Pilot-based Channel Estimation Research Articles

Image Super Resolution-Based Channel Estimation for Orthogonal Chirp Division Multiplexing on Shallow Water Underwater Acoustic Communications.

Orthogonal chirp division multiplexing (OCDM) offers a promising modulation technology for shallow water underwater acoustic (UWA) communication systems due to multipath fading resistance and Doppler resistance. To handle the various channel distortions and interferences, obtaining accurate channel state information is vital for robust and efficient shallow water UWA communication. In recent years, deep learning has attracted widespread attention in the communication field, providing a new way to improve the performance of physical layer communication systems. In this paper, the pilot-based channel estimation is transformed into a matrix completion problem, which is mathematically equivalent to the image super-resolution problem arising in the field of image processing. Simulation results show that the deep learning-based method can improve the channel distortion, outperforming the equalization performed by traditional estimator, the performance of Bit Error Rate is improved by 2.5 dB compared to the MMSE method in OCDM system. At the 7.5 to 20 dB region, it achieves better bit error rate performance than OFDM systems, and the bit error rate is reduced by approximately 53% compared to OFDM when the SNR value is 20, which is very useful in shallow water UWA channels with multipath extension and severe time-varying characteristics.

Pilot based channel estimation improvement in orthogonal frequency-division multiplexing systems using linear predictive coding

Pilot based least square (LS) channel estimation is a commonly used channel estimation technique in orthogonal frequency-division multiplexing based systems due to its simplicity. However, LS estimation does not handle the noise effect and hence suffers from performance degradation. Since the channel coefficients are correlated in time and hence show a slower variation than the noise, it is possible to encode the channel using linear predictive coding (LPC) without the noise. In this work, the channel is estimated from the pilots using LS estimation and in a second step the channel’s LS estimated is encoded as LPC coefficients to produce an improved channel estimation. The estimation technique is simulated for space-time block coding (STBC) based orthogonal frequency-division multiplexing (OFDM) system and the bit error rate (BER) curves show improvement of the LPC estimation over the LS estimation of the channel.

Orthonormal pilot-based channel estimation with low complexity phase shift optimization and coverage enhancement for IRS-assisted B5G communication

Beyond fifth-generation (B5G) technology features millimeter wave (mmWave) to ensure low latency and high capacity while enabling multiple-input and multiple-output (MIMO) facilities. MMWave signals are attenuated over short distances, resulting in a smaller coverage area. The B5G network relies on Intelligent Reflecting Surfaces (IRS) for reducing mmWave attenuation. IRS increases mmWave coverage and reduces obstruction risks by introducing phase shifts on incident beams. Complete channel state information (CSI) is required to estimate phase shifts in IRS and precoding. A novel orthonormal pilot matrix with a low-complexity channel estimation method is proposed in this context. The proposed channel estimation method achieved maximum accuracy with limited pilot sequences. A low-complexity phase shift optimization technique is also presented in this study using estimated channel information. On the other hand, the use of the desired number of reflective elements is highlighted to maximize coverage. On the basis of received signal strength and outage probability, two approaches are presented for determining the number of reflective elements. The derived closed-form expression of outage probability (OP) seems simple. It shows close agreement with the theoretical model. Results show that the proposed model provides better achievable rates than conventional IRS models.

Superimposed Pilot-based Channel Estimation for RIS-assisted IoT Systems Using Lightweight Network

Superimposed Pilot-based Channel Estimation for RIS-assisted IoT Systems Using Lightweight Network

