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본 논문에서는 SC-FDE (Single Carrier Frequency Domain Equalization) 전송 방식으로 통신하는 이 동 채널 환경에서 도플러 효과 때문에 시간에 따라 채널이 빠르게 변할 때 수신성능을 개선할 수 있는 새로운 채 널추정 방식을 제안한다. 보편적인 SC-FDE 구조에서는 매 SC-FDE 심볼의 앞쪽에 파일럿을 삽입하여 전송한다. 수신기는 이 파일럿을 이용하여 채널을 추정하고 이 채널 추정값을 채널을 보상하는 등화기에 활용한다. 도플러 효과로 인해 시간에 따라 채널이 변하는 경우 앞 쪽에 위치한 파일럿으로 추청한 채널은 SC-FDE 심볼 구간 전 체의 대표 채널로 사용하기에는 부족할 수 있고, 이는 등화기의 성능과 비트오류율 성능을 저하시킬 수 있다. 제 안하는 채널추정은 SC-FDE 심볼의 중간 지점에서의 채널을 추정하여 대표 채널로 사용하는 것을 목표로 한다. 이를 위해 현 심볼의 채널추정과 다음 심볼의 채널 추정을 수행하고 이 두 채널의 선형보간을 통해 중간 지점의 채널을 추정하고 이를 현 심볼의 대표채널로 채널추정과 등화를 수행한다. 이렇게 비교적 간단한 방법으로 시변 채널에서 수신성능을 상당히 개선할 수 있다. 모의실험에 따르면 제안하는 채널추정 방법을 적용하면 기존 방법에 비해 2배 빠른 이동속도에서도 동일한 비트오류율 성능이 얻을 수 있다.

In this paper, we propose a new channel estimation method to improve receiver performance in mobile channels using single-carrier frequency-domain equalization (SC-FDE) transmission when the channel varies rapidly over time due to the Doppler effect. In a conventional SC-FDE structure, a pilot is inserted at the beginning of each SC-FDE symbol. The receiver estimates the channel from this pilot and uses the estimate for channel compensation in the equalizer. However, in fast time-varying channels, the channel estimated from the front pilot may be insufficient as a representative channel for the entire SC-FDE symbol interval, which can degrade equalizer and bit-error-rate (BER) performance. The proposed method aims to estimate the channel at the midpoint of an SC-FDE symbol and use it as the representative channel. To this end, channel estimates for the current and the next symbols are obtained, and linear interpolation between the two estimates is used to derive the midpoint channel. This midpoint channel is then used as the representative channel for channel estimation and equalization of the current symbol. Despite its simplicity, the proposed method significantly improves receiver performance in time-varying channels. Simulation results show that, compared with the conventional method, the proposed method achieves the same BER performance even at twice the mobile speed.

본 논문은 C-V2X (Cellular Vehicle-to-Everything) 의 혼잡 채널 환경에서 Successive Interference Cancellation (SIC) 채널추정을 이용한 셀 간 간섭제거 (Inter-Cell Interference Cancellation: ICIC) 기법을 제시한다. Minimum Mean Square Error (MMSE) 기반의 ICIC 기법의 성능은 채 널 추정의 품질에 따라 매우 달라진다. SIC 기반 채널 추정 을 사용하여 정확한 채널 값을 얻을 수 있다. 실험 결과에서 SIC 기반 채널 추정을 사용한 ICIC가 C-V2X 혼잡 채널에서 의 처리량이 향상된다는 것을 보여준다.

Channel estimation in massive-MIMO FSO systems is critical for ensuring reliable data transmission. However, conventional estimators offer limited benefits due to the computational difficulty of accurately estimating the channel. This paper presents a novel approach to estimate channels using an attention residual U-Net (ARU-Net) architecture which utilizes the advantages of both attention and residual connection. In the simulation, the channel matrix has been represented as a 2D image. The proposed model significantly outperforms traditional channel estimation methods and other deep learning models in terms of MSE ( at 25 dB SNR), especially in atmospheric turbulence and other noises.

