Low-complexity Channel Estimation Method Research Articles

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.

A Low-Complexity OTFS Channel Estimation Method for Fractional Delay-Doppler Scenarios

We propose a low-complexity channel estimation method for Orthogonal Time Frequency Space (OTFS) modulation based systems. Two important aspects of OTFS modulation, namely, (i) fine delay-Doppler (DD) resolution which results in path separation and, (ii) the separability of the effective DD domain channel gain expression into delay domain and Doppler domain terms, are used to propose an iterative channel estimation method which separately estimates the delay, Doppler shift and the channel gain for each channel path. The proposed estimation does not require matrix inversion and therefore has lower complexity when compared to other known methods. The proposed method is shown to achieve a normalized mean square estimation error (NMSE) performance better than other methods.

Low-Complexity Channel Estimation for Intelligent Reflecting Surface-Enhanced Massive MIMO

Intelligent reflecting surface (IRS) consisting of massive passive elements is a promising technology to save the hardware cost and energy consumption, but with a challenging channel estimation of IRS transmission links. In this letter, a low-complexity channel estimation method is first proposed for IRS-enhanced multi-user massive multiple-input and multiple-output systems, which can attain the separate channel state information (CSI) of the cascaded link. The acquisition of the separate CSI contributes to the flexibility of channel estimations for the multi-user case with user mobility. The proposed channel estimation consists of direction-of-arrival and path gain estimations, which are implemented by using the limited radio frequency chains and pilots, respectively. Besides, the computational complexity is analyzed for the proposed and existing methods for comparison. Numerical results show that the proposed method can achieve accurate channel estimation with a low complexity.

Channel Estimation for Reconfigurable Intelligent Surface-Assisted Wireless Communications Considering Doppler Effect

In wireless systems aided by reconfigurable intelligent surfaces (RISs), channel state information plays a pivotal role in achieving the performance gain of RISs. Mobility renders accurate channel estimation (CE) more challenging due to the Doppler effect. In this letter, we propose two practical wideband CE schemes incorporating Doppler shift adjustment (DSA) for multi-path and single-path propagation environments, respectively, for RIS-assisted communication with passive reflecting elements. For the multi-path scenario, ordinary CE is first executed assuming quasi-static channels, followed by DSA realized via joint RIS reflection pattern selection and transformations between frequency and time domains. The proposed CE necessitates only one more symbol incurring negligible extra overhead compared with the number of symbols required for the original CE. For the single-path case which is especially applicable to millimeter-wave and terahertz systems, a novel low-complexity CE method is devised capitalizing on the form of the array factors at the RIS. Simulation results demonstrate that the proposed algorithms yield high CE accuracy and achievable rate with low complexity, and outperform representative benchmark schemes.

Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

DFT-Based Low-Complexity Channel Estimation Method for Millimeter-Wave MIMO Systems

In this paper, a low-complexity channel estimation method is proposed for single-user millimeter-wave MIMO systems, which is applicable to both uniform linear array and uniform planar array structures. Through applying the discrete Fourier transform (DFT) basis to jointly represent the channel matrix and design the pilot beams such that the product of the pilot beam matrix with the inverse DFT is a unitary matrix, the received pilot signal can be approximately expressed as a scaled version of channel representation coefficients. Thus, the channel matrix can be estimated directly by two times of matrix multiplication. More specifically, multiply the received pilot signal with DFT matrix on the left and inverse DFT in the right. Analytical and simulation results show that the proposed method has lower computational complexity and better estimation accuracy than the least square and compressed channel sensing using orthogonal matching pursuit.

