Proposed Channel Estimation Method Research Articles

Network-assisted full-duplex cell-free mmWave networks: Hybrid MIMO processing and multi-agent DRL-based power allocation

This paper investigates the network-assisted full-duplex (NAFD) cell-free millimeter-wave (mmWave) networks, where the distribution of the transmitting access points (T-APs) and receiving access points (R-APs) across distinct geographical locations mitigates cross-link interference, facilitating the attainment of a truly flexible duplex mode. To curtail deployment expenses and power consumption for mmWave band operations, each AP incorporates a hybrid digital–analog structure encompassing precoder/combiner functions. However, this incorporation introduces processing intricacies within channel estimation and precoding/combining design. In this paper, we first present a hybrid multiple-input multiple-output (MIMO) processing framework and derive explicit expressions for both uplink and downlink achievable rates. Then we formulate a power allocation problem to maximize the weighted bidirectional sum rates. To tackle this non-convex problem, we develop a collaborative multi-agent deep reinforcement learning (MADRL) algorithm called multi-agent twin delayed deep deterministic policy gradient (MATD3) for NAFD cell-free mmWave networks. Specifically, given the tightly coupled nature of both uplink and downlink power coefficients in NAFD cell-free mmWave networks, the MATD3 algorithm resolves such coupled conflicts through an interactive learning process between agents and the environment. Finally, the simulation results validate the effectiveness of the proposed channel estimation methods within our hybrid MIMO processing paradigm, and demonstrate that our MATD3 algorithm outperforms both multi-agent deep deterministic policy gradient (MADDPG) and conventional power allocation strategies.

Time-Frequency Domain Channel Estimation for OTFS Systems

Orthogonal-time-frequency-space (OTFS) has been developed as a promising modulation scheme for high Doppler channels. In OTFS systems, channel estimation (CE) plays a key role. Most existing CE schemes put pilots in delay-Doppler (DD)-domain, often resulting in low spectrum efficiency and high peak-to-average-power-ratio (PAPR). In this paper, we propose three schemes to solve the problems. Proposed methods have distinct features being able to estimate fractional delays and fractional Dopplers. Different from existing methods, our approach places pilots in time-frequency (TF)-domain. The first scheme overlaps pilots and data in both TF- and DD-domain, while Scheme II overlaps them in TF- but not in DD-domain. To further enhance the performance, the third scheme makes pilots and data non-overlapped in both TF- and DD-domain. To conduct CE, time-domain pilot signals are extracted with a filtering operation and a two-dimensional frequency estimation problem is formulated consequently. Delays are first estimated with root-MUSIC. Then, Dopplers are estimated using a simple line-fitting method. Finally, channel gains are estimated by a least-squares method. To solve the interference between pilots and data, successive-interference-cancellation (SIC) is also applied. With the estimated channel, a minimum-mean-square-error (MMSE) equalizer is used for the compensation of channel effect. Simulation results show that the bit-error-rate (BER) performance with the channel estimated by proposed CE methods can approach to that with known channel. Also, spectrum efficiency can be improved by at most 25% and PAPR can be reduced by at least 19 dB.

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.

Enhancing channel estimation accuracy in polar-coded MIMO–OFDM systems via CNN with 5G channel models

In this paper, a novel channel estimation technique for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO–OFDM) systems in fifth generation (5G) networks is proposed, which leverages the power of convolutional neural networks (CNNs) along with the application of polar coding. By employing polar encoding at the transmitter and polar decoding at the receiver, the proposed system enhances error correction performance, reliability, and data transmission rate in 5G networks. A novel optimized CNN-based channel estimation technique, named CNN-CENet, has been integrated into the polar-coded MIMO–OFDM receiver. This integration aims to address the interference and noise challenges in 5G systems. The proposed channel estimation method utilizes multiple layers of CNN to accurately characterize the properties of the real channel by incorporating least squares (LS) estimations. Hence, the proposed CNN-CENet provides an accurate estimation of the channel coefficients, enabling efficient signal detection at the receiver. The MATLAB simulation has been conducted using deep learning and 5G toolboxes to obtain results for various channel parameters. These results are then compared with the LS and minimum mean square error (MMSE) methods for performance evaluation. The proposed model surpasses the performance of the LS and MMSE methods, achieving a significant 95.6% reduction in mean square error (MSE) compared to LS and a substantial 59.7% reduction compared to MMSE. Additionally, it delivers notable bit error rate (BER) reductions of 59.8% and 53.2% for LS and MMSE, respectively, in low mobility scenarios, and 57.4% and 48.5% reductions in high mobility scenarios. This improvement leads to enhanced reliability and overall system efficiency.