In this paper, we propose deep learning-based channel estimation and pilot reduction for mmWave point-to-point multi-input multi-output systems. The proposed scheme consists of a two-step approach where the first step is applying a denoising autoencoder for channel estimation. With the denoising characteristic of autoencoder, sparse channel estimation can be conducted although the orthogonality of pilot sequences is not guaranteed due to shorter pilots. The second step is exploiting the temporal correlation of the channel, using the previous estimate to extract information for the current estimate. Through simulation, the proposed scheme shows superior performance with reduced pilots.

Intelligent reflecting surfaces (IRS) have been proposed as a promising solution for enhancing wireless communication by manipulating the wireless propagation environment. To facilitate IRS-assisted communication, the base station (BS) requires accurate channel state information (CSI), which is typically obtained by receiving sounding reference signals (SRS) from the user equipment (UE). Due to numerous reflecting elements in IRS, however, the transmission of SRS is resource-intensive and reduces the available resources for data transmission. To reduce the overhead, in this paper, the SRSs are transmitted exclusively to estimate only the dominant channel at the cost of reduced CSI accuracy. Next, we consider residual interference caused by the channel estimation error, and factor it into the signal-to-interference-plus-noise-ratio of a UE. We then formulate an optimization problem that jointly computes the coefficients of reflecting elements in the IRS and MIMO precoding. Lastly, we adopt a computationally efficient algorithm based on the weighted minimization of the minimum square error (WMMSE). The performance of the proposed scheme with reduced overhead is compared to that of the ideal and conventional schemes by varying the number of SRS transmissions, IRS elements, and penetration loss.

A novel channel estimation method based on deep learning algorithm is proposed for large-scale IoT networks. We consider asymmetric backscatter communication system to maintain low-power at sensor nodes. In order to obtain channel data, we design denoising autoencoder which consists of encoder with Feedforward Neural Network (FNN) and decoder with Convolutional Neural Network (CNN). Finally, the channel estimation error is minimized, while the pilots are optimized. Especially, we adopt beamforming technique that relies only on cascaded channel data to reduce complexity in multi-sensor system. It is shown that the accuracy is slightly degraded while the complexity is greatly reduced.

This paper proposes a modified pilot selection for channel estimation of systematic polar coded MIMO-OFDM. Polar codes provably achieve the theoretical limit for communication systems and systematic polar codes generate the codeword that has the same value of information bit. Using this property, an efficient channel estimation scheme was proposed; values of some information bits are fixed to zeros or ones and utilized for pilot symbols. Similarly, we also can fix values of some frozen bit as zeros or ones by exploiting features of the encoding generator. By combining these advantages, pilot signals are selected from the systematic polar coded symbols themselves and we do not need add and insert pilot symbols just to estimate the channel. Leveraging this, we apply the proposed systematic polar coding scheme to MIMO-OFDM system under frequency selective fading channel.

This paper presents a reduced rank channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) high speed railway (HSR) systems. Conventional interpolation based channel estimators require high pilot load for robust estimation of the rapidly time varying frequency-selective MIMO-OFDM HSR channel. To relax the high pilot overhead requirement, we take advantage of the channel’s restriction to low dimensional subspaces due to the time, frequency and spatial correlation and propose a low complexity linear minimum mean square error (LMMSE) channel estimator. The channel estimator utilizes a four-dimensional (4D) basis expansion channel model obtained from band-limited generalized discrete prolate spheroidal (GDPS) sequences. Simulation results validate that the mean square estimation error of the proposed estimator is smaller than that of the conventional interpolation based least square (LS) estimator and the performance is robust to different delay, Doppler and angular spreads.

