Channel Estimation in Carrier Aggregation Systems by Leveraging Sensing and Channel Correlation

Achieving accurate channel estimation and adaptive communications with moving transceivers is challenging due to rapid changes in the underwater acoustic channels. We achieve an accurate channel estimation of fast time-varying underwater acoustic channels by using the superimposed training scheme with a powerful channel estimation algorithm and turbo equalization, where the training sequence and the symbol sequence are linearly superimposed. To realize this, we develop a ‘global’ channel estimation algorithm based on Gaussian likelihood, where the channel correlation between (among) the segments is fully exploited by using the product of the Gaussian probability-density functions of the segments, thereby realizing an ideal channel estimation of each segment. Moreover, the Gaussian-likelihood-based channel estimation is embedded in turbo equalization, where the information exchange between the equalizer and the decoder is carried out in an iterative manner to achieve an accurate channel estimation of each segment. In addition, an adaptive communication algorithm based on constellation aggregation is proposed to resist the severe fast time-varying multipath interference and environmental noise, where the encoding rate is automatically determined for reliable underwater acoustic communications according to the constellation aggregation degree of equalization results. Field experiments with moving transceivers (the communication distance was approximately 5.5 km) were carried out in the Yellow Sea in 2021, and the experimental results verify the effectiveness of the two proposed algorithms.

Channel estimation can be classified into pilot‐assisted channel estimation, blind channel estimation, and decision DDCE. This chapter focuses on pilot‐assisted channel estimation in OFDM systems. There are two main design issues for channel estimation in OFDM systems. The first one is pilot design and the second one is channel estimator design with both low complexity and accuracy. An interpolation technique should be performed to obtain a channel estimation value at each data subcarrier. Channel estimation for MIMO‐OFDM systems is more complicated than single antenna systems but the basic principle is similar to the single antenna systems. The purpose of equalizers is to find the inverse function of the channel response and eliminate an Inter‐Symbol Interference (ISI) caused by the dispersive nature of the channel. The channel estimator and the equalizer are designed according to the system requirements such as pilot assignment, antenna array, system complexity, latency, and power.

The number of transmit and receive antennas in multi-input multi-output (MIMO) systems is increasing rapidly to enhance the throughput and reliability of next-generation wireless systems. Benefits of large size MIMO systems, however, can be realized only when the quality of estimated channels is ensured at the transmitter and receiver side alike. In this paper, we introduce a new decision-directed channel estimation technique dealing with pilot shortage in the MIMO-OFDM systems. The proposed channel estimator uses soft symbol decisions obtained by iterative detection and decoding (IDD) scheme to enhance the quality of channel estimate. Using the soft information from the decoders, the proposed channel estimator selects reliable data tones, subtracts interstream interferences, and performs re-estimation of the channels. We analyze the optimal data tone selection criterion, which accounts for the reliability of symbol decisions and correlation of channels between the data tones and pilot tones. From numerical simulations, we show that the proposed channel estimator achieves considerable improvement in system performance over the conventional channel estimators in realistic MIMO-OFDM scenarios.

This paper develops a low-complexity channel estimation (CE) scheme based on compressive sensing (CS) for time-domain synchronous (TDS) orthogonal frequency-division multiplexing (OFDM) to overcome the performance loss under doubly selective fading channels. Specifically, an overlap-add method of the time-domain training sequence is first proposed to obtain the coarse estimates of the channel length, path delays and path gains of the wireless channel, by exploiting the channel's temporal correlation to improve the robustness of the coarse CE under the severe fading channel with long delay spread. We then propose the priori-information aided (PA) iterative hard threshold (IHT) algorithm, which utilizes the priori information of the acquired coarse estimate for the wireless channel and therefore is capable of obtaining an accurate channel estimate of the doubly selective fading channel. Compared with the classical IHT algorithm whose convergence requires the $l_2$ norm of the measurement matrix being less than 1, the proposed PA-IHT algorithm exploits the priori information acquired to remove such a limitation as well as to reduce the number of required iterations. Compared with the existing CS based CE method for TDS-OFDM, the proposed PA-IHT algorithm significantly reduces the computational complexity of CE as well as enhances the CE accuracy. Simulation results demonstrate that, without sacrificing spectral efficiency and changing the current TDS-OFDM signal structure, the proposed scheme performs better than the existing CE schemes for TDS-OFDM in various scenarios, especially under severely doubly selective fading channels.

