An optimal resource allocation strategy for MIMO relay system is considered in simultaneous wireless information and energy transfer network, where two users with multiple antennas communicate with each other assisted by an energy harvesting MIMO relay that gathers energy from the received signal by applying time switching scheme and forwards the received signal by using the harvesting energy. It is focused on the precoder design and resource allocation strategies for the system to allocate the resources among the nodes in decode-and-forward (DF) mode. Specifically, optimal precoder design and energy transfer strategy in MIMO relay channel are firstly proposed. Then, we formulate the resource allocation optimization problem. The closed-form solutions for the time and power allocation are derived. It is revealed that the solution can flexibly allocate the resource for the MIMO relay channel to maximize the sum rate of the system. Simulation results demonstrated that the performance of the proposed algorithm outperforms the traditional fixed method.

Wireless power transfer technology, where the receiver can scavenge energy from the received signals, has recently attracted much attention in academia and industry [

The concept of simultaneous wireless information and power transfer (SWIPT) is first proposed by Varshney in [

In this paper, we focus on a general MIMO cooperative network, where two users communicated with each other via an energy harvesting relay. Specifically, each node is equipped with multiple antennas. The relaying transmission is powered by the scavenged energy from the signals sent by the users. Assuming that the battery of the relay is sufficiently large, the relay can accumulate a significant amount of power for relaying transmission. The aim of this paper is to analyze the precoder design for the source node and the relay node to optimize the system performance and study how to efficiently distribute the time resource between information transmission and power transfer. Moreover, the optimal power allocation strategies for the system are also investigated.

This paper is organized as follows. Section

We consider a MIMO energy harvesting (EH) cooperative network with two users and a relay node as shown in Figure

System model of MIMO relay network with energy harvesting.

The relaying protocol with energy harvesting can be stated as follows.

In the first phase (Slot 1), the source terminal transmits the signal to the energy harvesting relay node with precoder matrix

In the second phase (Slot 2), the source terminal transmits the signal to the energy harvesting relay node with precoder matrix

It is noted that the total duration for the first and second phase is half of the total transmission of the relay system. It can maintain the consistence with the conventional relay transmission protocol that the durations of the first hop and the second hop are the same. Then, the consumed energy of the transmitter in proposed protocol is the same with conventional relay transmission protocol. Therefore, the simulation comparison in Section

In the third phase (Slot 3), the relay node broadcasts the signal with precoder matrix

Then, the rate in DF cooperative network with energy harvesting can be presented as

In this section, we formulate the optimal problem to maximize the system throughput. Consider the three-phase MIMO link from the transmitter to the relay node and the relay node to the receiver; the optimization problem can be formulated as follows:

In the following, we propose dividing the original problem into four subproblems. Each subproblem can be solved efficiently by using convex optimization technology.

Consider the MIMO link of the first phase from the transmitter to the relay node; the design objective in this case is to maximize the scavenged power.

Since the objective function is monotonically increasing of

Considering (

Consider the MIMO link of the second phase from the transmitter to the relay node; the design objective in this case is to maximize the data rate over the MIMO channel.

Consider the MIMO link of the third phase from the relay node to the receiver; the design objective in this case is also to maximize the data rate over the MIMO channel. The subproblem can be formulated as

In this section, the performance of MIMO relay network with energy harvesting would be investigated. For comparison, we adopt four reference methods:

First, we evaluate the system throughput of all the mentioned methods. Suppose that each node equipped 4 antennas. The relay node is in the middle of the source terminal and destination terminal. Figure

Achievable sum rate of the SWIPT relay network for different transmission strategy.

Next, considering the total transmit power is 20 dBmW, we investigate the influence on the rate with the different distance between relay node and users. Figure

Achievable sum rate of the SWIPT relay network for different relay position (total transmit power

In this paper, an optimal transmission strategy for DF MIMO relay network with energy harvesting is investigated. It is proposed to design optimal precoders specifically for energy harvesting and information transmission. For energy harvesting, an energy beamforming matrix is designed to scavenge maximum power during the finite time. For information transmission, traditional SVD method is adopted. Moreover, the optimal ratio of the time between energy harvesting and information transmission is also derived. Numerical results show that the proposed method outperforms other traditional methods. In future work, optimal strategy with QoS constraint for multiple relay nodes would be further investigated.

The authors declare that there is no conflict of interests regarding the publication of this paper.

This work was partly supported by the National Natural Science Foundation of China (61340025, 61461029), the Natural Science Foundation of Jiangxi Province (20114ACE00200, 20142BAB217005, and 20142BBE50046), Technology Foundation of Department of Education in Jiangxi Province (no. GJJ13062), and China/Jiangxi Postdoctoral Science Foundation Funded Project (nos. 2013MT541875, 2014MT561879, 2013KY007, and 2014KY046).