Reliable channel estimation is critical for achieving high-speed and energy-efficient communication in Unmanned Aerial Vehicle-Free Space Optical (UAV-FSO) systems, particularly under dynamic impairments such as atmospheric turbulence (AT) and pointing errors (PEs). This paper proposes a pilot-free channel estimation framework based on a Variational Autoencoder (VAE). The system employs Intensity Modulation/Direct Detection (IM/DD) with?-ary one-hot encoded symbols (). The VAE encodes noisy received signals into a 128-dimensional latent space and reconstructs the transmitted data, effectively learning the joint effects of AT, PEs, and AWGN. Unlike prior works that primarily consider boresight or Gaussian-jitter PEs, this study explicitly incorporates a Nakagami-modeled PE distribution, capturing UAV-induced beam misalignment under mobility, vibration, and turbulence coupling. Simulation results show that the proposed VAE significantly outperforms conventional estimators (LS, MMSE, LMMSE) and deep learning baselines (AE, DNN, CNN) across various turbulence strengths. Under strong turbulence and PEs, the VAE attains nearly two-fold lower MSE compared to CNN and DNN. In addition, evaluation on real turbulence-impaired datasets further validates robustness and generalization. The proposed pilot-free scheme delivers accurate channel estimation, reduced BER, and improved spectral efficiency, making it suitable for real-time adaptive UAV-FSO communication.

Most studies on symbol-level (SL) precoding have assumed that channel estimation is perfect. However, because interference signals or additive white Gaussian noise exist in the received signal for channel estimation, channel estimation errors always exist. In this paper, we propose an SL precoding scheme that is robust to channel estimation errors. First, using the characteristics of the channel estimation errors, we derive an equation for the worst-case mean squared error (MSE) which is the maximum of the MSE. Then by designing the SL precoding to minimize the worst-case MSE, it has robust characteristics against channel estimation errors.

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.

Optical Intelligent Reflective Surface (OIRS)-assisted MIMO VLC offers high-capacity communication but struggles with accurate channel estimation due to multipath and noise. We propose a hybrid CNN?Swin Transformer denoising network (HCSTNet), integrating a CNN-based noise estimator with a Swin Transformer bottleneck to learn local features and global spatial dependencies jointly. In a realistic indoor MIMO VLC setup with LOS, NLOS, and OIRS links, HCSTNet achieves an NMSE of???and outperforms existing models in terms of PSNR while also reducing the parameter count and inference time. These results demonstrate the efficiency and robustness of HCSTNet for practical VLC applications.

This article proposes a novel wireless channel estimation method suited for fast fading environment. Generally, forward error correction (FEC) requires long block code length to raise error correction capability while it deteriorates the channel tracking accuracy in pilot-aided estimation. To meet these trade-off demands, we propose a frame splitting and decision directed channel estimation (DDCE). It splits information data in half and estimate channel state information (CSI) of the former part. That of latter is simply extrapolated using the former CSI. Computer simulation result shows that the proposed method can improve bit error rate performance even at fast fading environment.

In this paper, we propose a method to estimate the mean square error (MSE) of the estimated channel for ATSC (Advanced Television Systems Committee) 3.0 systems. When combining the channel MSE and noise variance, we can better estimate the a priori LLR (log likelihood ratio) for the sum?product algorithm. The experimental results show that doing so yields better BER (bit error rate) performance in the 0 dB echo channel. The improvement in the 2-D channel estimation is about 0.2 dB. In the 1-D estimation case, the proposed approach is essential to decode codewords.

5G 시스템에서는, 통신 서비스가 4G 통신보다 더욱 더 빠른 망 연결을 요구한다. 최근, 선도적인 5G 기술들 중 하나로 비직교 다중접속이 주목받고 있다. 비직교 다중접속에서는 사용자들이 주파수와 시간을 공유하 여, 더 많은 사용자들이 동시에 서비스를 받을 수가 있다. LTE와 같은 직교 다중접속과 비교하면, 비직교 다중접속 은 더 큰 시스템 용량과 초저 지연과 같은 장점을 가진다. 본 논문에서는 비직교 다중접속의 SIC에 채널 추정 오류가 미치는 영향을 고찰한다. 우선, 채널 추정 오류를 가지는 수신기의 폐쇄형 수식을 구한다. 그리고 채널 추 정 오류의 BER를 완벽한 채널 추정의 BER과 비교한다, 또한, 채널 추정 오류로 발생하는 SNR 손실을 분석한다.