A Practical Channel Estimation Strategy for XL-MIMO Communication Systems

Extremely large-scale massive MIMO (XL-MIMO) has been emerged as an innovative technique for future 6G communication. Channel state information (CSI) acquisition is a crucial but challenging task in XL-MIMO wireless systems due to the larger number of base station (BS) antennas. In this letter, we investigate a practical strategy to realize efficient channel estimation for XL-MIMO communication systems with the consideration of hybrid-field channel model. Different from the existing work requiring the specific proportion of the number of far-field and near-field channel paths as a prior knowledge, which is not feasible in practical communications, our method aims to provide a criterion to determine the value of proportion first and then acquire the corresponding estimated channel components. Simulation results show the effectiveness and superiority of our proposed channel estimation method as compared to the benchmarks.

Demodulation Reference Signal (DM-RS) based channel estimation for non-terrestrial networks to support high mobility

This paper provides an adaptive 5G-NR based solution for non-terrestrial networks (NTN) to support the high mobility of the user equipment (UE). A front-loaded demodulation reference signal (DM-RS) symbol pattern with additional orthogonal symbols is proposed to support UE mobility 500 km/h. We develop a 5G-NR link-level simulator (LLS) to analyze the performance of physical downlink shared channel (PDSCH) under the proposed front-loaded DM-RS symbol pattern, tapped delay line (TDL) channel model, and precoding-based beamforming/MIMO schemes. The simulation results show that the proposed channel estimation method based on front-loaded DM-RS helps to improve the link reliability and achieves the maximum spectral efficiency up to 5.05 bps/Hz under 64-QAM, which can also contribute to the evolution of NTN in 6G networks to support the transonic speed mobility of UE.

Spectral Efficiency Analysis of Cell-Free Distributed Massive MIMO Systems With Imperfect Covariance Matrix

In this paper, the impacts of imperfect channel covariance matrix on the spectral efficiency (SE) of cell-free distributed massive multiple-input multiple-output (MIMO) systems are analyzed. We propose to estimate the channel covariance matrix by alternately using the assigned pilots and their phase-shifted pilots in different coherent blocks, which improves the accuracy of channel estimation with imperfect covariance matrix and reduces pilot overhead. Under this scheme, the closed-form expressions of SE with maximum ratio combination (MRC) and zero-forcing (ZF) receivers are derived, which enables us to select key parameters for better system performance. Simulation results verify the effectiveness of the proposed channel estimation method and the accuracy of the derived closed-form expressions. When more coherent blocks are used to estimate the covariance matrix, we can get better system performance. Moreover, some insightful conclusions are arrived at from the SE comparisons between different receiving schemes (ZF and MRC) and different pilot allocation schemes (orthogonal pilot and pilot reuse).

A Low Complexity Learning-Based Channel Estimation for OFDM Systems With Online Training

In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.

Group Successive Interference Cancellation Assisted Semi-Blind Channel Estimation in Multi-Cell Massive MIMO-NOMA Systems

Both non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (MIMO) are the promising techniques to satisfy the foreseeable massive connectivity requirements. In this letter, a novel semi-blind channel estimation method is proposed for multi-cell massive MIMO-NOMA systems, in which a group successive interference cancellation (GSIC) assisted method is developed to mitigate pilot contamination. Specifically, the users in each cell are divided into multiple groups and the same pilot sets are reused by the different groups in order to reduce pilot overhead. Besides, by following the NOMA principle, a GSIC assisted method is proposed to eliminate inter-group pilot contamination. In simulations, it is shown that the proposed channel estimation method is capable of reducing the overlapping probability of different groups as the number of receiving antennas increases. In addition, the proposed scheme outperforms the conventional channel estimation methods in terms of normalized mean square error (NMSE) performance.

