Simultaneous Wireless Information and Power Transfer for MIMO Interference Channel Networks Based on Interference Alignment

Abstract

This paper considers power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) for multiple-input multiple-output (MIMO) interference channel networks where multiple transceiver pairs share the same frequency spectrum. As the PS model is adopted, an individual receiver splits the received signal into two parts for information decoding (ID) and energy harvesting (EH), respectively. Aiming to minimize the total transmit power, transmit precoders, receive filters and PS ratios are jointly designed under a predefined signal-to-interference-plus-noise ratio (SINR) and EH constraints. The formulated joint transceiver design and power splitting problem is non-convex and thus difficult to solve directly. In order to effectively obtain its solution, the feasibility conditions of the formulated non-convex problem are first analyzed. Based on the analysis, an iterative algorithm is proposed by alternatively optimizing the transmitters together with the power splitting factors and the receivers based on semidefinite programming (SDP) relaxation. Moreover, considering the prohibitive computational cost of the SDP for practical applications, a low-complexity suboptimal scheme is proposed by separately designing interference-suppressing transceivers based on interference alignment (IA) and optimizing the transmit power allocation together with splitting factors. The transmit power allocation and receive power splitting problem is then recast as a convex optimization problem and solved efficiently. To further reduce the computational complexity, a low-complexity scheme is proposed by calculating the transmit power allocation and receive PS ratios in closed-form. Simulation results show the effectiveness of the proposed schemes in achieving SWIPT for MIMO interference channel (IC) networks.

PHY‐layer Security of MIMO‐NOMA with SWIPT

“…The complexity for the SDR‐oriented problem , as discussed for example, in Reference is where n it is the number of iterations. More specifically, the simulations are performed through 1000 iterations within 8600 seconds.…”

Transceiver Design for Wireless Power Transfer for Multiuser MIMO Communication Systems

“…The convergence of Algorithm 3 is given by the following proposition [14]. Proposition 10 If (53) is feasible for the initial receivers U k , ∀k, the convergence to a locally optimal solution can be guaranteed by Algorithm 3.…”

“…For the sake of brevity, we omit the proof to these propositions. Interested readers are suggested to refer to [12][13][14][15][16]. Proposition 1 reveals that the feasibility of the original problem (7) is irrelevant to the EH constraints, while Proposition 2 further shows that the EH constraints are irrelevant to the feasibility.…”

A Novel Codeword Selection Scheme for MIMO-MAC Lower-Bound Maximization

“…The limited feedback of IA shares the same codebook between the transmitter and receiver, and the receiver quantizes the channel matrix or precoding according to the obtained CSI and sends feedback for the location index of the quantization codeword [ 3 , 4 ]. Because the quantized channel matrix has a larger dimension than the quantized precoding matrix, the quantized precoding scheme has been widely studied [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. This paper proposes a MIMO Multiple Access Channel (MIMO-MAC) limited feedback IA algorithm that maximizes the rate lower-bound of the system user.…”