In the fifth generation (5G) networks, the mobile services require much faster connections than in the fourth generation (4G) mobile networks. Recently, as one of the promising 5G technologies, non-orthogonal multiple access (NOMA) has been drawing attention. In NOMA, the users share the frequency and time, so that the more users can be served simultaneously. NOMA has several superiorites over orthogonal multiple access (OMA) of long term evolution (LTE), such as higher system capacity and low transmission latency. In this paper, we investigate impact of channel estimation errors on successive interference cancellation (SIC) performance of NOMA. First, the closed-form expression of the bit-error rate (BER) with channel estimation errors is derived, And then the BER with channel estimation errors is compared to that with the perfect channel estimation. In addition, the signal-to-noise (SNR) loss due to channel estimation errors is analyzed.

예측할 수 없는 강우 패턴으로 인해 수자원 조사 시 지속적인 유속 모니터링은 많은 비용과 여러 어려움 을 가지고 있다. 이에 비교적 간편하고 안전성이 확보된 비접촉식 유속계를 활용하여 유량측정을 수행하 는 경우가 많아지고 있다. 하지만 일반적으로 사용되는 평균유속 환산계수 0.85 값을 일괄적으로 사용하 여 지역적 특성을 고려하지 못하고 있는 실정이다. 본 연구에서는 농업용수를 공급하는 소규모 저수지 중 하상경사가 급한 1개 지점, 하상경사가 완만한 1개 지점, 총 2개 지점의 관개수로에서 2가지 타입의 유속계를 이용하여 관측하고 이를 통해 평균유속을 산정하고 연구 대상 저수지의 지점 특성을 고려한 환산계수를 산정하였다. 완만한 하상경사를 가진 쌍암저수지 관개수로의 경우 유속환산계수 값이 1.06으 로 나타났고 수심평균 유속인 V0.6이 표면유속보다 더 높게 측정되었다. 반면 비교적 급한 하상경사를 가진 한계저수지의 경우는 유속환산계수 값이 0.83으로 일반적으로 사용되는 유속환산계수(0.85)와 비슷 한 값으로 나타났다. 이를 통해 하상경사가 수로내 수심방향의 유속 특성에 지배적인 영향을 미치는 것으로 분석되었다.

The continuous flow velocity monitoring has become a difficult and expensive task in hydrological survey due to unpredictable rainfall patterns. Therefore, the velocity measurements using a non-contact type flowmeter is more effective and easy to use. The generally used average velocity conversion factor 0.85 does not take into account the regional characteristics such as curves of a river, bed materials which consequentially affects the vertical flow characteristics. Therefore, considering the those characteristics of irrigation channels of two small reservoirs (Ssangam and Hangye reservoir), results showed that the gentle bed slope of irrigation channel of the Ssangam reservoir, has a velocity conversion factor of 1.06 and the depth-averaged velocity of V0.6, was measured to be higher compared to the surface velocity. On the other hand, velocity conversion factor of 0.83 was computed in case of Hangye reservoir with a relatively sharp bed slope, which is similar to the generally used velocity conversion factor of 0.85. This shows that the bed slope is the dominant factor in affecting the depth-wise velocity characteristics of a waterway.

논문에서는 길쌈 부호화 시공간 블록 부호 시스템의 오류 성능에 대한 채널 변화 및 채널 추정 오류의 영향에 대해 살펴보았다. 본 논문에서는 직교 방식의 Alamouti 시공간 블록 부호 및 준직교 방식의 Jafarkhani 시공간 블록 부호의 두 가지 시공간 블록 부호 방식을 고려하여, 길쌈 부호화 시공간 블록 부호 시스템들에서 채널 변화 및 추정 오류 정도에 따른 오류 성능 변화를 모의실험을 통해 분석하였다. 분석 결과 채널 변화 속도가 느린 경우에는 시간 다이버시티 효과로 인해 채널 변화가 없는 상황 대비 검출 방식에 관계없이 오류 성능이 향상되나, 채널 변화 속도가 빠른 경우에는 검출 과정에서의 오류로 인해 특히 준직교 Jafarkhani 적용 시스템에서 STBC 검출 사용 시 ZF 및 MMSE 검출 사용 시와 달리 큰 폭의 성능 열화가 발생함을 관측하였다. 또한 채널 추정 오류 증가 시에는 검출 기법 및 채널 변화 속도에 관계없이 큰 폭의 성능 열화가 발생함을 확인하였다.

