# In-Band Full-Duplex Relaying for SWIPT-Enabled Cognitive Radio Networks
###### Abstract  
This paper studies sum rate maximization of a cognitive radio network, where a full-duplex relay (FDR) is considered to assist data transmission. An FDR equipped with multiple transmit/receive antennas is introduced to harvest energy from the radio frequency signal of the primary system to reuse the energy for its own data transmission. By exploiting the time-switching relaying protocol, we first formulate an optimization problem for the sum rate of primary and secondary receivers and then propose a low-complexity algorithm to find the optimal solution. Numerical results verify the effectiveness of the proposed technique for wireless information and power transfer in cognitive radio systems. c pacs: 98.35.-a, 98.35. In the context of SWIPT-integrated networks, there are many efforts to improve the system performance by applying SWIPT to both FD- and CR-based networks (CRNs). In [22], an FD-enabled system using SWIPT was proposed to improve spectral and energy efficiencies. In CRNs, the performance analysis for EH and system throughput was investigated in [23], indicating the effectiveness of SWIPT. To further enhance the performance for SWIPT-based CRN systems, the authors in [24] developed a model, where an FD-enabled access point simultaneously charges the battery of ST and receives the signals from PT in the first phase, and in the second phase, the ST transmits/forwards the messages to secondary/primary receivers (SR/PR). Although the CRN scheme in [23] could reuse the energy by sensing the appearance of the PT's signal, the conventional EH based on single-input single-output (SISO) cannot exploit the benefit of the multiple-antenna technique. In addition, the use of an energy beam in [22; 24] might lead to inefficient power consumption at the energy beaming sources, i.e., base stations and APs, due to the strong attenuation caused by the path loss. The above discussions motivate us to develop an FD-based CRN that takes advantage of the beamforming technique for SWIPT. This paper investigates a CRN where the data transmission between an ST and an SR is entirely performed via an FDR. Differently from previous works, the data transmission operates in two phases of a time block as in the time-switching relaying (TSR) protocol, where the fraction is optimized so that the sum rate of the primary and secondary systems is maximized. In the first phase, the ST and FDR harvest energy from the primary signal and utilize the harvested energy to transmit their independent signals. With the given spectrum access and harvested energy, in the second phase, the FDR is enabled to assist PT and ST in forwarding the data to PR and SR, respectively. To the best of our knowledge, a thorough performance evaluation of FDR enabled by EH in CRNs has not been reported in the literature. The proposed model brings some advantages: (i) inherited from the property of relaying, the FDR utilizes distances geometrically near receivers for improving the system performance; (ii) FDR is able to exploit the benefit of the multiple-antenna technique to harvest energy and transmit data efficiently; (iii) the proposed model is particularly suitable for CRNs, in that the CRNs can sense the appearances of PT's signals in one block transmission to recycle the energy from PT's signal, instead of requesting the new energy beam from PT. Under the proposed model, we formulate a novel sum rate maximization problem for both SR and PR, which is found to be non-convex programming. To solve the problem efficiently, we then propose a low-complexity iterative algorithm based on the inner convex approximation (ICA) framework. Numerical results are provided not only to demonstrate the effectiveness of the proposed system model and optimization algorithm, but also to show the performance trade-off between the secondary and primary systems under the multiple-antenna technique and fractional time optimization. Notation: \(\mathbf{X}^{H}\) and \(\mathrm{tr}(\mathbf{X})\) are the Hermitian transpose and trace of a matrix \(\mathbf{X}\), respectively.