Superimposed training-based channel estimation for miso optical-OFDM vlc

In this paper, we investigate a novel channel estimation (CE)method for multiple-input and single-output (MISO) systems in visible lightcommunication (VLC). Direct current biased optical orthogonal frequencydivision multiplexing (DCO-OFDM) is commonly used in VLC where halfof the available subcarriers are spent to guarantee a real-valued outputafter the inverse fast Fourier transform operation. Besides, dedicated subcarriers are typically used for CE, thus, many resources are wasted andthe spectral efficiency is degraded. We propose a superimposed trainingapproach for CE in MISO DCO-OFDM VLC scenarios. Analytical expressions of mean squared error (MSE) and spectral efficiency are derived whenthe least squares estimator is considered. This analysis is valid for outdoorand indoor scenarios. For the CE error, simulation results of MSE showa perfect match with analytical expressions. Moreover, results prove thatthis technique guarantees a larger spectral efficiency than previous schemeswhere dedicated pilots were used. Finally, the optimal data power allocationfactor is also analytically derived.This work was supported in part by the National Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) in Ecuador and in part by the Spanish National Project TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE). The work of B. G. Guzmán was supported by the Spanish MECD FPU Fellowship Program

Robust Channel Estimation in Multiuser Downlink 5G Systems Under Channel Uncertainties

In wireless communication, the performance of the network highly relies on the accuracy of channel state information (CSI). On the other hand, the channel statistics are usually unknown, and the measurement information is lost due to the fading phenomenon. Therefore, we propose a channel estimation approach for downlink communication under channel uncertainty. We apply the Tobit Kalman filter (TKF) method to estimate the hidden state vectors of wireless channels. To minimize the maximum estimation error, a robust minimax minimum estimation error (MSE) estimation approach is developed while the QoS requirements of wireless users is taken into account. We then formulate the minimax problem as a non-cooperative game to find an optimal filter and adjust the best behavior for the worst-case channel uncertainty. We also investigate a scenario in which the actual operating point is not exactly known under model uncertainty. Finally, we investigate the existence and characterization of a saddle point as the solution of the game. Theoretical analysis verifies that our work is robust against the uncertainty of the channel statistics and able to track the true values of the channel states. Additionally, simulation results demonstrate the superiority of the model in terms of MSE value over related techniques