Downlink Channel Tracking for Intelligent Reflecting Surface-Aided FDD MIMO Systems

Intelligent reflecting surfaces (IRSs) can improve the wireless link between the transmitter and receiver by configuring the propagation environments of the system. In order to achieve intelligence of the configuration, the channel state information is required. In this paper, we study the downlink channel estimation problem in an IRS-aided frequency division duplexing (FDD) multiple-input multiple-output (MIMO) system. We propose a two-phase training scheme such that the direct channel (non-IRS channel) and the cascaded channel (IRS-aided channel) can be tracked by two Kalman filters separately. The pilots transmitted by the base station (BS) and the reflective coefficients at the IRS are then jointly optimized to improve the channel estimation accuracy. Since the formulated problem is non-convex, the optimal solution is not straightforward. A lower bound of the objective function is derived to serve as the baseline for evaluating the performance of a certain pilot and reflective coefficients design. In two special cases, namely the direct channel dominant scenario and the cascaded channel dominant scenario, we find that the optimal pilots and the optimal reflective coefficient matrix can be approximated by some columns of the discrete Fourier transform (DFT) matrices as the number of antennas at the BS and the number of reflective elements at the IRS get larger. We further propose a codebook-based low-complexity design for a general scenario which can significantly reduce the control overhead of the IRS and cut the computational burden. Numerical results are provided to verify the efficiency of the proposed channel estimation method.

Communication and Localization With Extremely Large Lens Antenna Array

Achieving high-rate communication with accurate localization and wireless environment sensing has emerged as an important trend of beyond-fifth and sixth generation cellular systems. Extension of the antenna array to an extremely large scale is a potential technology for achieving such goals. However, the super massive operating antennas significantly increases the computational complexity of the system. Motivated by the inherent advantages of lens antenna arrays in reducing system complexity, we consider communication and localization problems with an ex tremely large lens antenna array, which we call “ExLens”. Since radiative near-field property emerges in the setting, we derive the closed-form array response of the lens antenna array with spherical wave, which includes the array response obtained on the basis of uniform plane wave as a special case. Our derivation result reveals a window effect for energy focusing property of ExLens, which indicates that ExLens has great potential in position sensing and multi-user communication. We also propose an effective method for location and channel parameters estimation, which is able to achieve the localization performance close to the Cramer-Rao lower bound. Finally, we examine the multi-user communication performance of ExLens that serves coexisting near-field and far-field users. Numerical results demonstrate the effectiveness of the proposed channel estimation method and show that ExLens with a minimum mean square error receiver achieves significant spectral efficiency gains and complexity-and-cost reductions compared with a uniform linear array.

Application of Maximum Entropy Theorem in Channel Estimation

Scholars have been highly concerned with the blind channel estimation of multi-carrier modulation transmission systems, and the decoding process of block codes is usually closely related to channel parameters. Therefore, the accurate estimation of channel parameters is an important method to ensure communication accuracy. According to the transmission characteristics of multi-carrier modulated transmission channels, this paper uses the maximum likelihood theorem and the maximum entropy theorem to put forward a blind channel estimation method based on the proposed received signals. In order to verify the effect of this method, this research utilizes the BP decoding algorithm, which is used in LDPC coding, to calculate the symbol error rate and analyze the estimated effect of the transmission system. The simulation results show that the proposed method can estimate channel parameters effectively, and that the symbol error rate of LDPC decoding is almost similar to that of non-blind channel estimation. This implies that the proposed channel estimation method can effectively improve both channel utilization and transmission efficiency.

