Jiamin Li

On the performance of iterative receivers in massive MIMO systems with pilot contamination

In this paper, we study iterative detection algorithms of the multi-cell multi-user massive MIMO systems with pilot contamination. First, a iterative algorithm applying soft decision interference cancellation is introduced. It is based on minimum mean square error (MMSE) filter and derived from [1] where it was applied in block transmission. Then, by considering the algorithm complexity, two iterative soft input soft output (SISO) detection algorithms, which avoid matrix inversion, are presented: a soft interference cancellation algorithm for the polynomial expansion linear detector (PELD) and a SISO detector based on singular value decomposition (SVD). Moreover, matched filter-SISO (MF-SISO) is given as a special case of PELD-SISO. Simulation comparisons of iterative detection algorithms in massive MIMO systems are presented, which are of great significance for the future research.

An Improved Constrained Least Squares Localization Algorithm in NLOS Propagating Environment

The non-line-of-sight (NLOS) error is a major error source in wireless localization. Therefore, an improved constrained least-squares (CLS) algorithm is put forward to tackle this issue, where the positioning problem is formulated as a mathematical programming problem. And then, the cost function of the optimization is studied and a new one is proposed. Finally, through the presented optimization, we try to minimize the positioning influence of NLOS errors. Moreover, the studied method does not depend on a particular distribution of the NLOS error. Simulation results show that the positioning accuracy is significantly improved over traditional CLS algorithms, even under highly NLOS conditions.

Improved RSA Localization Based on the Lagrange Multiplier Optimization

The non-line-of-sight (NLOS) error in wireless network is the main factor that affects the accuracy of positioning algorithm. Therefore, this paper proposes an improved range-scaling-algorithm (RSA) using the Lagrange multiplier method in the wireless sensor networks, where we account for two kinds of nodes, i.e., the static nodes (SN) and the mobile nodes (MN). The key of the proposed algorithm is to construct a composite cost function by the Lagrange multiplier method. Meanwhile, the SN grouping operation followed by a positioning combination is proposed to further improve the performance. Simulation results show that the proposed algorithm can effectively suppress the loss of positioning accuracy caused by non-line-of-sight error. Moreover, the proposed algorithm performs better with increasing number of SNs.

A Non-line-of-Sight Localization Method Based on the Algorithm Residual Error Minimization

Wireless localization has become a key technology location based services, and the non-line-of-sight (NLOS) propagation is one of the most important error source in the localization. Therefore, this paper defines a novel algorithm residual error (ARE) in NOLS environment, and estimates the position of mobile station (MS) by minimizing this ARE, where the quadratic programming is employed to solve the minimization problem. The simulation results show that the proposed algorithm produces significant performance improvements in NLOS environments.

Two-Phase Prototype Filter Design for FBMC Systems

FBMC has been taken as a candidate waveform for the next enhanced 5th generation (5G). To further improve its advantages over OFDM as well as to promote its application in burst transmission, a two-phase method is applied to design its prototype filter, i.e. a square-root Nyquist filter. In this method, the autocorrelation-based technique and a spectral factorization aimed at minimum stopband energy are successively exploited to acquire the final prototype filter. Through the relaxation of Nyquist condition and benefited from the nonlinear-phase, the frequency selectivity of our designed filters can be greatly improved. Furthermore, the performances of the proposed prototype filter brings a better BER in simulations, which demonstrates the effectiveness of our square-root Nyquist filter design for FBMC systems.

Uplink performance of MMSE receivers in multi-cell MU-MIMO systems

This paper investigates the uplink performance of multiple-input multiple-output (MIMO) wireless systems employing linear minimum mean-squared error (MMSE) receivers. We focus on multi-cell MU-MIMO systems with pilot contamination over i.i.d Rayleigh fading channels. The MMSE estimator is adopted to get the CSI and we build the equivalent model of estimated channel for MU-MIMO system with pilot contamination. Then we give the relationship between the achievable sum rate with MMSE receivers and the ergodic mutual information achieved with optimal receivers. We further study the achievable ergodic sum rate of MMSE receiver and get the closed-form approximations of the lower bound with the relationship we achieved and existing results on the ergodic mutual information for optimal receivers.