Millimeter-wave (mmWave) massive antenna array has shown great potential in gaining high-accuracy localization for user equipments (UEs). Yet, mmWave signals suffer from non-line-of-sight (NLOS) propagation and fast time-scale fading in practice, which will affect UE localization performance. It is non-trivial to reveal the impact of multipath propagation and fast time-scale fading on mmWave-based localization, and gain guidelines for harnessing NLOS propagation. In this paper, we aim to provide a unified framework for performance analysis of mmWave-based UE localization and channel estimation. Firstly, closed-form Cramer-Rao lower bounds for UE localization and channel estimation, respectively, are derived to shed lights on their performance limits. Secondly, the effect of system elements (e.g., channel correlation and fast time-scale fading) on UE localization performance is quantitatively analyzed. In addition, information contribution from NLOS channel is quantified to reveal the impact of multipath propagation on UE localization. Finally, we show that the NLOS propagation-caused localization error can be alleviated by using a NLOS model folding-based localization scheme <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which strikes a balance between computational complexity and localization performance.

Channel estimation is one of the most important aspects of wireless communication. Especially in Sparse Code Multiple Access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time-frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things(IoT) when time-frequency resources is restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in SCMA system based on complex-valued sparse autoencoder which is effective to learn features of wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In our work, the encoder part is used to realize pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates performance of the SCMA receiver in our work is very close to that of the perfect channel state information (CSI).

In this paper parametric tracking of channel and synchronization errors for an adaptive multi-dimensional channel estimator for uplink Orthogonal Frequency Division Multiplex Access (OFDMA) communication systems is proposed. The estimator consists of a two-dimensional LMMSE filter. In real world scenarios, channel estimation should work not only in a wide range of terminal velocities and delay spread but it is also confronted with time and frequency synchronization errors affecting the channel estimation and thus limits the performance of both the coherent receiver and overall OFDMA system. We jointly track channel statistics and synchronization errors so that LMMSE filter coefficients closely reflect the current statistics of the channel. Through simulations it is verified that the proposed channel estimator outperforms the robust LMMSE estimator in the presence of synchronization errors and varying channel conditions.

Multicarrier code division multiple access (MC-CDMA) is a promising technique for high data rate and high capacity (large number of users) transmission. However, rapidly time varying channels degrade the performance due to loosing the orthogonality among the subcarriers as well as code patterns. Channel estimation is very important to design adaptive system for improvement in all round performance. This paper proposes an efficient channel estimation for Rayleigh fading channel in high capacity carrier interferometry /multi-carrier code division multiple access (CI/MC-CDMA) system with variable data rate using Genetic algorithms (GAs). The system implementation is done by allocating all sub-carriers to the users who transmit high data rate and alternate odd and even subcarriers are assigned to the other sets of users transmitting at low data rate. GAs is used to estimate a near optimal solution of the set of parameter values on frequency selective Rayleigh fading channel. Objective function is formed from the weighted average of channel capacity and bit error rate. Simulation results show that both channel capacity and bit error probability are improved with the increase of number of iterations/generations.

Underwater acoustic channels typically exhibit clustered-sparse structure, and sparse channel recovery based on the clustered-sparse structure can achieve smaller structural error. We propose a novel clustered-sparse Bayesian model for sparse channel estimation in underwater acoustic (UWA) orthogonal frequency-division multiplexing (OFDM) communication systems. In this model, the sparsity and temporal correlation of channel impulse responses (CIRs) are controlled by a set of hyperparameters. For continuously transmitted OFDM blocks in one frame, the auto-regressive (AR) process is used to characterize temporal correlation of time-varying UWA channels, and different clusters are considered to have different AR model coefficients, as they are usually generated according to different transmission routes. A joint cluster partition and sparse Bayesian learning algorithm (JCP-SBL) is proposed to learn the clustered-sparse structure of UWA channels when the cluster partition is unknown. Results of numerical simulation and lake trial demonstrate the effectiveness of the JCP-SBL channel estimator in time-varying UWA channel, which achieve better channel estimation performance and lower bit error rate (BER) compared with the orthogonal matching pursuit (OMP), simultaneous OMP (SOMP) and temporal multiple sparse Bayesian learning (TMSBL).

