Linear Minimum(선형 최소값)란 무엇입니까?
Linear Minimum 선형 최소값 - We propose a new design for a repetitive control scheme for nonlinear minimum-phase systems with arbitrary relative degree and globally Lipschitz nonlinearities. [1] In order to solve the above problem, this paper proposes two novel filters according to the linear minimum-variance unbiased estimation criterion. [2]임의의 상대 차수와 전역적으로 Lipschitz 비선형성을 갖는 비선형 최소 위상 시스템에 대한 반복 제어 방식을 위한 새로운 설계를 제안합니다. [1] 위의 문제를 해결하기 위해 본 논문에서는 선형 최소 분산 무편향 추정 기준에 따라 두 개의 새로운 필터를 제안한다. [2]
mean square error 평균 제곱 오차
In this brief, we investigate the widely linear minimum mean square error (WLMMSE) successive interference cancellation (SIC) detector in multiple-input-multiple-output systems with rectilinear or quasi-rectilinear transmit signals. [1] In particular, we apply the linear minimum mean square error technique to estimate the time-varying channel gain and adopt extended Kalman filtering to track the time-varying phase noise. [2] Closed-form linear minimum mean square error (LMMSE) channel estimation and achievable spectral efficiency (SE) expressions with maximal ratio combining (MRC) receiver filters are derived. [3] Specifically, we first introduce a quantization-aware technique for channel estimation based on linear minimum mean-square error (LMMSE) theory. [4] In particular, our algorithm provides a significant improvement over the practical least squares (LS) estimation method and provides performance that approaches that of the ideal linear minimum mean square error (LMMSE) estimation with perfect knowledge of channel statistics. [5] We firstly investigate widely-linear minimum mean square error (WL-MMSE) estimation for the effective channel which is the product of the IQI coefficients and the wireless channel. [6] Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). [7] We apply SPADE to beamspace linear minimum mean square error (LMMSE) spatial equalization in all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-output (MU-MIMO) systems. [8] A multi-linear minimum mean square error (MMSE) receiver using the tensor framework is also presented. [9] Linear minimum mean square error (LMMSE) receivers are often applied in practical communication scenarios for single-input-multiple-output (SIMO) systems owing to their low computational complexity and competitive performance. [10] The performance of the LMMSE (Linear Minimum Mean Square Error) algorithm is equivalent to the proposed algorithm also in terms of bit error rate. [11] The proposed NN architecture uses fully connected layers for frequency-aware pilot design, and outperforms linear minimum mean square error (LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices utilizing convolutional NN layers. [12] In this study we propose a novel correction scheme that filters Magnetic Resonance Images data, by using a modified Linear Minimum Mean Square Error (LMMSE) estimator which takes into account the joint information of the local features. [13] This paper presents a performance comparison between the single-carrier frequency-domain equalization (SCFDE) system and an experimental, linear minimum-mean-square-error (LMMSE) based single-carrier system operating in the time domain (SC-TDE). [14] Unlike the Gaussian approximate process of the input symbols of the traditional linear minimum mean square error equalization, the discrete independent random variable form of the input symbols of the JMTE algorithm is retained to avoid input information loss. [15] The linear minimum