Bing Fang

AN-Aided Secrecy Precoding for SWIPT in Cognitive MIMO Broadcast Channels

In this letter, we study the secrecy precoding problem for simultaneous wireless information and power transfer (SWIPT) in a cognitive multiple-input multiple-output (MIMO) broadcast channel. We adopt an artificial noise (AN)-aided precoding scheme and formulate the problem as a secrecy rate maximization (SRM) problem, which is subject to both an interference power constraint imposed to protect the primary user (PU) and an energy harvesting constraint required by the secondary energy receiver (SER). Since the formulated SRM problem constitutes a difference convex (DC)-type programming problem, we solve it by employing a successive convex approximation (SCA) method. With the SCA method, the nonconvex part of the SRM problem can be locally linearized to its first-order Taylor expansion. Then, relying on solving a series of convexified optimization problems, an iterative precoding algorithm is developed. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can achieve a near-optimal performance with guaranteed convergence.

Precoding and Artificial Noise Design for Cognitive MIMOME Wiretap Channels

In this paper, we study the secrecy precoding problem for a cognitive multiple-input-multiple-output multiple-eavesdropper (MIMOME) wiretap system. The problem is studied with an artificial noise (AN)-aided precoding scheme. First, we place AN in the null space of the legitimate receivers channel matrix and formulate a secrecy rate maximization (SRM) problem, which is subject to both an interference power constraint imposed to protect the primary user (PU) and a maximum transmit power constraint available for the secondary transmitter. Second, we drop the null space constraint and reformulate the SRM problem. Because the formulated SRM problems naturally constitute difference-convex-type programming problems, we solve them by employing a successive convex approximation method, where the nonconvex parts of each problem are approximated by their first-order Taylor expansion. Thus, the SRM problems can be iteratively solved through successive convex programming of their convexified versions. Results show that our algorithms can achieve a satisfactory solution with guaranteed convergence.

RAISE: A New Fast Transmit Antenna Selection Algorithm for Massive MIMO Systems

In this paper, we study the problem of transmit antenna selection for massive multiple-input multiple-output systems by maximizing the determinant modulus of the selected channel matrix. Based on the maximum-volume submatrix finding method, we propose a real-time antenna-by-antenna iterative swapping enhancement (RAISE) transmit antenna selection algorithm with low memory cost and low computational complexity. The convergence of the proposed algorithm is proved and the performance of it is evaluated via numerical simulations. Our results show that, compared to the traditional antenna selection algorithms, RAISE can achieve near optimal capacity performance while the computational complexity and the memory cost are significantly reduced.

Distributed precoding for wireless information and power transfer in MIMO DF relay networks

In this paper, we focus on the problem of distributed precoding for simultaneous wireless information and power transfer (SWIPT) in a decode-and-forward (DF) multiple-input multiple-output (MIMO) relay network with two heterogeneous destination nodes, i.e., an information decoding (ID) receiver and a RF energy harvesting (EH) receiver. Since full channel state information (CSI) is usually unavailable in practice, we consider a practical scenario in this paper, where only local transmit CSI is available. Such a scenario is naturally constituted as a noncooperative game. Then, it is proved that the existence and uniqueness of the pure strategy Nash Equilibrium (NE) of this game are both guaranteed. Next, we propose a distributed precoding algorithm based on best response dynamic. Finally, numerical results are provided to evaluate the performance of the proposed algorithm. It is shown that the convergence of the proposed algorithm is very fast.

Game-Theoretic Precoding for SWIPT in the DF-Based MIMO Relay Networks

In this paper, we study the distributed precoding problem for simultaneous wireless information and power transfer (SWIPT) in decode-and-forward (DF)-based multiple-input-multiple-output (MIMO) relay networks. The system model considered here consists of a source, a relay, an information decoding (ID) receiver, and an energy harvesting (EH) receiver, all mounted with multiple antennas. Since full channel state information (CSI) is usually unavailable, a practical scenario, where only local CSI is required, is considered in this paper. Such a practical scenario naturally constitutes a noncooperative game, where the source and the relay can be regarded as two rational game players. With properly designed utilities, it can be further shown that the existence and uniqueness of the pure-strategy Nash equilibrium (NE) of the proposed game can both be guaranteed under some mild conditions. Therefore, a distributed iterative precoding algorithm can be developed based on the best-response dynamic to obtain the unique NE solution for the proposed game. Moreover, a proximal-point-based regularization approach is also pursued to ensure the convergence of the proposed algorithm without requiring special restrictions on the channel ranks. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can converge quickly to a satisfactory solution with guaranteed convergence.