This paper derives a MIMO system model that considers the spatial correlation and sparse nature of MIMO UWA channels. Accordingly, we proposes a improved sparse Bayesian learning (I-SBL) algorithm for joint estimating the channel coefficients, residual noise and channel covariance power via the robust expectation maximum (EM) update algorithm. By employing the a priori noise power and the second-order channel covariance matrix acquired from the training sequence, the proposed I-SBL channel estimator achieves faster convergence and easier hyperparameter control than that of the conventional Bayesian algorithm while enjoying better estimation accuracy than the classic channel estimation algorithms. Experimental results have verified that the proposed scheme outperforms the minimum mean square error (MMSE), conventional Bayesian, orthogonal matching pursuit (OMP) and adaptive improved least mean squares (IPNLMS) channel estimators in terms of the channel sparseness control and BER performance.

Multiple transmit and receive antennas (MIMO) have been used in OFDM systems for capacity improvement. In practice, channel state information has to be estimated for diversity combining or space-time decoding. Previous work on channel estimation assumes that MIMO channels are independent and identically distributed (i.i.d.). In certain propagation environments, there exists spatial correlation among channels corresponding to different pairs of transmit and receive antennas. The spatial correlations can he exploited to improve channel estimation. In this paper, we develop a minimum mean-square-error (MMSE) channel estimator for MIMO-OFDM systems that can make full use of the spatial correlation. We also design optimum training sequences that minimize the channel estimation error. When MIMO channels are i.i.d., the training sequences for different transmit antennas are orthogonal and with equal power. However, when MIMO channels are spatially correlated, the power allocation for training sequences can be further optimized. Our simulation results show that the proposed MMSE estimator can exploit spatial and frequency correlations of MIMO channels in OFDM systems and therefore has good performance.

Cloud radio access networks (C-RANs) have drawn considerable interests due to the significant improvements of spectral and energy efficiencies. Since most signal processing functions are moved to the centralized baseband unit (BBU), remote radio heads (RRHs) in C-RANs can be regarded as soft relays to transfer the received signals. The centralization characteristics in C-RANs make traditional channel estimation and training design approaches inefficient, and the requirements of perfect channel state information (CSI) would not be satisfied in turn. To solve this problem, a segment training based individual channel estimation scheme and the corresponding training design are proposed for C-RANs in this paper. Particularly, the channel estimator in terms of the sequential minimum mean-square-error (MMSE) is developed through a prior knowledge of long-term channel correlation statistics and previous channel estimates. The optimal training design for the developed estimator is derived by minimizing the estimation mean-square-error (MSE). Further, the optimal training design for the channel estimation of radio access links is computed by applying the eigenvalue decomposition (EVD). Numerical results show that performance gains of the proposed channel estimation and training design schemes are significant.

A low-complexity two-stage receiver for space–time block-coded CDMA MIMO systems

Channel correlation is usually tracked for adaptive channel estimation, prediction and network optimization. Time correlation is determined by the relative movement of the transmitter and the receiver, while frequency and spatial correlation depend on the environment layout that can be collected and stored for future use. In this paper, we introduce the concept of channel correlation map stored at the base station (BS), which adds both frequency and space direction channel correlation parameters as overlay information to a geographic map. We propose a measurement method of channel correlation maps by processing uplink transmission data and show the influence of available bandwidth on the measurement accuracy. Two application cases with the correlation map are shown through numerical simulation. One is adaptive MIMO-OFDM channel estimation without online tracking correlation parameters, the other is channel prediction for transmit scheduling and beamforming design to reduce the performance degradation due to outdated channel state information (CSI) caused by the feedback delay.

With the growing need for Internet of things (IoT) applications, wireless communications are facing the challenge of providing massive connectivity. However, as the number of users increases, the capacity of massive MIMO systems will be severely affected due to the huge channel estimation overhead. Therefore it is necessary to introduce non-orthogonal pilots to massive MIMO systems in order to ease the burden on the resources for channel estimation. In this paper, we propose a KKT-basediterativenon-orthogonalpilotdesignalgorithmwhich maximizes the mutual information between the received signal and the channel for MMSE channel estimation. For the typical antenna deployment of uniform linear array (ULA), we also provide scalable estimation schemes with low complexity based onchannelangularrepresentationasalternatives.Thesimulation results show that our schemes signi&#64257;cantly save the estimation overhead at the cost of slight decrease in the MSE performance of the channel estimation, and the saving is even more prominent when the system accommodates more users.