MIMO–OFDM communication systems for traffic data transmission in 5G drone small cells

A compressed sensing-based channel estimation (CE) method is proposed for doubly selective channels whilst multiple-input multiple-output (MIMO)–orthogonal frequency-division multiplexing (OFDM) system is employed. According to the simulation results, the proposed CE method, which is dubbed as the MIMO-linearising scheme performs better than the basic expansion model (BEM), which is a state-of-the-art CE method and applies frequency domain scattered pilots. Furthermore, MIMO-linearising can be utilised as an accurate procedure for estimating only the positions of non-zero channel taps in the scenarios where those positions change with a slower rate than amplitudes and phases of the channel taps. Based on that priori estimated positions (PEPs), modified versions of the linearising scheme and the BEM methods, called MIMO-linearising-PEP and BEM-PEP, respectively, are described, showing improvement on performance and spectral efficiency. In addition, by considering the physical characteristic of the vehicular channels, an alternative version of the MIMO-linearising-PEP, called MIMO-linearising-efficient, is described to reduce the computational complexity and increase the spectral efficiency further. Simulation results indicate that the proposed CE methods can measure the channel more accurately and enhance the car detection accuracy in the frames which have been used as the inputs of the communication systems.

BEM-based iterative EKF channel estimation using superimposed pilot for OFDM system in high-speed railway

In high-speed mobile scenarios, to resist the time–frequency domain selective fading (double-selective fading) and non-stationary characteristics and solve the problem of low spectrum utilization caused by channel estimation, this paper concentrates on how to eliminate the inter carrier interference (ICI) effectively and decrease the interference of data symbols at the superimposed symbols. So this paper proposes an iterative extend Kalman filter (iEKF) channel estimation method based on basis expansion model (BEM) for orthogonal frequency division multiplexing (OFDM) system. The simulation results show that the proposed channel estimation method has better estimation accuracy, robustness and higher throughput under low signal to noise ratio (SNR) condition in high-speed mobile scenes compared with traditional estimation methods.

A Novel Noise Suppression Channel Estimation Method Based on Adaptive Weighted Averaging for OFDM Systems

Orthogonal frequency division multiplexing (OFDM) systems have inherent symmetric properties, such as coding and decoding, constellation mapping and demapping, inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT) operations corresponding to multi-carrier modulation and demodulation, and channel estimation is a necessary module to resist channel fading in the OFDM system. However, the noise in the channel will significantly affect the accuracy of channel estimation, which further affects the recovery quality of the final received signals. Therefore, this paper proposes an efficient noise suppression channel estimation method for OFDM systems based on adaptive weighted averaging. The basic idea of the proposed method is averaging the last few channel coefficients obtained from coarse estimation to suppress the noise effect, while the average frame number is adaptively adjusted by combining Doppler spread and signal-to-noise ratio (SNR) information. Meanwhile, to better combat the negative effect brought by Doppler spread and inter-carrier interference (ICI), the proposed method introduces a weighting factor to correct the weighted value of each frame in the averaging process. Simulation results show that the proposed channel estimation method is effective and provides better performance compared with other conventional channel estimation methods.

Study on communication channel estimation by improved SOMP based on distributed compressed sensing

Wireless communication channel usually show the feature of time-varying; however, the time-varying channel has the characteristic that the channel structure within the adjacent time slots having serious time correlation. Therefore, how to use the time slow-changing characteristics of the channel to design the suitable channel state information acquisition method is of great significance to further improve communication performance with low communication bit error rate (BER) for OFDM communication system. The distributed compressed sensing (DCS) is proposed for the phenomenon that multiple sparse signals with time correlation. Based on the DCS theory framework, this article will re-build a time-domain channel estimation method with joint structure by improving the synchronous orthogonal matching pursuit (simultaneous orthogonal matching pursuit, SOMP) algorithm to get better channel information acquisition performance. Simulation results demonstrate the effectiveness of the proposed channel estimation method. Compared with the conventional compressed sensing-based channel estimations which perform at each time separately, the method proposed has better performance in terms of BER.