mean square error (LMMSE) channel estimation yields the optimal performance in terms of the mean square error (MSE). [16] For sensing matrices with low-to-moderate condition numbers, CAMP can achieve the same performance as high-complexity orthogonal/vector AMP that requires the linear minimum mean-square error (LMMSE) filter instead of the MF. [17] We also describe a time domain low complexity linear minimum mean square error (MMSE) equalization and successive interference cancellation (SIC) receiver for LDPC (low density parity check) coded CP-OTFS in this work. [18] Moreover, we unveil and prove that conventional elementary signal estimator (ESE) and linear minimum mean square error (LMMSE) receivers are special cases of EP and VEP, respectively, thus bridging the gap between classic linear receivers and message passing based nonlinear receivers. [19] The BS uses either least squares (LS) or linear minimum mean square error (LMMSE) estimators for channel estimation and utilizes either maximum-ratio-combining (MRC) or zero-forcing (ZF) decoding vectors. [20] In this study, we first derive linear minimum mean-square-error channel estimator for cyclic-prefix (CP) free single carrier MIMO (SC-MIMO) under frequency-selective channel. [21] Simulation results show that the proposed art significantly outperforms both classic receivers, such as the linear minimum mean square error (LMMSE), and recent CS-based state-of-the-art (SotA) alternatives, such as the sum-of-absolute-values (SOAV) and the sum of complex sparse regularizers (SCSR) detectors. [22] Then a linear minimum mean square error estimator is constructed using the nonlinear uncorrelated MT, and a Bayesian filter under Gaussian assumptions is derived. [23] Numerical tests show that the DCCN receiver can outperform the legacy channel estimators based on ideal and approximate linear minimum mean square error (LMMSE) estimation and a conventional CP-enhanced technique in Rayleigh fading channels with various delay spreads and mobility. [24] Then, we develop an approximation for the spectral efficiency of various detectors such as maximum-ratio combining (MRC), zero-forcing (ZF), and linear minimum mean square error (LMMSE). [25] This article studies the distributed linear minimum mean square error (LMMSE) estimation problem for large-scale systems with local information (LSLI). [26] Then, the ML-based channel predictor using the linear minimum mean square error (LMMSE)-based noise pre-processed data is developed. [27] The signal is modulated on the scatter coefficient of a single eigenvalue and linear minimum mean square error (LMMSE) estimator is used to reduce the noise. [28] This paper proposed a system based on the special multiplexing (SM) technique and linear minimum mean square error (LMMSE) detection method with the assistance of the hamming code as well as the interleaving techniques for a better enhanced performance of an audio transmission. [29] In addition to objective quality assessments, the subjective evaluations carried out by radiologist and neurologists show the relatively better visual quality of the proposed method compared to the methods such as linear minimum mean square error (LMMSE) and bilateral filtering (BF). [30] We consider linear minimum mean square error (LMMSE) reception for a multiuser MIMO uplink, and provide performance guarantees based on two key concepts: (a) summarization of the impact of per-antenna nonlinearities via a quantity that we term the “intrinsic SNR”, (b) using linear MMSE