Accurate MMSE Expressions for Short-Packet Pilot-Based Channel Estimation

Accurate MMSE Expressions for Short-Packet Pilot-Based Channel Estimation

Channel reconstruction for mmWave Massive MIMO systems based on Channel Path Map

For the needs of high data rate and massive device access in current fifth-generation (5G) mobile communication networks and future sixth-generation (6G) mobile communication networks, Millimeter-Wave (mmWave) Massive Multiple-Input Multiple-Output (MIMO) is a crucial technical support. However, the conventional pilot based channel estimation methods are exceedingly time-consuming and substantially degrade system performance when the number of users or antennas is high. This paper proposes a Path Clustering based Channel Reconstruction (PCCR) scheme for channel reconstruction in order to solve this issue. By leveraging channel information (such as angle of arrival, angle of departure, and path gain) at a number of geographical locations that are recorded in the Channel Path Map (CPM), we aim to reconstruct the mmWave channel of any locations of interests. To be specific, the CPM is used to extract the neighborhood propagation path data, and an autonomous determination algorithm is used to find the number of path clusters at the location of interest. The recorded propagation path information is processed by the Equivalent Base Station (EBS) scheme, and the path clustering is accomplished by using the AGglomerative NESting (AGENS) algorithm. In each path cluster, Inverse Distance Weighted (IDW) algorithm is developed to forecast the path information at the user location. With all path information at user location are forecast, the channel can be reconstructed. The anticipated overhead is substantially lower than that of conventional methods because the computational cost of the procedure only depends on the total number of paths at the nearby locations. Ray-tracing based simulations are carried out to demonstrate the performance of the proposed algorithm. The results show that, when applied to a CPM with a spatial location precision of 5 m, the Normalized Mean Squared Error (NMSE) of the proposed algorithm is nearly identical to that of Linear Minimum Mean Square Error (LMMSE) estimation. The actual achievable date rate can match the needs of upcoming wireless communications and is superior over the conventional LMMSE.

Deep Learning Peephole LSTM Neural Network-Based Channel State Estimators for OFDM 5G and Beyond Networks

This study uses deep learning (DL) techniques for pilot-based channel estimation in orthogonal frequency division multiplexing (OFDM). Conventional channel estimators in pilot-symbol-aided OFDM systems suffer from performance degradation, especially in low signal-to-noise ratio (SNR) regions, due to noise amplification in the estimation process, intercarrier interference, a lack of primary channel data, and poor performance with few pilots, although they exhibit lower complexity and require implicit knowledge of the channel statistics. A new method for estimating channels using DL with peephole long short-term memory (peephole LSTM) is proposed. The proposed peephole LSTM-based channel state estimator is deployed online after offline training with generated datasets to track channel parameters, which enables robust recovery of transmitted data. A comparison is made between the proposed estimator and conventional LSTM and GRU-based channel state estimators using three different DL optimization techniques. Due to the outstanding learning and generalization properties of the DL-based peephole LSTM model, the suggested estimator significantly outperforms the conventional least square (LS) and minimum mean square error (MMSE) estimators, especially with a few pilots. The suggested estimator can be used without prior information on channel statistics. For this reason, it seems promising that the proposed estimator can be used to estimate the channel states of an OFDM communication system.

Cell-Free Massive MIMO With OTFS Modulation: Statistical CSI-Based Detection

We study the downlink spectral efficiency (SE) of a cell-free massive multiple-input multiple-output (CF-mMIMO) system with orthogonal time frequency space (OTFS) modulation. Each user equipment (UE) uses the minimum mean-squared error-based successive interference cancellation (MMSE-SIC) scheme to detect the received signal. Given the statistical channel information at the UEs, an analytical closed-form expression for the downlink SE is derived. To improve the per-user SE, a max-min fairness resource allocation is proposed and developed. Numerical results show that the proposed resource allocation strategies for superimposed pilot-based (SP-CHE) and embedded pilot-based channel estimation (EP-CHE) achieve up to 43% and 65% performance gain over the baseline system without power allocation.