Channel Estimation for Uniform Rectangular Array Based Massive MIMO Systems With Low Complexity

In this paper, a low complexity channel estimation method is proposed for two-dimensional (2-D) massive MIMO systems with uniform rectangular arrays of antennas. Instead of assuming independent fading between different transmit–receive antenna pairs, an array-structure-based physical channel model that characterizes the realistic scattering environments via the angles and gains associated with different propagation paths is studied. A 2-D Fourier-transform (FT) based on elevation and azimuth steering factors is applied to derive the spatial spectrum of received signals in angular domain at base station . Based on the spectrum analysis, the spatial spectrum of the received data is used to estimate the channel parameters, including direction of arrival (incident azimuth and elevation angles) and the path amplitude of each resolvable path. Since the channel coefficients are estimated in spatial domain and the proposed 2-D FT can be realized by fast-Fourier-transform , the computational complexity is reduced significantly. Simulation results are provided to verify the accuracy and the complexity of the proposed scheme.

Continuous and Burst Pilot Sequences for Channel Estimation in FBMC-OQAM

Filter bank multicarrier offset quadrature amplitude modulation (FBMC) has advantages over the well-known orthogonal frequency division multiplexing due to its improved time-frequency efficiency. However, the self-interference of FBMC is an important issue in order to perform the channel estimation. To face this interference in FBMC, several pilot-based schemes have been proposed, but with a high complexity. We propose a novel approach, simple yet effective, based on the use of continuous pilot sequences (CPS) in FBMC. Our scheme considers an adequately designed pilot sequence, taking into account the self interference, combined with a low-complexity channel estimation method that has a better performance in terms of mean squared error (MSE) and symbol error rate than previous methods proposed in the literature. Additionally, we also introduce burst pilot sequences, which have the same benefits of CPS while they reduce the number of required pilots and, thus, increase the available resources for data transmission. Moreover, we derive an analytical model to theoretically characterize the MSE of our proposed schemes. Simulation results have confirmed the validity of these theoretical expressions since their values match perfectly.

Low Complexity Estimation of Frequency Selective Channels for the LTE-A Uplink

3GPP LTE-A uplink channel estimation is based on pilot symbols. For the support of MIMO transmissions, a special reference symbol structure was standardized to perform MIMO channel estimation without excessive overhead. We propose a low complexity channel estimation method in the frequency domain based on correlation that exploits the orthogonal structure of the reference symbols to mitigate inter-layer interference. Further, we introduce a smoothing method to deal with the interference that leads to improved performance at low signal to noise ratio.

Low‐complexity large‐scale multiple‐input multiple‐output channel estimation using affine combination of sparse least mean square filters

Large-scale multiple-input multiple-output (MIMO) system is considered one of promising technologies to realise next-generation wireless communication system (5G). So far, channel estimation problem is a big obstacle to develop large-scale MIMO system design due to high computational complexity and curse of dimensionality, which are caused by the long delay spread as well as a large number of antennas. Hence, devising any low-complexity channel estimation method could promote the successful development of the large-scale MIMO system. Due to the fact that, large-scale MIMO channels often exhibit sparse or/and cluster-sparse structure, in this study, the authors propose an effective low-complexity large-scale MIMO channel estimation method by using affine combination of sparse adaptive filtering filters. First, problem formulation and standard affine combination of adaptive least mean square (LMS) filters are introduced. Then they propose an effective affine combination method with two sparse LMS filters and design an approximate optimum affine combiner according to stochastic gradient search method. Later, to verify the proposed algorithm for large-scale MIMO channel estimation, both theoretical analysis and numerical simulations are provided to confirm effectiveness of the proposed algorithm which can achieve better estimation performance than the traditional methods.

Low Complexity Channel Estimation for MC-CDMA System Based Chaos under Fast Multipath Channel

Based on interpolation methods, a low complexity channel estimation method for MC-CDMA system based chaos under fast fading multipath channel is proposed.Different interpolation methods are used; Linear interpolation (LI), cubic spline interpolation (CSI), and piecewise cubic Hermite interpolation (PCHI). The multi-carrier scheme is established by using a chaotic generator with different initial conditions, while the pilots are established using another chaotic generator with another different initial conditions. The proposed channel estimator without and with interpolation methods for single user and multiusers is tested under fast frequency selective Rayleigh fading channel. Simulation results demonstrate that the channel estimation of MC-CDMA system based chaos using interpolation methods and when the pilots are chaos signal too, achieves better performance improvement in fast multipath environment compared to the conventional MC-CDMA system when the pilots are considered as Walsh- Hadamard codes. The proposed system is also built and tested using Simulink program.