performance in an ideal system without nonlinearities to bound that in our non-ideal system. [31] Furthermore, we propose novel least squares (LS) and linear minimum mean square error (LMMSE) channel estimators by considering the energy concentration and spectral compaction properties of DCT-I for the uplink NB-IoT system. [32] The linear minimum mean square error (LMMSE) scheme is very effective in estimating the channel but introduces massive complexity because of having complex matrix inversion. [33] The analytical results of the linear minimum mean square error (MMSE) channel estimation show that there is nonzero floor on the estimation error with respect to the RF impairments, ADC/DAC precision and the pilot power of the eavesdropper which is different from the conventional case with perfect transceiver. [34] We propose iterative linear minimum mean-square error (MMSE) precoding along with optimal and uniform power allocation. [35] First, with linear minimum mean square error estimation, we theoretically characterize the relationship between channel estimation performance and impairment level, number of reflecting elements, and pilot power. [36] Based on the orthogonal pilot and linear minimum mean square error (LMMSE) estimation, the LDSM optimized by bare-bone particle swarm optimization (BBPSO) algorithm has a larger girth and can gather more accurate information in the process of iterative decoding convergence. [37] The receiver in downlink performs the detection in two sequential phases: first, the conventional OTFS detection using the method of linear minimum mean square error (LMMSE) estimation, and then the SCMA detection. [38] To do this, a non-linear blind edge-guided spatial filter based on linear minimum mean square error-estimation (LMMSE) theory has recently been proposed for video sequence reconstruction problems in green monitoring with radars. [39] When the SOHE model is employed to analyze the linear minimum-mean-square-error (LMMSE) channel equalizer, it is revealed that the current LMMSE algorithm can be enhanced by incorporating a symbol-level normalization mechanism. [40] Numerical results show the improvement provided by the proposed RMMSE precoder against linear minimum mean-square error, zero-forcing and conjugate beamforming precoders in the presence of imperfect CSI. [41] In the basic estimation process, an NSCT shrinkage method that is suitable for Poisson-Gaussian noise characteristics is developed by optimizing the local linear minimum mean square error estimator in the NSCT domain. [42] Accordingly, the proposed KF operation performs symbol-level combining (SLC) based on linear minimum mean-square-error (LMMSE) detection using the information aggregated up to the current TTI. [43] We then propose a compressive sensing algorithm enabling the server to iteratively find the linear minimum-mean-square-error (LMMSE) estimate of the transmitted signal by exploiting its sparsity. [44] The trained model detects the noise state online and thus applies a linear minimum mean square error (LMMSE) method to estimate the source signal. [45] The time selective fading links arise due to nodes’ mobility are modeled via first-order auto-regressive process, and the channel state information is estimated using linear minimum mean square error estimation method. [46] The simulation results show that our scheme is superior to other SR-based channel estimation methods and close to the linear minimum mean square error (LMMSE) performance. [47] The estimated