Reference Signal Structure Research Articles

On Efficient DCT Type-I Based Low Complexity Channel Estimation for Uplink NB-IoT Systems

Channel estimation is a challenging and timely issue for the 3rd Generation Partnership Project (3GPP) standardized low power wide-area network technology named narrowband Internet of Things (NB-IoT). Channel estimation is crucial to achieve extended radio coverage, energy efficiency, coherent detection, and channel equalization for signal repetition dominated NB-IoT uplink transmission. The NB-IoT inherits simplified baseband radio frequency processing, physical channels, reference signal structure, and numerology from existing Long Term Evolution (LTE) systems to save power and costs. Thus, channel estimation methods extensively employed in LTE systems may not be applied to the NB-IoT uplink systems. In this paper, efficient discrete cosine transform type-I (DCT-I)-based transform-domain channel estimation approaches are proposed by modifying the original definition of DCT-I. The proposed methods can mitigate the problems experienced in the discrete Fourier transform (DFT)-based channel estimation, such as signal aliasing error, and border effect. The proposed approaches improve channel estimation precision by reducing signal distortion from the high-frequency region in the time-domain when non-sample-spaced path delays exist in multipath fading channels. Signal aliasing error experienced from the virtual subcarriers is also minimized with the anticipated schemes. The proposed methods are applied on simple least squares (LS) estimates in time-domain to eliminate estimation noise. The viability of the proposed estimators is verified as compared to the conventional LS, DFT-based de-noising LS, and standard DCT-I based methods through extensive numerical simulations. Based on the numerical simulations, the proposed estimators show better mean square error and bit error rate performances than their competitors in extremely low coverage conditions.

Application of Adaptive Neuro Fuzzy Logic Method of Noised Electrical Signals for Correcti

In this article, the correction of the distorted electrical signal with an adaptive neural fuzzy logic (ANFIS) due to noise forming in is discussed. An electrical signal, such as a sinus signal, is widely used both in load supply and in modulation techniques for control and information transport. While the deterioration in the sinus structure causes energy losses, it also causes damage to the control and information transmission signals. Therefore, after considering the structure of the clear reference signal equation, a noise that may occur in the sinus structure is included in the clear reference signal structure. An interference signal must be formed with the unknown nonlinear process from another noise source for measuring of the information signal because of an interference signal needs. After an interference signal is generated, the measuring signal is given in the sum of the pure reference signal and the interference. For the correction phase of the signals, at inputs of the adaptive fuzzy logic system in the Matlab toolbox, the source signal with noise and the measuring signal values are entered and the experiment is performed via a 3-membership function. When the results are observed, it is seen that the signal which is distorted after the correction operations is very close to the reference signal

ON USING PERIODIC REFERENCE SIGNAL IN HOMODYNE ACOUSTO-OPTIC SPECTRUM ANALYZER

Introduction. For a homodyne acousto-optic spectrum analyzer functioning a reference optical channel must be organized. The signal in this channel should provide uniform reference illumination throughout the spatial frequency range. In the general case, the spectrum analyzer functioning can be considered with a continuous photosensor and photosensor with charge accumulation. With the last one, the signal in the reference channel is proposed to be a wideband pulses periodic sequence. Objective. Analyze the spectrum analyzer functioning with a periodic reference signal. Materials and methods. We derive the mathematical expression to describe the influence of the reference signal structure on the analyzer’s output signal for the cases of continuous photosensor and photosensor with charge accumulation. Results. It is shown that in the case of continuous photosensor, the reference signal periodicity does not lead to characteristics degradation. However, in the case of many frequency resolution points it is impractical, since each photodetector signal is parallel, processing is required: filtering, amplification and digitization. In the case of using of the charge accumulation sensor, the discrete frequency grid appears, which means signals omissions in frequency. This can be avoided by choosing the accumulation time equal to the minimum among the values of the acousto-optic modulator time aperture and the reference signal period, which is hard to implement, or still leads to the signal omissions in frequency or time. Conclusion. To perform a real-time mode in the homodyne acousto-optic spectrum analyzer, the reference signal must be either non-periodic, which raises the question of its synthesis, or a continuous photodiode array should be used.

Two-Dimensional AoD and AoA Acquisition for Wideband Millimeter-Wave Systems With Dual-Polarized MIMO

In this paper, a novel two-dimensional super-resolution angle-of-departure (AoD) and angle-of-arrival (AoA) estimation technique is proposed for wideband millimeter-wave multiple-input multiple-output systems with dual-polarized antenna elements. The key ingredient of the proposed method is the custom designed beam pairs, from which there exists an invertible function of the AoD/AoA. A new multi-layer reference signal structure is developed for the proposed method to facilitate angle estimation for wideband channels with dual-polarized antenna elements. To reduce feedback in closed-loop frequency division duplexing systems, a novel differential feedback strategy is proposed to feedback the estimated angle pairs. Numerical results demonstrate that good azimuth/elevation AoD and AoA estimation performance can be achieved under different levels of signal-to-noise ratio, channel conditions, and antenna array configurations.

Support Vector Machine Regression and Artificial Neural Network for Channel Estimation of LTE Downlink in High-Mobility Environments

In this paper we apply and assess the performance of support vector machine regression (SVR) and artificial neural network (ANN) channel estimation algorithms to the reference signal structure standardized for LTE Downlink system. SVR and ANN where applied to estimate real channel environment such as vehicular A channel defined by the International Telecommunications Union (ITU) in the presence of nonlinear impulsive noise.The proposed algorithms use the information provided by the received reference symbols to estimate the total frequency response of the time variant multipath fading channel in two phases. In the first phase, each method learns to adapt to the channel variations, and in the second phase it predicts all the channel frequency responses. Finally, in order to evaluate the capabilities of the designed channel estimators, we provide performance of SVR and ANN, which is compared with traditional Least Squares (LS) and Decision Feedback (DF). The simulation results show that SVRhas a better accuracy than other estimation techniques.

Improvement of LTE System Performances by Using a New Pilot Structure

In this paper, a new pilot structure for LTE downlink system in SISO and MIMO cases is introduced in order to improve the channel estimation performances. The downlink reference signal structure in LTE is important for channel estimation. Pre-defined resource elements in the time and frequency domain are carrying the cell-specific reference signal sequence. However, those standard pilot scattering contains more empty resource element as the number of transmit or receiver antennas increases, leading to a decrease of the system performances. Simulation results shows that the proposed LTE pilot structure allows a high performance in terms of QoS and throughput for both SISO and MIMO cases.