Iterative Channel Estimation Method Research Articles

DFT-Spread Orthogonal Time Frequency Space System With Superimposed Pilots for Terahertz Integrated Sensing and Communication

Terahertz (THz) integrated sensing and communication (ISAC) is a promising interdisciplinary technology that realizes simultaneously transmitting Terabit-per-second (Tbps) and millimeter-level accurate environment or human activity sensing. However, both communication performance and sensing accuracy are influenced by the Doppler effects, which are especially severe in the THz band. Moreover, peak-to-average power ratio (PAPR) degrades the THz power amplifier (PA) efficiency. In this paper, a discrete Fourier transform spread orthogonal time frequency space (DFT-s-OTFS) system with superimposed pilots is proposed to improve the robustness to Doppler effects and reduce PAPR for THz ISAC. Then, a two-phase sensing parameter estimation algorithm is developed to integrate sensing functionality into the DFT-s-OTFS waveform. Meanwhile, a low-complexity iterative channel estimation and data detection method with a conjugate gradient based equalizer is proposed to recover the data symbols of DFT-s-OTFS. The proposed DFT-s-OTFS waveform can improve the PA efficiency by 10% on average compared to OTFS. Simulation results demonstrate that the proposed two-phase sensing estimation algorithm for THz DFT-s-OTFS systems is able to realize millimeter-level range estimation accuracy and decimeter-per-second-level velocity estimation accuracy. Moreover, the effectiveness of the iterative method for data detection aided by superimposed pilots in DFT-s-OTFS systems is validated by the simulations and the bit error rate performance is not degraded by the Doppler effects.

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.

CSI-Based NOMA for Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is a key enabler of beyond fifth-generation networks and a multitude of futuristic applications. However, the coexistence, scheduling and spectrum scarcity issues due to additional sensing signals are valid concerns. A communicating device requiring sensing can use its communication signal for sensing. However, a non-communicating device requiring sensing will need to transmit additional sensing signals, increasing traffic in the network. Thus, in this letter, a novel ISAC with iterative channel estimation (ISAC-ICE) method is proposed to provide spectral efficiency while maintaining sensing and communication performance. The proposed method attains this by enabling the multi-channel estimation from received non-orthogonal communication and sensing signals for uplink transmission. The simulation results and complexity analysis demonstrate that the proposed method enables non-orthogonal multiple accessing (NOMA) of ISAC with a linearithmic complexity.

Pattern search and compressed sensing based phase noise and channel estimation for mmWave massive MIMO-OFDM systems

Phase noise (PN) can seriously affect the performance of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, especially when orthogonal frequency division multiplexing (OFDM) is employed. This paper studies the PN effect on mmWave massive MIMO-OFDM systems, and proposes a time variant training mode to save training time. In addition, an iterative channel and PN estimation method is proposed, which can estimate the PN with pattern search and estimate the channel with compressed sensing, and can converge according to the residual of the received signal. Moreover, the error vector magnitude and the computational complexity are analyzed. Finally, our simulation results demonstrate that by employing the proposed method, the performance of the mmWave massive MIMO-OFDM system can be improved.

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.

Superimposed Pilot Optimization Design and Channel Estimation for Multiuser Massive MIMO Systems

For massive multiuser multiple input multiple output (MIMO) (MU-MIMO) systems, pilot contamination (PC) and data interference (DI) are two main factors that affect the accuracy of the superimposed pilot (SP) based channel estimation, provided that the channel coherence time is less than the total number of users. In this paper, to tackle the effect of the PC, we propose a block-diagonal Grassmannian line packing (GLP) approach, in which the specific sequences for different cells are first designed based on the GLP, and then are block-diagonally extended to form the SP matrices for the users in different cells. To also alleviate the effect of DI on the channel estimation, with the designed SP matrices, an iterative channel estimation (ICE) method based on Tikhonov regularization is presented. The impact that the proposed ICE method has on the signal-to-interference-plus-noise ratio with matched filtering receiver is theoretically analyzed. Moreover, the optimal power allocation factor to the SP by maximizing the spectral efficiency (SE) of the target cell is also derived. Numerical results show that the proposed ICE method with the designed SP matrices effectively mitigates the effects of the PC and DI, and hence, improves the accuracy of channel estimation and further the SE of the massive MU-MIMO systems.

An Iterative Channel Estimation Method for Multi-Cell Massive MIMO Systems

For a massive multiple-input multiple-output system in the multi-cell scenarios, it generally suffers from pilot contamination due to the fact that the adjacent cells may use the same pilot sequences. In order to reduce the impact of pilot contamination on the system performance, a novel iterative channel estimation method is proposed. Specifically, in the proposed method, besides the pilot sequences, the data symbols decoded with high reliability are selected to enhance the channel estimation. Furthermore, the selection criteria are derived to determine which data symbols are reliably decoded. The simulation results show that the proposed method can effectively improve the channel estimation accuracy and the error performance of the considered systems.

