closed-form cooperative gain obtained by applying mutual information accumulation in cognitive radio networks. Finally, we characterize the performance of mutual information accumulation in random networks using tools from stochastic geometry. The second part of this dissertation focuses on the application of mutual information accumulation in cognitive radio networks. An optimization problem is formulated to identify the cooperative routing and optimal resources allocation to minimize the transmission delay in underlay cognitive radio networks with mutual information accumulation. Efficient centralized as well as distributed algorithms are developed to solve this cross-layer optimization problem using the fundamental properties obtained in the first part of this dissertation. Simulation results show that mutual information accumulation can reduce more than $77\%$ delay compared to conventional two-hop transmission in underlay cognitive radio network. The third part of this dissertation focuses on the application of mutual information accumulation in massive machine type communication (massive MTC) network. A new cooperative retransmission strategy is developed for massive MTC networks. Theoretical analysis of the new developed retransmission strategy is conducted using the same methodology developed in the fundamental part of this dissertation. Monte Carlo simulation results and numerical results are presented to verify our analysis as well as to show the performance improvement of our developed strategy.