Pilot based channel estimation of OFDM systems using deep learning techniques

Pilot based channel estimation of OFDM systems using deep learning techniques

Pilot-based channel estimation in spatial modulated OFDM systems for mobile wireless applications

Spatial modulation Orthogonal Frequency Division Multiplexing (SM OFDM) is a newly developed transmission technique that has been proposed as an alternative for multiple input multiple output (MIMO)-OFDM transmission. Channel estimation is important for coherent data detection in practical scenarios. This paper investigates estimate power, Mean Square Error (MSE) and Bit Error Rate (BER) parameter metrics with channel estimation algorithm for Additive White Gaussian Noise (AWGN) and Rayleigh fading channel on three test environments proposed by International Telecommunication Union (ITU) specified standard model. Simulation output shows that for an AWGN channel, an improvement of approximately 1 dB and 0.3 dB power is obtained with Minimum Mean Square Error (MMSE) estimate and Least Square (LS)-linear/Spline using DFT. Additionally, for various interpolations employed, MSE decreases as Signal to Noise Ratio (SNR) rises, and performance improvement in BER when compared to traditional LS/MMSE estimators also occurs. For Rayleigh fading channel also there arises an enhanced performance in the parameter metrics with MMSE-DFT compared to conventional estimators. In summary, simulation output shows that incorporating DFT algorithm can provide better estimate power, BER and MSE by eliminating the effect of noise externally the extreme channel delay length.

Pilot-based channel estimation in spatial modulated OFDM systems for mobile wireless applications

Pilot-based channel estimation in spatial modulated OFDM systems for mobile wireless applications

An improved least squares (LS) channel estimation method based on CNN for OFDM systems

<abstract><p>Least squares (LS) is a commonly used pilot-based channel estimation algorithm in orthogonal frequency division multiplexing (OFDM) systems. This algorithm is simple and easy to implement because of its low computation complexity. However, it has poor performance, especially at low signal-to-noise ratio (SNR). To solve this problem, an improved LS channel estimation method based on convolutional neural network (CNN) is proposed on the basis of analyzing the traditional LS channel estimation methods. A channel estimation compensated network is designed based on CNN, which can solve the problem of performance degradation of the mean square error (MSE) through the online and offline modules. By designing the input-output relations, training data set, and testing data set, a CNN network is iteratively trained to learn the relevant features of the channels, so that the traditional LS estimation value can be corrected to improve the accuracy. Simulation results show that the proposed method can achieve better performance in bit error rate (BER) and MSE, compared with the traditional channel estimation methods.</p></abstract>

3D indoor positioning with spatial modulation for visible light communications

In this paper, a novel three-dimensional (3D) indoor visible light positioning (VLP) algorithm is proposed based on the spatial modulation (SM) and its error performance assessed as compared to the conventional received signal strength (RSS)-based 3D VLP systems. As contrasted to the traditional VLP system, the proposed SM-based 3D VLP system first estimates the optical channel gain between the transmitting light-emitting diodes (LEDs) and the two photo detectors (PDs) attached to the user by a pilot-based channel estimation technique. Then, unknown 3D positions of the receiver are determined by the trilateration algorithm with distances computed from the estimates of the channel gains. Consequently, the 3D VLP system achieves an interference-free transmission with increased spectral efficiency and without the need for a demultiplexing process at the receiving end. The algorithm’s performance is evaluated regarding positioning error by applying the SM over four LEDs and the number of pilots selected as a function of the environmental signal-to-noise ratios (SNRs). The computer simulation results show that the positioning errors are obtained in an order of magnitude smaller than RSS-based techniques in an indoor industrial environment. This is mainly because the distances involved in determining the 3D positions can be determined more precisely by the pilot-aided channel estimation method without creating any data rate problem in transmission due to the higher spectral efficiency of the SM.