Iterative Channel Estimation Method to Enhance PER in V2V Communications Based on IEEE 802.11p

본 논문에서는 WAVE(Wireless Access for Vehicular Environments) 기반 차량 간 통신 시스템에서 패킷 에러율(PER, Packet Error Rate) 성능 향상을 위한 새로운 채널 추정 기법을 제안한다. 제안 기법은 1D-MMSE (Minimum Mean Square Error) 기반의 반복적 채널 추정 기법으로서 기존의 채널 추정 기법을 사용하여 초기 채널을 추정한 후 디코더에서 최종 복호화된 비트 단위의 신호를 재사용하여 채널 추정의 파일럿 신호로 활용하는 방식이다. 기존 대비 복잡도는 높아지지만 패킷 에러율을 비약적으로 향상시킬 수 있다. 제안 기법의 성능 검증을 위해 Cohda Wireless 사에서 제시한 채널 모델 환경에서 모의실험 결과 제안 기법의 패킷 에러율 성능이 기존 기법 대비 향상되며 채널 변화가 극심한 환경에서도 1%의 패킷 에러율 성능을 만족시킬 수 있다.

Iterative channel estimation method for long‐term evolution downlink transmission

Long-term evolution (LTE) standard employs orthogonal frequency division multiplexing (OFDM) for downlink (DL) data transmission. As pilot subcarriers are non-uniformly and sparsely spaced in the OFDM DL transmission, more complicated channel estimation methods may have to be adopted in the mobile devices to ensure adequate performance. An iterative approach is proposed for the channel estimation task, in which an inverse fast Fourier transform and fast Fourier transform pair is iteratively used to smooth channel estimates provided by decision feedback. The performance of this approach is compared with two two-dimensional interpolation methods and one discrete Fourier transform-based estimation method that have been proposed for the LTE DL transmission. Simulation results show that the performance of the proposed method surpasses all the other methods except at high signal-to-noise ratio, and moreover, not a single estimation method consistently performs better than the proposed method for all the channel models considered. Thus, the proposed method is the best among the considered methods for LTE DL transmission.

Approach to suppress out‐of‐band emission for dual pseudo noise padded time‐domain synchronous‐orthogonal frequency division multiplexing systems

The dual pseudo noise padded (DPNP) time-domain synchronous-orthogonal frequency division multiplexing (TDS-OFDM) which utilises the second pseudo noise (PN) sequence for channel estimation is able to reduce the complexity and improve the accuracy of channel estimation compared to the classical TDS-OFDM. However, because of the duplicate PN sequence, DPNP TDS-OFDM will suffer from severer out-of-band emission, which is a headache issue for the conventional TDS-OFDM. In this study, a novel out-of-band suppression approach based on a new PN design criterion as well as a windowing operation is proposed for the DPNP TDS-OFDM systems. The frame structure and system model are also modified to accommodate to the approach, while the overlap-and-add approach is adopted to guarantee the spectral efficiency. Both simulation and experimental results show that this approach could achieve satisfactory performance with much less complexity and no spectral efficiency loss compared to the classical DPNP TDS-OFDM system. In addition, an iterative channel estimation method is also proposed for the modified frame structure to combat against the long channel delay spread.

Advanced parallel combined Gaussian mixture model based feature compensation integrated with iterative channel estimation

This study proposes an effective feature compensation scheme to address severely adverse environments for speech recognition where background noise and channel distortion are simultaneously involved. In the proposed scheme, an iterative channel estimation method is integrated into the framework of our previously proposed Parallel Combined Gaussian Mixture Model (PCGMM) based feature compensation algorithm. A new speech corpus is developed which reflects both additive and convolutional noise corruption. The channel distortion effects are obtained from the NTIMIT and CTIMIT corpora. Evaluation based on objective measures including STNR, PESQ, and speech recognition shows that generated speech corpus includes highly challenging acoustic conditions for speech recognition. The proposed feature compensation method is evaluated over the developed speech corpus. The experimental results demonstrate that the proposed feature compensation scheme is effective at improving speech recognition performance in the presence of both background noise and channel distortion, employing the iterative channel estimation method. The proposed PCGMM-based feature compensation scheme employing the channel estimation method shows +3.58% and +11.61% relative improvements in averaged WER compared to the ETSI AFE algorithm for the developed speech corpora including NTIMIT and CTIMIT channel effects respectively. For real-life application, a voice activity detection technique is employed to estimate the noise model for PCGMM-based method without a priori knowledge of the non-speech locations of input speech. The proposed method is also evaluated on the CU-Move corpus which represents actual in-vehicle conditions, showing a +12.99% relative improvement compared to the ETSI AFE. This study confirms that the proposed PCGMM-based feature compensation method integrated with channel estimation is effective at increasing speech recognition accuracy in real-life severely adverse conditions.

Sparsity-Aware Adaptive Channel Estimation Based on SNR Detection

Time domain synchronous orthogonal frequency division multiplexing OFDM (TDS-OFDM) has a higher spectral efficiency and faster synchronization than standard cyclic prefix OFDM, but suffers from the performance loss over multipath fading channels due to severe interblock-interference. Recently, compressive sensing (CS) is explored to solve those problems, but it heavily relies on the sparsity level of the channel, which is unknown in practice. In this paper, a signal-to-noise ratio-based sparsity estimation method is proposed to detect the sparsity level of the channel. Then we propose the priori aided subspace pursuit algorithm if the channel sparsity meets the theoretical CS model, otherwise we use an improved iterative channel estimation method with the help of the channel sparsity. Simulation results demonstrate that the proposed scheme could improve the robustness and performance of TDS-OFDM systems compared with the conventional iterative methods or recently proposed CS based methods.

