Estimation and detection techniques for doubly-selective channels in wireless communications

A fundamental problem in communications is the estimation of the channel. The signal transmitted through a communications channel undergoes distortions so that it is often received in an unrecognizable form at the receiver. The receiver must expend significant signal processing effort in order to be able to decode the transmit signal from this received signal. This signal processing requires knowledge of how the channel distorts the transmit signal, i.e. channel knowledge. To maintain a reliable link, the channel must be estimated and tracked by the receiver. The estimation of the channel at the receiver often proceeds by transmission of a signal called the 'pilot' which is known a priori to the receiver. The receiver forms its estimate of the transmitted signal based on how this known signal is distorted by the channel, i.e. it estimates the channel from the received signal and the pilot. This design of the pilot is a function of the modulation, the type of training and the channel. [Continues.

Partial-Data Superimposed Training with Data Precoding for OFDM Systems

Superimposed training (ST) is a recently addressed technique used for channel estimation where known training sequences are arithmetically added to data symbols, avoiding the use of dedicated pilot subcarriers, and thus, increasing the available bandwidth compared with traditional pilot symbol assisted modulation schemes. However, the system handles data interference over channel estimation as a result of the ST process; also, data detection is degraded by pilot interference. Recent ST methods have analyzed the data interference and presented schemes that deal with it. We propose a novel superimposed model over a precoded data scheme, named partial-data superimposed training (PDST), where an interference control factor assigns the adequate information level to be added to the training sequence in orthogonal frequency division multiplexing systems. Also, a data detection method is introduced to improve the symbol error rate performance. Moreover, a capacity analysis of the system has been derived. Finally, simulation results confirm that performance of PDST is superior to previous proposals

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