A Low-Complexity Signal Detection Utilizing AOR Iterative Method for Massive MIMO Systems

Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error(MMSE) signal detection using the accelerated overrelaxation(AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.

Bi-extrapolated subgradient projection algorithm for solving multiple-sets split feasibility problem

This paper deals with a bi-extrapolated subgradient projection algorithm by intro- ducing two extrapolated factors in the iterative step to solve the multiple-sets split feasibility problem. The strategy is intend to improve the convergence. And its convergence is proved un- der some suitable conditions. Numerical results illustrate that the bi-extrapolated subgradient projection algorithm converges more quickly than the existing algorithms.

An accelerated augmented Lagrangian method for linearly constrained convex programming with the rate of convergence O(1/k^2)

In this paper, we propose and analyze an accelerated augmented Lagrangian method（denoted by AALM） for solving the linearly constrained convex programming. We show that the convergence rate of AALM is O（1/k^2） while the convergence rate of the classical augmented Lagrangian method（ALM） is O1 k. Numerical experiments on the linearly constrained 1-2minimization problem are presented to demonstrate the effectiveness of AALM.

Accelerated Landweber iteration based control allocation for fault tolerant control of reusable launch vehicle

This paper presents a novel Fault Tolerant Control(FTC)scheme based on accelerated Landweber iteration and redistribution mechanism for a horizontal takeoff horizontal landing reusable launch vehicle(RLV).First,an adaptive law based on fixed-time non-singular fast terminal sliding mode control(NFTSMC),which focuses on the attitude tracking controller design for RLV in the presence of model couplings,parameter uncertainties and external disturbances,is proposed to produce virtual control command.On this basis,a novel Control Allocation(CA)based on accelerated Landwber iteration is presented to realize proportional allocation of virtual control command among the actuators according to the effective gain as well as the distance from the current position of actuator to corresponding saturation limit.Meanwhile a novel redistribution mechanism is introduced to redistribute oversaturated command among healthy actuators(non-faulty or redundant).The proposed method can be applied to a real-time FTC system so that the controller reconfiguring is not required in case of actuator faults.Finally,the effectiveness of the proposed method is demonstrated by numerical simulations.