A New Iterative Doppler-Assisted Channel Estimation Joint With Parallel ICI Cancellation for High-Mobility MIMO-OFDM Systems

In this paper, we propose an iterative channel estimation and intercarrier interference (ICI) cancellation method for highly mobile users in long-term evolution (LTE) systems. The proposed scheme estimates the wireless channel by using pilot symbols, estimates of the data symbols, and Doppler spread information at the receiver. The wireless channel is expressed by weighted time-domain channel interpolation, where the interpolation weights are designed based on the Doppler spread and time-domain channel correlations. The channel estimates are obtained by employing a least-square (LS) method. A simplified parallel interference cancellation (PIC) scheme coupled with decision statistical combining (DSC) is used to cancel the ICI and to improve data symbol detection. These data symbols are then utilized to further refine the channel estimation iteratively. The simulation results show that the performance degradation of the proposed iterative channel estimation and ICI cancellation scheme in a high-mobility environment (e.g., normalized Doppler spread of 0.1), compared with the performance of a system when users are static and perfect channel state information (CSI) is available at the receiver, is minimal.

Preamble Design and Iterative Channel Estimation for OFDM/Offset QAM System

In present of multi-path effect, the inter symbol interference (ISI) always exists in the OFDM/OQAM system and the preamble based channel estimation for the conventional orthogonal frequency division multiplex with cyclic prefix (CP-OFDM) is not feasible any more. Considering the characteristic of the extended Gaussian function (EGF), we propose two modified preamble based channel estimation by adding zero-value guard symbols, which are located at both sides (method A) and the left side (method B) of preamble reference symbol. Compared with the CP-OFDM system, the proposed preamble based channel estimation achieves 2dB improvement. Furthermore, we shorten the preamble without zero-value guard symbol to improve the spectrum efficiency. Unfortunately, the residual inter symbol interference (ISI) from neighbor symbols degrade the channel estimation performance. We propose an iterative channel estimation method for OFDM/OQAM system to remove the residual ISI. Simulation results demonstrate that those proposed preamble design and iterative channel estimation methods are effective for OFDM/OQAM system.

Bandwidth-efficient OFDM transmission with iterative cyclic prefix reconstruction

For orthogonal frequency division multiplexing (OFDM), cyclic prefix (CP) should be longer than the length of channel impulse response, resulting in a loss of bandwidth efficiency. In this paper, we describe a new technique to restore the cyclicity of the received signal when the CP is not sufficient for OFDM systems. The proposed technique efficiently restores the cyclicity of the current received symbol by adding the weighted next received symbol to the current received symbol. Iterative CP reconstruction (CPR) procedure, based on the residual intersymbol interference cancellation (RISIC) algorithm, is analyzed and compared to the RISIC. In addition, we apply the CPR method to Alamouti space-time block coded (STBC) OFDM system. It is shown that in the STBC OFDM, tail cancellation as well as cyclic reconstruction of the CPR procedure should be repeated. The computational complexities of the RISIC, the proposed CPR, the RISIC with STBC, and the proposed CPR with STBC are analyzed and their performances are evaluated in multipath fading environments. We also propose an iterative channel estimation (CE) method for OFDM with insufficient CP. Further, we discuss the CE method for the STBC OFDM system with the CPR. It is shown that the CPR technique with the proposed CE method minimizes the loss of bandwidth efficiency due to the use of CP, without sacrificing the diversity gain of the STBC OFDM system.

Iterative Joint Channel Estimation and Data Detection Using Superimposed Training: Algorithms and Performance Analysis

Channel estimation for single-input multiple-output time-invariant channels is considered using superimposed training. A periodic (nonrandom) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission. We extend a recently proposed first-order statistics-based channel estimation approach (IEEE Commun. Lett., vol. 7, p. 413, 2003) to iterative joint channel estimation and data detection using a conditional maximum likelihood approach where the information sequence is exploited to enhance performance instead of being viewed as interference. An approximate performance analysis of the iterative channel estimation method is also presented under certain simplifying assumptions. Illustrative computer simulation examples are presented.

Iterative cyclic prefix reconstruction and channel estimation for a STBC OFDM system

In order to prevent a loss of spectral efficiency due to the use of a cyclic prefix (CP), a CP reconstruction (CPR) method, such as residual intersymbol interference cancellation (RISIC), has recently been developed. In this letter, we apply the RISIC method to an Alamouti space-time block coded (STBC) orthogonal frequency division multiplexing (OFDM) system with insufficient CP. It is shown that in the STBC OFDM, tail cancellation as well as cyclic restoration of the RISIC should be repeated. An iterative channel estimation method for the STBC OFDM system with CPR is proposed. The performance of the proposed method is evaluated by computer simulation in a multipath fading environment.