Cell-Free Massive MIMO Meets OTFS Modulation

We provide the first-ever performance evaluation of orthogonal time frequency space (OTFS) modulation in cell-free massive multiple-input multiple-output (MIMO) systems. To investigate the trade-off between performance and overhead, we apply embedded pilot-aided and superimposed pilot-based channel estimation methods. We then derive a closed-form expression for the individual user downlink and uplink spectral efficiencies (SEs) as a function of the numbers of APs, users and delay-Doppler domain channel estimate parameters. Based on these analytical results, we also present new scaling laws that the AP’s and user’s transmit power should satisfy, to sustain a desirable quality of service. It is found that when the number of APs, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M_{a}$ </tex-math></inline-formula> , grows without bound, we can reduce the transmit power of each user and AP proportionally to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/M_{a}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/M_{a}^{2}$ </tex-math></inline-formula> , respectively, during the uplink and downlink phases. We compare the OTFS performance with that of orthogonal frequency division multiplexing (OFDM) at high-mobility conditions. Our findings reveal that, OTFS modulation with embedded pilot-based channel estimation provides up to 20-fold gain over the OFDM counterpart in terms of 95%-likely per-user downlink SE. Finally, with superimposed pilot-based channel estimation, the increase in the uplink sum SE is more pronounced when the channel delay spread is increased.

An embedded pilot power based channel estimation and low-complexity feedback equalization scheme for OTFS system

Orthogonal Time Frequency Space modulation (OTFS) has evolved as an astounding modulation technique for high-speed communication in a doubly dispersive channel. In any wireless communication system, channel estimation and equalization are essential at the receiver to recover the transmitted data. To accomplish this for the emerging OTFS based systems, a modified embedded pilot-based channel estimation technique and low complexity feedback equalization algorithm for integer Doppler shifts in the delay-Doppler domain are proposed in this paper. Our channel estimation scheme exploits embedded-pilot arrangement, and the symbol equalization relies on the Interference calculation and its mitigation iteratively. To achieve this we contemplate a prudent arrangement of symbols in the OTFS frame in such a way that the Guard symbols prevent the interference between data symbols and the pilot symbol at the receiver. Two distinct lumps of received data of the same OTFS frame will be engaged in channel estimation and data detection. An analytical expression of the theoretical Cramer Rao Lower Bound (CRLB) is derived and plotted for the proposed channel estimation scheme. The attained simulation results for Bit-Error-Rate (BER) under the proposed scheme show a significant error rate improvement over the Minimum Mean Squared Error (MMSE) equalization algorithm. Further, a lower computational complexity is also achieved in comparison with modified MMSE detection and MP detection algorithms.

Accurate Channel Prediction Based on Transformer: Making Mobility Negligible

Accurate channel prediction is vital to address the channel aging issue in mobile communications with fast time-varying channels. Existing channel prediction schemes are generally based on the sequential signal processing, i.e., the channel in the next frame can only be sequentially predicted. Thus, the accuracy of channel prediction rapidly degrades with the evolution of frame due to the error propagation problem in the sequential operation. To overcome this challenging problem, we propose a transformer-based parallel channel prediction scheme to predict future channels in parallel. Specifically, we first formulate the channel prediction problem as a parallel channel mapping problem, which predicts the channels in next several frames in parallel. Then, inspired by the recently proposed parallel vector mapping model named transformer, a transformer-based parallel channel prediction scheme is proposed to solve this formulated problem. Relying on the attention mechanism in machine learning, the transformer-based scheme naturally enables parallel signal processing to avoid the error propagation problem. The transformer can also adaptively assign more weights and resources to the more relevant historical channels to facilitate accurate prediction for future channels. Moreover, we propose a pilot-to-precoder (P2P) prediction scheme that incorporates the transformer-based parallel channel prediction as well as pilot-based channel estimation and precoding. In this way, the dedicated channel estimation and precoding can be avoided to reduce the signal processing complexity. Finally, simulation results verify that the proposed schemes are able to achieve a negligible sum-rate performance loss for practical 5G systems in mobile scenarios.