channel coefficients are used by a MIMO 2×2 soft-input soft-output (SISO) linear minimum mean square error (LMMSE) equalizer to compensate for the time-varying ISI. [48] The proposed channel estimation method showed a performance gain of 4 dB in NMSE compared to linear minimum mean square error for clustered delay line channel model at signal to noise ratio of 30 dB, and is robust to mismatches in parameters like DS and DoS because of estimation errors. [49] Simulation results illustrate that the proposed nonlinear estimator achieves better estimation qualities compared with the existing nonlinear minimum mean square error methods. [50]이 요약에서는 직선 또는 준직선 전송 신호가 있는 다중 입력 다중 출력 시스템에서 광범위하게 선형인 WLMMSE(최소 평균 제곱 오차) SIC(연속 간섭 제거) 검출기를 조사합니다. [1] 특히, 선형 최소 평균 제곱 오차 기법을 적용하여 시변 채널 이득을 추정하고 확장 칼만 필터링을 채택하여 시변 위상 잡음을 추적합니다. [2] nan [3] nan [4] nan [5] nan [6] 다중 입력 다중 출력(MIMO) 무선 센서 네트워크(WSN)에서 베이지안 학습(BL) 기반 희소 매개변수 벡터 추정을 위한 최적의 선형 최소 평균 제곱 오차(MMSE) 송수신기 설계 기술이 제안됩니다. [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15] nan [16] nan [17] 우리는 또한 이 작업에서 LDPC(저밀도 패리티 검사) 코딩된 CP-OTFS를 위한 시간 영역 저복잡도 선형 최소 평균 제곱 오차(MMSE) 등화 및 연속 간섭 제거(SIC) 수신기에 대해 설명합니다. [18] nan [19] nan [20] nan [21] nan [22] nan [23] nan [24] nan [25] nan [26] nan [27] nan [28] nan [29] nan [30] nan [31] nan [32] nan [33] nan [34] 우리는 최적의 균일한 전력 할당과 함께 반복적인 선형 최소 평균 제곱 오차(MMSE) 프리코딩을 제안합니다. [35] nan [36] nan [37] nan [38] nan [39] nan [40] nan [41] 기본 추정 과정에서 NSCT 영역에서 국소 선형 최소 평균 제곱 오차 추정기를 최적화하여 Poisson-Gaussian 노이즈 특성에 적합한 NSCT 수축 방법을 개발합니다. [42] nan [43] nan [44] nan [45] nan [46] nan [47] nan [48] nan [49] nan [50]
mean squared error 평균 제곱 오차
Assuming linear minimum mean squared error (LMMSE) multiuser detection, we provide an analytical framework that can be used to specify the required power spectral density (PSD) mask for phase noise for a target system performance. [1] We show that this system outperforms the traditional 2-D linear minimum mean squared error (2-D-LMMSE) equalizer. [2] The Monte Carlo simulations are used to corroborate the theoretical results, which demonstrate that the proposed PC estimators are robust to the uncertainties compared to the nominal linear minimum mean squared error (LMMSE) estimator and the nominal best linear unbiased estimator (BLUE), and are less conservative compared to the traditional minimax robust estimator. [3] We derive closed form expression for the sum rate considering the Rician channel model, linear minimum mean squared error (LMMSE) is considered for uplink channel estimation, while maximum ratio transmission (MRT) is considered for the downlink. [4] We propose two turbo detector algorithms: orthogonal approximate message passing with linear minimum mean squared error (OAMP-LMMSE) and Gaussian approximate message passing with expectation propagation (GAMP-EP). [5] First, we describe the linear minimum mean-squared error (LMMSE) estimator and its associated mean-squared error (MSE) for the general mixed-resolution model. [6] We develop a linear minimum mean squared error (LMMSE) channel estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. [7] We consider Rician fading and maximum ratio processing based on either linear minimum mean-squared error (LMMSE) or least-squares (LS) channel estimation. [8] It comprises of three modules: 1) Dynamic estimation of the temporal response functions (TRF) in every trial using a sequential linear minimum mean squared error (LMMSE) estimator, 2) Extract the N1-P2 peak of the estimated TRF that serves as a marker related