Low-Complexity Multiuser Receiver for Massive Hybrid Array mmWave Communications

In this paper, we study the low complexity reception of multiuser signals in uplink millimeter wave (mmWave) communications using a partially connected hybrid antenna array. Exploiting the mmWave channel property, we propose a low-complexity user-directed multiuser receiver with three novel schemes for allocating subarrays to users. This receiver only requires the knowledge of angles-of-arrival (AoAs) for dominating paths and a small amount of equivalent channel information instead of perfect channel state information. For comparison, we also derive a successive interference cancellation-based solution as a performance benchmark. We design two types of reference signals with the channel estimation method to enable efficient and simple estimation for AoA and equivalent baseband channel. Also, we provide analytical results for the performance of the AoA estimation, using the lower bounds of mean square errors in line-of-sight dominated mmWave channels. The simulation results validate that the proposed channel estimation method is effective when employed in combination with a zero-forcing equalizer.

Iterative channel estimation and pilot design rules for high‐mobility comb‐pilot OFDM system

SummaryWith wireless communications in high‐mobility environment becoming popular, this poses a big challenge for communication systems based on the comb‐pilot OFDM, such as IEEE 802.11p, since it has not the enough pilots to estimate the time‐ and frequency‐selective channel accurately. In this paper, several comb‐pilot schemes and three comb‐pilot design rules are proposed to meet the Nyquist criterion for sampling the vehicle‐to‐vehicle (V2V) channel and the requirements of second‐order statistic of V2V channel. Based on the proposed pilot schemes, an iterative channel estimation method from the CE‐BEM model is proposed, together with three ICI cancellation methods. After thorough simulation, the effectiveness of the comb‐pilot design rules, the proposed channel estimation method, and intercarrier interference (ICI) cancellation methods is verified. Compared with other channel estimation methods, the proposed method performs better. The simulation results also reveal that the channel order L+1 has a great impact on the performance of the comb‐pilot OFDM system.

Fast Channel Estimation and Beam Tracking for Millimeter Wave Vehicular Communications

Millimeter wave (mmWave) has been claimed to be the only viable solution for high-bandwidth vehicular communications. However, frequent channel estimation and beamforming required to provide a satisfactory quality of service limits mmWave for vehicular communications. In this paper, we propose a novel channel estimation and beam tracking framework for mmWave communications in a vehicular network setting. For channel estimation, we propose an algorithm termed robust adaptive multi-feedback (RAF) that achieves comparable estimation performance as existing channel estimation algorithms, with a significantly smaller number of feedback bits. We derive upper and lower bounds on the probability of estimation error (PEE) of the RAF algorithm, given a number of channel estimations, whose accuracy is verified through Monte Carlo simulations. For beam tracking, we propose a new practical model for mmWave vehicular communications. In contrast to the prior works, the model is based on position, velocity, and channel coefficient, which allows a significant improvement of the tracking performance. Focused on the new beam tracking model, we re-derive the equations for Jacobian matrices, reducing the complexity for vehicular communications. An extensive number of simulations is conducted to show the superiority of our proposed channel estimation method and beam tracking algorithm in comparison with the existing algorithms and models. Our simulations suggest that the RAF algorithm can achieve the desired PEE, while on average, reducing the feedback overhead by 75.5% and the total channel estimation time by 14%. The beam tracking algorithm is also shown to significantly improve beam tracking performance, allowing more room for data transmission.

Compressed channel estimation methods for high mobility doubly selective channels in orthogonal frequency division multiplexing systems

In this study, four channel-estimation (CE) methods are proposed for sparse high mobility doubly selective channels. These methods are designed to take advantage of the sparsity of the channel to decrease the training sequence size and the computational complexity of the CE. Two of the new methods use time domain training sequences. In the first method, the pilot is designed such that the time domain channel can be estimated tap-by-tap using an autoregressive procedure. In the second method, a truncated Taylor expansion of the channel phase is applied to linearise the equations for CE and a pseudorandom training sequence is utilised to solve those equations. The last two proposed channel estimators employ frequency domain pilots and are built upon a previously proposed estimator that exploits both the delay and Doppler shift sparsity of the channel. The computational complexity and the spectral efficiency of the methods are compared. In addition, simulations are performed to compare the performance of these estimators in a sparse channel. Simulation results indicate up to 30 dB improvement in the bit error rate calculation for our proposed CE methods compared to the conventional CE methods.