Deep Learning Based Channel Estimation for OFDM Systems With Doubly Selective Channel

In wireless communication orthogonal frequency division multiplexing (OFDM) systems with high mobility, channel estimation becomes challenging due to the double selectivity of channels. To solve this problem, a deep learning based method which fully considers the channel features and noise effects is proposed in this letter. In the pilot-based channel estimation scheme, a compression-and-reconstruction channel estimation network (CRCENet) is designed to improve the channel estimation performance, which fuses not only the low-level features of the rapid changes between adjacent subcarriers in the short term and the high-level features of the overall channel changes under large time spans in the backbone, but also the features using the attention mechanism in the up-sampling branches. Meanwhile, considering that the samples with different signal-to-noise ratios (SNRs) have different credibility, loss weights based on credibility are assigned to data with different SNRs during network training, to further improve the estimation performance of the network. The experimental results revealed that CRCENet performed better than the traditional least squares estimation algorithm and state-of-the-art learning networks.

Blind and Semi-Blind Channel Estimation/Equalization for Poisson Channels in Optical Wireless Scattering Communication Systems

The communication systems for Poisson channels require long pilot for channel estimation and may result in large percentage of overhead due to the signal-dependent noise of Poisson-distributed signal, i.e., both signal part and noise part experience random processes. In this paper, both blind and semi-blind channel estimation methods are studied to shorten the overhead and increase the transmission efficiency for Poisson channels. First, fractionally spaced equalizers are proposed based on modified constant modulus algorithm (CMA) and subspace (SS). Second, a data-aided iterative channel estimation (ICE) is designed and analyzed in terms of its asymptotic unbiasedness and convergence. The proposed methods are evaluated based on both a constant channel scenario and a varying channel scenario. Numerical simulation results show that the modified CMA has the worst bit-error rate performance but requires the lowest computational complexity. Besides, both SS based channel estimation and ICE have negligible overhead and comparable bit-error rate performances with respect to the conventional periodic pilot based channel estimation having 50% overhead.

Deep Learning for Joint Pilot Design and Channel Estimation in MIMO-OFDM Systems.

In MIMO-OFDM systems, pilot design and estimation algorithm jointly determine the reliability and effectiveness of pilot-based channel estimation methods. In order to improve the channel estimation accuracy with less pilot overhead, a deep learning scheme for joint pilot design and channel estimation is proposed. This new hybrid network structure is named CAGAN, which is composed of a concrete autoencoder (concrete AE) and a conditional generative adversarial network (cGAN). We first use concrete AE to find and select the most informative position in the time-frequency grid to achieve pilot optimization design and then input the optimized pilots to cGAN to complete channel estimation. Simulation experiments show that the CAGAN scheme outperforms the traditional LS and MMSE estimation methods with fewer pilots, and has good robustness to environmental noise.

Analysis of Variable Bit Rate Spread FBMC-Based Integrated Satellite–Terrestrial Broadcast System

For the joint impact of carrier frequency offset (CFO) and phase noise (PHN), the exact closed-form signal-to-interference-noise ratio (SINR) expressions are derived for novel Walsh Hadamard (WH)-spread filterbank multicarrier (SFBMC) transmission scheme in integrated satellite–terrestrial broadcast system (ISTBS). For this evaluation, variable bit rates (VBR)s for WH-SFBMC are considered for transmission in 3-state Fontan land mobile satellite suburban environment. Further, symbol error rate (SER) and spectral efficiency are analyzed based on the obtained SINR expressions. Such analyses are conducted to support VBR quality of service (QoS) in orthogonal frequency division multiplexing (OFDM)-based digital video broadcasting standards utilizing ISTBS. Furthermore, pilot-based channel estimation error variance is evaluated for VBR WH-SFBMC. Later, block-by-block system computational complexity of VBR WH-SFBMC, perfect reconstruction (PR)-filterbank multicarrier (FBMC), and OFDM schemes are evaluated. It is shown that, VBR WH-SFBMC is rate-dependent and relatively more complex system than single rate PR-FBMC and OFDM. However, the SER performance of WH-SFBMC is illustrated to outperform PR-FBMC and OFDM significantly even for higher bit rates in presence of CFO and PHN. Moreover, simulation and analytic results are in close approximation with each other. Thus, a trade-off exists between achieved error performance and system complexity.