| The problem of energy consumption caused by the growing development of the communication technology has attracted lots of attention from academics and related communication organizations, and the goal of reducing the energy consumption of future wireless communication system has been proposed. The emergence of energy harvesting technology opens up a new way for self-sustained devices in the system. Wireless networks powered by ambient energy sources such as heat and solar are being researched for future wireless communication designs. However, for some applications, such as wireless sensor nodes which are located in unaccessible regions, the charging of batteries remains a major problem. Research on radio-frequency (RF) energy harvesting is being inves-tigated as a solution or partial solution to overcome these problems.The physical scarcity of spectrum resources and the hyper densification of network deployment caused by the rapid growth of data traffic impose new challenges of the wireless coverage and network throughput limitations for the next generation wireless networks. Therefore, RF energy harvesting based cog-nitive radio networks and heterogeneous cellular networks are considered as two significant paradigms of future wireless networks. In this dissertation, by applying stochastic geometry and information theory, the network throughput in large-scale wireless communication networks with RF energy harvesting is developed and analyzed as follows.1. The performance of large-scale cognitive radio networks with sec-ondary users sustained by opportunistically harvesting RF energy from nearby primary transmissions (PTs) is investigated. A variable power transmission mode is proposed, and an energy-based opportunistic spectrum access (OSA)strategy is considered, under which a secondary transmitter (ST) is allowed to transmit only if its harvested energy is larger than a predefined transmission threshold and it is outside the guard zones of all active PTs. The transmission probability of the STs is derived. The outage probability and the throughput of the primary and the secondary networks, respectively, are characterized. Com-pared with prior work, the transmission probability and the network throughput with the energy-based OSA strategy have been both significantly improved.2. A cognitive device-to-device (D2D) network with D2D transmitters(DTs) that harvest RF energy from the PTs is investigated. A novel D2D trans-mitter assisted cooperative (DTAC) protocol is proposed, in which a group of DTs that have no transmission opportunity act as potential relays to improve the communications of the primary network. The outage probability of the primary network is characterized and used to make comparisons between the direct link and the cooperative link which adopts different combining techniques at the primary receivers. The active probability of the DTs is derived, and the D2D network throughput is maximized by finding an optimal transmission power for the PTs.3. A large-scale heterogeneous cellular network (HCN) consisting of ultra-dense small cells and macro cells is investigated. A mixture small cell base stations deployment (M-SBSD) strategy is considered, in which composes of a tier of macro BSs (MBSs), and a mixture tier of on-grid and off-grid small BSs (SBSs), respectively. Each SBS has a personal user which is located at a fix distance away in a random direction. The on-grid SBSs are powered by the electric grid, and the off-grid SBSs have no other power supply but harvest RF energy broadcasted by all the MBSs and the on-grid SBSs. We optimize the on/off-grid coefficients to maximize the energy efficiency, and the results show the importance of hybrid deployment of SBSs. Furthermore, the outage probability and the network throughput are characterized by considering the effect of the density of the SBSs, the cell association biases, and the traffic load conditions. |
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