A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems. MIDE is a revision of the alternating direction method of multipliers (ADMM)-based algorithm, in which a self-updating method is designed with the damping factor estimated and updated at each iteration based on the Euclidean distance between the iterative solutions of the IDE-based algorithm in order to accelerate the algorithm’s convergence. Compared to the existing ADMM-based detection algorithm, the overall computational complexity of the proposed MIDE algorithm is reduced from O N t 3 + O N r N t 2 to O N t 2 + O N r N t in terms of the number of complex-valued multiplications, where Ntand Nr are the number of users and the number of receiving antennas at the base station (BS), respectively. Simulation results show that the proposed MIDE algorithm performs better in terms of the bit error rate (BER) than some recently-proposed approximation algorithms in MIMO detection of uplink massive MIMO systems.

The Education-Job Mismatch Determinants Among Youth of Kyrgyzstan

This paper aims to examine determinants of education-job mismatch among youth in Kyrgyzstan. Analysis are based on the data School-to-Work Transition Survey (STWT) for 2013. Education-job mismatch is measured by the subjective and objective approach. Subjective approach is based on subjective self-reporting of youth whether their education match to their current employment requirements. Objective approach uses field of work according to the ISCO classification and then the required level of education for a certain position is compared to highest level of education. Determinants of education-job mismatch are identified according to objective measures. Bivariate discrete estimation technique is applied.