Transceiver optimization and design for wireless energy and information transfer system

Abstract

In conventional energy-constrained and battery-limited wireless communication networks, the sustainable battery of each device is one key factor. Sometimes, it is expensive to replace batteries or recharge from alternating current (AC) power supplies, and it is not even feasible for some applications such as wireless sensors in IoT and medical implanted devices in body. Since radio frequency (RF) radiation can convey information as well as energy at the same time, wireless energy and information transfer technologies with RF radiation have been considered as a promising solution to supply energy to remote devices.

Recently, there have been two main research topics on energy harvesting (EH): simultaneous wireless information and power transfer (SWIPT) and wireless powered communication network (WPCN). The first topic is SWIPT which transmits simultaneously energy and information. There are two different practical receivers such as a time switching receiver which switches alternately between EH and information decoding (ID) in a time division, and a power splitting receiver which splits the received signal into two streams for EH and ID. The second topic is WPCN, in which a hybrid access point (H-AP) broadcasts wireless energy and each user sends its own information sequentially consuming the harvested energy. This is well-suitable for EH sensors in wireless sensor networks.

In this dissertation, we first consider the point-to-point (P2P) multi-input-multiple-output (MIMO)-SWIPT for a single-user case, and then we extend it to two different scenarios, i.e., SISO/MISO WPCNs and MIMO-SWIPT NOMA system for a multi-user case. In the first part of this dissertation, we design a practical MIMO-SWIPT scheme to maximize the average signal-to-error-plus-noise ratio (SENR) with joint channel estimation, training design, transmit (Tx) power allocation, and receive (Rx) power splitting under average harvested energy constraints at the receiver. Specifically, we present the optimal solution in iterative-form, and consider a lower bound of the total SNER and derive a suboptimal solution in closed-form, which achieves the asymptotic optimality with low computational complexity in high signal to noise ratio (SNR) regime. We evaluate the performances of the proposed schemes with simulations.

In the second part, we consider two different practical WPCN and MIMO-NOMA schemes with SWIPT for a multi-user case. Firstly, we propose the optimal energy-efficient WPCN harvest-then-transmit scheme under QoS-constraint in SISO/MISO systems. Considering fairness among users, we present a time resource allocation scheme to maximize the minimum of individual energy efficiency (EE), defined by a ratio of the user throughput to its harvested energy. Since the EE optimization problem is given in non-convex form, it is difficult to directly solve the problem. In order to tackle it, we initially rewrite the EE optimization problem in equivalent subtractive parameterized form, and we derive the corresponding optimal solution with a two-layer iterative algorithm. We show that the proposed scheme gives better EE performances than conventional schemes, and that the fairness-based algorithm considerably outperforms the other schemes in terms of the minimum user EE. Extending the MISO WPCN system, we optimize energy transmit covariance at the power transmitter, the power allocation at the users and the time allocation for energy and information transfer. The simulation results show that the proposed scheme outperforms the throughput maximization scheme in terms of EE.

Secondly, we study user grouping, precoding design, and power allocation for multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) systems with wireless power transfer. An optimization problem is formulated to maximize sum rate under transmit power constraint at a base station and rate constraints and EH constraints at users, which is nonconvex and combinatorial, and thus, is very challenging to solve. To tackle this problem, we carry out the optimization in two steps. In the first step, exploiting the machine learning technique, we develop an efficient iterative algorithm for the user grouping and precoding design. In the second step, we develop a power allocation scheme in closed form by recasting the original problem into a useful and tractable convex form. %Numerical results demonstrate that the proposed %scheme considerably outperforms the existing schemes.

The performance of the proposed schemes is evaluated through simulations. Numerical results show that in wireless energy and information transmission systems, the proposed schemes significantly improve the performance of channel estimation, energy efficiency, and data transmission rates compared to the existing methods.