Optimal precoding for simultaneous information and power transfer in MIMO relay networks

In this paper, we focus on the problem of optimal precoding for simultaneous wireless information and power transfer (SWIPT) in a two-hop decode-and-forward (DF) multiple-input multiple-output (MIMO) relay network, which consists of a source, a relay station, an information decoding (ID) receiver and an energy harvesting (EH) receiver. Each node in this relay network is assumed to employ multiple antennas. In order to better character the optimal tradeoff between simultaneous information and power transfer in MIMO relay networks, a 3D rate-energy (R-E) region is first defined. In addition, an algorithm based on convex programming is proposed to effectively compute the R-E region. Finally, numerical simulations are provided to evaluate the proposed algorithm. It is found that the optimal tradeoff between the maximum information and power transfer locates on the contour of the 3D regions boundary.

Game Theoretic Resource Allocation for D2D MIMO Heterogenous Networks

In this paper, we formulate a sum-rate maximization problem for device-to-device (D2D) communications underlaying downlink multiple-input multiple-output heterogenous cellular networks subject to the quality of service constraints and the interference temperature constraint. Since it is difficult to obtain the optimal solution due to the high complexity, game theory is used to study the resource allocation in such networks. The proposed game is a potential game where the existence of Nash equilibrium (NE) is guaranteed. A practical resource allocation algorithm based on better response dynamic is proposed. We prove that the proposed algorithm can converge to a feasible NE. Numerical results show that the proposed algorithm can achieve near optimal sum rate performance with low complexity.

Utility-Based Optimal Precoding for SWIPT in MIMO Broadcasting Systems

In this paper, we focus on optimal precoding for simultaneous wireless information and power transfer in multiple-input multiple-output broadcasting systems. A utility function, which is a nonnegative weighted sum of the achievable data rate and the received energy, is designed to effectively allocate the available power. Then, the problem of optimal precoding is formulated as a utility maximization problem, and a closed-form solution is provided. In order to clearly illustrate the rate-utility tradeoff, a two-tier optimization structure is adopted, and the corresponding two-tier optimization algorithm, for which the outer-tier optimization is accomplished by the golden section search method, is also provided. Our results indicate that the maximum information transmission and the energy transfer must be well balanced in order to maximize the total payoff, and the method employed by this paper can obtain the unique precoding matrix for the optimal operating point.

Coordinated Precoding for D2D Communications Underlay Uplink MIMO Cellular Networks

In this paper, we study the coordinated precoding problem for device-to-device (D2D) communications underlay a multiple-input multiple-output (MIMO) cellular network. The system considered here constitutes of multiple D2D user pairs attempting to share the uplink radio resources of a cellular base station (BS). The problem is formulated as a sum-rate maximization (SRM) problem, which subjects to both a interference power constraint imposed to protect the BS and individual transmit power budgets allowed for the D2D user pairs. Because the SRM problem is nonconvex in general, we reformulate it as a difference convex (DC)-type programming problem, which can be solved by employing a successive convex approximation (SCA) method. Moreover, a proximal point-based regularization approach is also pursued to ensure the convergence of the proposed algorithm. Numerical results further show that our algorithm can achieve a near-optimal solution with guaranteed convergence.

Energy-efficient secrecy precoding for general MIMOME wiretap channels

In this paper, we study the energy-efficient secrecy precoding problem for a general multiple-input multiple-output multiple-eavesdropper (MIMOME) wiretap channel. The system model considered here consists of a common transmitter, a legitimate receiver, and multiple eavesdroppers, all mounted with multiple antennas. In order to improve the energy efficiency (EE) performance of the proposed system, an artificial noise (AN)-aided precoding scheme is adopted. Because the formulated EE maximization problem is of huge nonconvex complexity, we solve it by employing a two-tier optimization structure. Although the outer-tier optimization problem is quite simple and can be efficiently solved by the famous golden section searching method, the inner-tier optimization problem turns out to be nonconvex and cannot be easily solved. To solve it, we relax it to a secrecy rate maximization (SRM) problem, which naturally constitutes a difference convex (DC)-type programming problem and can be iteratively solved by employing a successive convex approximation (SCA) method. Numerical simulations are further provided to demonstrate the proposed algorithms. Results show that our algorithms can converge fast to a near-optimal solution.