to the attentional state and 3) Obtain a probabilistic measure of the attentional state using a support vector machine followed by a logistic regression. [9] We introduce a joint weighted Neumann series (WNS) and Gauss–Seidel (GS) approach to implement an approximated linear minimum mean-squared error (LMMSE) detector for uplink massive multiple-input multiple-output (M-MIMO) systems. [10] Then a linear minimum mean squared error (MMSE) method is applied with these taps to estimate residual CE error value for each unique scenario, assuming Gaussian distribution of tap amplitudes and antenna noise. [11] In this study, we propose a low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator for such low-resolution MIMO receivers. [12] A linear Minimum Mean Squared Error (MMSE) based estimator is then employed to position the flying object passively. [13] In contrast to existing work, we propose an machine learning (ML)-enhanced MU-MIMO receiver that builds on top of a conventional linear minimum mean squared error (LMMSE) architecture. [14] The receiver is assumed to operate in two sequential stages that employ Linear Minimum Mean Squared Error (LMMSE) receivers. [15] The missing data can be recovered with the obtained spectral estimate using a linear minimum mean-squared error estimator. [16] Relying on the covariance matrix of linear minimum mean-squared-error (LMMSE) estimates of input symbols, we derive a novel communications metric as a function of both subcarrier powers and forwarded control information, and propose a joint waveform and control signaling optimization (JWCSO) strategy that leverages the sparsity and rank-one structure of DFRC waveforms within an alternating maximization framework. [17] We compare the performance and complexity of four different soft-output equalization algorithms, namely, two approximations of the linear minimum mean squared error (LMMSE) equalizer, a BCJR equalizer and a deep-learning based equalizer, for such systems. [18] Exploiting the statistical correlation between the noise in the sub-carriers—which is shown to be stronger in narrow-bandwidth선형 최소 평균 제곱 오차(LMMSE) 다중 사용자 감지를 가정하면 대상 시스템 성능에 대한 위상 노이즈에 필요한 전력 스펙트럼 밀도(PSD) 마스크를 지정하는 데 사용할 수 있는 분석 프레임워크를 제공합니다. [1] 우리는 이 시스템이 기존의 2D 선형 최소 평균 제곱 오차(2-D-LMMSE) 등화기를 능가한다는 것을 보여줍니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15] nan [16] nan [17] nan [18] nan [19] nan [20] nan [21] 로컬 선형 최소 평균 제곱 오류 수정, 분산 안정화 변환 및 마지막으로 적응형 양방향 필터링이 뒤따릅니다. [22] nan [23] nan [24] Boosted AMP를 통한 AUD 시 이 수신기는 잘 알려진 선형 최소 평균 제곱 오차(MMSE)를 통해 CE 및 데이터 감지를 모두 수행합니다. [25] 선형 최소 평균 제곱 오차(LMMSE)는 최적에 가까운 성능을 달성할 수 있지만 계산 비용이 많이 드는 대규모 행렬 반전이 필요합니다. [26] 우리는 각 반복에서 상태 합의 단계와 로컬 추정 단계를 포함하는 분산 반복 선형 최소 평균 제곱 오차(LMMSE) 알고리즘을 제안합니다. [27] 시뮬레이션 결과는 제안된 방식이 전체 신호 대 잡음비(SNR) 영역에서 최소 자승(LS) 및 Bussgang 선형 최소 평균 제곱 오차(BLMMSE) 채널 추정기보다 성능이 우수함을 보여줍니다. [28] CE-NET은 최소 제곱 채널 추정 알고리즘에 의해 초기화되고 선형 최소 평균 제곱 오차 신경망에 의해 개선됩니다. [29] 그 성능은 최신 왜곡 인식 및 비인식 베이지안 선형 최소 평균 제곱 오차(LMMSE) 추정기와 비교됩니다. [30] 도청자는 인코딩을 인식하지 않고 인코딩된 매개변수의 노이즈 관찰을 기반으로 선형 최소 평균 제곱 오차 추정기를 사용하도록 모델링되었습니다. [31] 그런 다음 1비트 오버샘플링된 시스템에 대한 저해상도 인식 선형 최소 평균 제곱 오차 채널 추정기를 제안합니다. [32] 센서 결함 및 OOSM(순서 외 측정) 관측이 동시에 존재하는 상태 추정의 문제에 초점을 맞추고 OOSM의 임의 시간 지연을 갖는 선형 최소 평균 제곱 오차(LMMSE) 필터의 공식화, 일반화 현재 작업의 핵심은 선형 최적 방식으로 임의의 지연에 도달하는 OOSM과 함께 상관된 결함을 동시에 처리하는 것입니다. [33] 시스템 모델을 설명한 후 DTU 및 RTU에 대한 선형 최소 평균 제곱 오차 채널 추정을 조사하고 채널 추정 정확도의 다루기 쉬운 표현을 도출합니다. [34] 반복 중 특별히 설계된 직교 투영 및 결정 작업과 함께 이 접근 방식은 Kaczmarz 또는 좌표 하강 반복의 독립 실행형 사용보다 성능이 뛰어나고 선형 최소 평균 제곱 오류 감지보다 더 나은 오류율 성능을 제공합니다. [35] 이 논문에서는 대규모 MIMO 시스템의 맥락에서 채널 정보 획득과 데이터 심볼 복구 모두에 LMMSE 추정기를 사용하는 선형 최소 평균 제곱 오차(LMMSE) 수신기를 고려합니다. [36] nan [37] nan [38] nan [39] nan [40] nan [41] nan [42] nan [43] nan [44] nan [45]