This paper investigates the impact of the channel variations and channel estimation errors on the error performance of convolutional coded STBC systems. We consider the orthogonal Almouti STBC and the quasi-orthogonal Jafarkhani STBC, and the error performance of the convolutional coded STBC system is investigated according to the channel variation and channel estimation error via numerical simulations. Simulation results show that, if the channel variation speed is slow, time diversity effects improve the error performance compared to the static-channel cases. However, if the channel variation speed is fast, unlike ZF or MMSE detection, the conventional STBC detection has the significant performance degradation especially with the quasi-orthogonal Jafarkhani STBC. Further, the error performance of the system is significantly degraded as the channel estimation errors become stronger, regardless of the detection scheme and channel variation speed.

The paper proposes a new channel estimation algorithm which has a good performance and low complexity computation in WiBRO system. The proposed method estimates the Time Offset(Sample Delay) and CFO(Carrier Frequency Offset) by using the pilots, after compensate the pilots interpolate them we can compensate the data channels, then can get the improved performance.

본 논문에서는 고속전송을 위해 넓은 대역폭을 사용하는 OFDM (Orthogonal Frequency Division Multiflex)시스템에서 심각한 주파수 선택적 페이딩 환경에서도 강한 특성을 갖는 FE(Frequency Estimation) 채널 추정 기법을 제안하였다. 시간적으로 천천히 변화하는 채널에서 주로 사용하는 채널추정기법은 상관도가 높은 심볼간 데이터를 이용하는 DDCE(Decision Directed Channel Estimation)기법이 있다. DDCE는 주파수 비선택적인 환경에서는 높은 이득을 보이지만 시스템 대역폭이 증가함에 따라 심각한 주파수 선택적 페이딩 환경이 되어 데이터의 신뢰도가 떨어지면 에러 플로우 현상이 일어나게 된다. 제안한 방법인 FE 기법은 주파수 선택적 페이딩 채널에서 OFDM의 부반송파간의 상관관계를 이용하여 목표 주파수에서의 채널 추정감을 인접 부반송파의 채널 추정값들의 평균으로 적응시켜주어 이득을 얻는다. FE 기법은 DDCE처럼 데이터를 이용하지 않고 프리엠블만을 이용하기 때문에 데이터의 신뢰도와 관계된 전송율과 다중 경로 수에 독립적이다 본 논문에서 제안한 FE 기법은 DDCE의 에러 플로우가 발생한 넓은 대역폭을 가진 시스템에서도 이득을 얻을 수 있어 심각한 주파수 선택적 페이딩 환경에서 유용하다.

This paper proposes the channel estimation method robust to severe frequency selective fading channels in O%M system using wide bandwidth for the high data rate transmission. DDCE, which uses data between the high correlated symbols, is usually used for channel estimation in the slow fading channels. DDCE can get high gains in the non-selective channels. As the bandwidth of system gets wider, it becomes more severe frequency selective fading environments so that the reliability of data becomes lower and error flow is occurred. FE method, this paper proposed, uses the relation between sub-carriers of OFDM in frequency selective fading channels so FE method gets some gains by adapting the power value at a target frequency to the mean value of channel estimated values of adjacent sub-carriers. Because FE uses only preamble unlike DDCE using data, it is independent of data rate related to the reliability of data and the number of multipath. Consequently, FE can obtain considerable gains in the wideband systems where the errorflow of DDCE is occurred, and FE is applicable to frequency selective fading environments.