Joint transceiver design methods for wireless powered communication networks

Abstract

Wireless powered communication networks (WPCNs) are recently developed networking systems where a dedicated hybrid-access point (H-AP) broadcasts radio-frequency (RF) energy signals to distributed wireless devices and then the wireless devices transmit their independent information to the H-AP using the harvested energy. Since the wireless devices in WPCNs are remotely replenished by RF energy signals from the H-AP, the wireless devices can be free from periodical battery replacement and are able to recharge their batteries without wired energy sources. Consequently, WPCNs based on RF energy transfer technology have great potential to provide energy-constrained wireless devices with the capability of self-sustainability. Accordingly, this thesis considers joint optimization of transceivers for two types of WPCNs: cognitive WPCNs (C-WPCNs) and full-duplex (FD) WPCNs. C-WPCNs consist of primary and secondary networks. In the secondary networks, a secondary H-AP equipped with multiple antennas communicates with energy-constrained secondary users equipped with a single antenna using the frequency resources allocated to primary networks under the condition that the secondary networks do not degrade quality of service (QoS) of primary users. In particular, in this thesis, beamforming and power allocation problems are formulated to maximize the sum rate of the secondary users. A hierarchical decomposition framework is employed to derive efficient algorithms to solve the optimization problems. From simulation results, it can be seen that the proposed algorithms exploiting multiple antennas offer substantial rate gain over the conventional approach. In FD-WPCNs, an FD-enabled H-AP having multiple antennas broadcasts RF energy signals to users, and concurrently each user transmits its own information to the H-AP. Specifically, we focus on the doubly near-far phenomenon, in which users located far from the H-AP experience severe signal attenuation in both the energy harvesting and information transmission phases. Due to this phenomenon, the transmission schemes for sum-rate maximization in general cause an unfair rate distribution among users. As an alternative approach, the proportional fairness (PF) based schemes are known to provide a good balance between the sum rate and fairness of users. Thus, this thesis presents a beamforming and time allocation algorithm to maximize the PF of users' transmission rate in FD-WPCNs. Numerical results demonstrate that the proposed algorithm significantly improves fairness of users compared to the sum-rate maximization scheme. Moreover, robust transceiver design is investigated for more practical environments of FD-WPCNs, where the realistic nonlinear energy harvesting model and imperfect estimation of channel state information (CSI) are considered. Furthermore, the complete FD-WPCNs are presented, in which each user also operates in an FD mode, as well as the H-AP. In the complete FD-WPCNs, The FD-enabled users transmit information signals and receive RF energy signals, simultaneously. Thus, the users are able to recycle some portion of their energy consumed for their information transmission. In this setup, the optimization problems are formulated to maximize the weighted sum rate of users against the uncertainty of CSI. In order to solve the problem, an effective algorithm is derived based on the transformation from the weighted sum rate to the weighted sum of mean-square error. It is shown that the proposed robust algorithms provide robustness against imperfect channel knowledge and benefits of utilizing multiple antennas in FD-WPCNs.