Low-Complexity Channel Estimation For The UHF MIMO-RFID Systems With Optimal Training

The issue of channel estimation is investigated for the UHF MIMO-RFID systems over pinhole channel. A training sequences aided linear minimum mean square error (LMMSE) channel estimation is proposed for the whole pinhole channel at the reader. Based on the mean square error (MSE) criterion, the MSE bound and the optimal training scheme with this bound are derived. By exploiting the optimal training scheme, an optimal low-rank LMMSE channel estimator is proposed to reduce the computational complexity via singular value decomposition (SVD). Furthermore, the Chu sequence is employed as the optimal training sequence to implement with easy realization and reduce computational complexity at the reader. Simulation results demonstated the effectiveness of the proposed low-complexity channel estimation method and the superiority of the derived optimal training scheme for the MIMO-RFID systems.

Low-Complexity Channel Estimation Using Short-Point FFT/IFFT for an Iterative Receiver in an LDPC-Coded OFDM System

A frequency-domain channel expanding technique (FD-CET) is proposed for channel estimation in an iterative receiver of a low-density parity-check coded orthogonal frequency division multiplexing system. The FD-CET can expand a subset of the frequency-domain channel gains to all of them in a short-point FFT/IFFT format. This technique is applied to estimate the channel gains utilizing the pilots and information from the decoder in an iterative manner. It is a novel low complexity channel estimation method. Numerical results demonstrate that the proposed channel estimator approaches a maximum-likelihood performance within only a few iterations under multi-path channels.

Low complexity channel estimation method for TDS-OFDM based Chinese DTTB system

The Chinese digital television terrestrial broadcasting (DTTB) standard uses pseudo-noise (PN) sequence as guard interval (GI) and the training sequence. This PN sequence is used to do channel estimation and synchronization in the system. The existing channel estimation methods often have to adopt iterative scheme when the length of channel impulse response (CIR) is long, which will increase the receiver complexity dramatically. In this paper, a channel estimation scheme for time domain synchronous OFDM (TDS-OFDM) systems is proposed. Interference free CIR information can be obtained by constructing special correlation windows, rather than removing interference from the time domain sequences through iteration. Moreover, the estimation can be achieved by using correlation process. Comparing to conventional schemes, the proposed algorithm greatly reduces the computational complexity. Simulation results show that the proposed method can meet the requirement of the Chinese DTTB systems. And the scheme is also suitable for other communication OFDM systems with time domain sequence headers.

A Low-Complexity LMMSE Channel Estimation Method for OFDM-Based Cooperative Diversity Systems with Multiple Amplify-and-Forward Relays

Orthogonal frequency division multiplexing- (OFDM-) based amplify-and-forward (AF) cooperative communication is an effective way for single-antenna systems to exploit the spatial diversity gains in frequency-selective fading channels, but the receiver usually requires the knowledge of the channel state information to recover the transmitted signals. In this paper, a training-sequences-aided linear minimum mean square error (LMMSE) channel estimation method is proposed for OFDM-based cooperative diversity systems with multiple AF relays over frequency-selective fading channels. The mean square error (MSE) bound on the proposed method is derived and the optimal training scheme with respect to this bound is also given. By exploiting the optimal training scheme, an optimal low-rank LMMSE channel estimator is introduced to reduce the computational complexity of the proposed method via singular value decomposition. Furthermore, the Chu sequence is employed as the training sequence to implement the optimal training scheme with easy realization at the source terminal and reduced computational complexity at the relay terminals. The performance of the proposed low-complexity channel estimation method and the superiority of the derived optimal training scheme are verified through simulation results.