orthogonal frequency division 직교 주파수 분할
The proposed algorithm is integrated with orthogonal frequency-division multiplexing (OFDM) to eliminate intersymbol interference (ISI) induced by the frequency-selective multipath channel and compared with the well-known least square (LS) and linear minimum mean square error (LMMSE) channel estimation algorithms. [1] In orthogonal frequency division multiplexing (OFDM) systems, the least square (LS) in addition to the technique of linear minimum mean square error (LMMSE) channel estimation are compared using frequency as well as time domain in this paper. [2] Assuming orthogonal frequency-division multiplexing (OFDM) transmission, we leverage Bussgang’s theorem to derive an equivalent linear model and we formulate a distortion-aware linear minimum mean squared error (LMMSE)-based receiver. [3]제안된 알고리즘은 OFDM(orthogonal frequency-division multiplexing)과 통합되어 주파수 선택 다중 경로 채널에 의해 유도되는 ISI(기호 간 간섭)를 제거하고 잘 알려진 LS(최소 제곱) 및 LMMSE(최소 평균 제곱 오차)와 비교합니다. 채널 추정 알고리즘. [1] 본 논문에서는 OFDM(Orthogonal Frequency Division Multiplexing) 시스템에서 LMMSE(Linear Minimum Mean Square Error) 채널 추정 기법과 함께 주파수 및 시간 영역을 사용하여 최소 제곱(LS)을 비교합니다. [2] 직교 주파수 분할 다중화(OFDM) 전송을 가정하면 Bussgang의 정리를 활용하여 등가 선형 모델을 도출하고 왜곡 인식 선형 최소 평균 제곱 오차(LMMSE) 기반 수신기를 공식화합니다. [3]
bit error rate
The proposed algorithm exchanges the exponential complexity in channel length for a linear complexity in the number of particles and achieves better bit error rate than the linear minimum mean square error (LMMSE) detector. [1]제안된 알고리즘은 채널 길이의 지수적 복잡성을 입자 수의 선형 복잡성으로 교환하고 선형 최소 평균 제곱 오차(LMSE) 검출기보다 더 나은 비트 오류율을 달성합니다. [1]
multiple input multiple
In massive multiple-input multiple-output (MIMO) system, Neumann series (NS) expansion-based linear minimum mean square error (LMMSE) detection has been proposed due to its simple and efficient multi-stage pipeline hardware implementation. [1]대규모 다중 입력 다중 출력(MIMO) 시스템에서 Neumann 시리즈(NS) 확장 기반 선형 최소 평균 제곱 오차(LMMSE) 감지는 간단하고 효율적인 다단계 파이프라인 하드웨어 구현으로 인해 제안되었습니다. [1]
Iterative Linear Minimum
We propose iterative linear minimum mean-square error (MMSE) precoding along with optimal and uniform power allocation. [1] We propose a distributed iterative linear minimum mean squared error (LMMSE) algorithm that contains a state consensus stage and a local estimation stage in each iteration. [2] The channel estimation is achieved by deriving an iterative linear minimum mean-square-error (LMMSE) algorithm, with a weighted least square algorithm as the initialization point. [3] This paper considers a low-complexity iterative linear minimum mean square error (LMMSE) multiuser detector for the multiple-input and multiple-output system with nonorthogonal multiple access (MIMO-NOMA), where multiple single-antenna users simultaneously communicate with a multiple-antenna base station (BS). [4]우리는 최적의 균일한 전력 할당과 함께 반복적인 선형 최소 평균 제곱 오차(MMSE) 프리코딩을 제안합니다. [1] 우리는 각 반복에서 상태 합의 단계와 로컬 추정 단계를 포함하는 분산 반복 선형 최소 평균 제곱 오차(LMMSE) 알고리즘을 제안합니다. [2] 채널 추정은 가중 최소 제곱 알고리즘을 초기화 지점으로 사용하여 반복 선형 최소 평균 제곱 오차(LMMSE) 알고리즘을 유도하여 수행됩니다. [3] nan [4]
Optimal Linear Minimum
Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). [1] The clean coefficients are estimated based on optimal linear minimum mean square error (LMMSE) estimation with a shrinkage on Saak coefficients. [2] An optimal maximum likelihood (ML) receiver serves as a performance benchmark, and a sub-optimal linear minimum mean square error introduces a reduced-complexity implementation. [3]다중 입력 다중 출력(MIMO) 무선 센서 네트워크(WSN)에서 베이지안 학습(BL) 기반 희소 매개변수 벡터 추정을 위한 최적의 선형 최소 평균 제곱 오차(MMSE) 송수신기 설계 기술이 제안됩니다. [1] 깨끗한 계수는 Saak 계수의 수축과 함께 최적의 선형 최소 평균 제곱 오차(LMMSE) 추정을 기반으로 추정됩니다. [2] 최적의 최대 가능도(ML) 수신기는 성능 벤치마크 역할을 하며 차선의 선형 최소 평균 제곱 오차는 복잡성 감소 구현을 도입합니다. [3]
Aware Linear Minimum
In this study, we propose a low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator for such low-resolution MIMO receivers. [1] We then propose a low-resolution aware linear minimum mean-squared error channel estimator for 1-bit oversampled systems. [2] Assuming orthogonal frequency-division multiplexing (OFDM) transmission, we leverage Bussgang’s theorem to derive an equivalent linear model and we formulate a distortion-aware linear minimum mean squared error (LMMSE)-based receiver. [3]nan [1] 그런 다음 1비트 오버샘플링된 시스템에 대한 저해상도 인식 선형 최소 평균 제곱 오차 채널 추정기를 제안합니다. [2] 직교 주파수 분할 다중화(OFDM) 전송을 가정하면 Bussgang의 정리를 활용하여 등가 선형 모델을 도출하고 왜곡 인식 선형 최소 평균 제곱 오차(LMMSE) 기반 수신기를 공식화합니다. [3]
Widely Linear Minimum
In this brief, we investigate the widely linear minimum mean square error (WLMMSE) successive interference cancellation (SIC) detector in multiple-input-multiple-output systems with rectilinear or quasi-rectilinear transmit signals. [1] To solve this problem, a novel widely linear minimum-mean-square-error (WLMMSE) anti-collision method is proposed by taking into account the improper second-order statistics of backscattered tag signals, which result from the quadrature nature of RFID readers. [2] This paper proposes a new Widely Linear Minimum Mean Square Error with Decision Feedback Equalizer (WL-MMSE-DFE) based on the Expectation Propagation (EP) formalism for Faster-Than-Nyquist (FTN) communications. [3]이 요약에서는 직선 또는 준직선 전송 신호가 있는 다중 입력 다중 출력 시스템에서 광범위하게 선형인 WLMMSE(최소 평균 제곱 오차) SIC(연속 간섭 제거) 검출기를 조사합니다. [1] 이 문제를 해결하기 위해 RFID 리더의 직교 특성으로 인해 발생하는 후방 산란 태그 신호의 부적절한 2차 통계를 고려하여 새로운 WLMMSE(최소 평균 제곱 오차) 충돌 방지 방법이 제안되었습니다. [2] 이 논문에서는 FTN(Faster-Than-Nyquist) 통신을 위한 EP(Expectation Propagation) 형식을 기반으로 하는 새로운 WL-MMSE-DFE(Widely Linear Minimum Mean Square Error with Decision Feedback Equalizer)를 제안합니다. [3]
Complexity Linear Minimum 복잡성 선형 최소값
We also describe a time domain low complexity linear minimum mean square error (MMSE) equalization and successive interference cancellation (SIC) receiver for LDPC (low density parity check) coded CP-OTFS in this work. [1] However, the Bayes-optimal OAMP/VAMP requires high-complexity linear minimum mean square error estimator. [2] In this work, we investigate a low complexity linear minimum mean square error receiver which exploits sparsity and quasi-banded structure of matrices involved in the demodulation process which results in a log-linear order of complexity without any performance degradation of BER. [3]우리는 또한 이 작업에서 LDPC(저밀도 패리티 검사) 코딩된 CP-OTFS를 위한 시간 영역 저복잡도 선형 최소 평균 제곱 오차(MMSE) 등화 및 연속 간섭 제거(SIC) 수신기에 대해 설명합니다. [1] 그러나 Bayes-optimal OAMP/VAMP는 복잡한 선형 최소 평균 제곱 오차 추정기가 필요합니다. [2] nan [3]
Bussgang Linear Minimum
The simulation results show that the proposed scheme outperforms least squares (LS) and Bussgang linear minimum mean squared error (BLMMSE) channel estimators in the whole signal-to-noise-ratio (SNR) region. [1] In this paper, we study the problem of one-bit quantizer design when a Bussgang linear minimum mean square error (BLMMSE) estimator is used for channel estimation. [2]시뮬레이션 결과는 제안된 방식이 전체 신호 대 잡음비(SNR) 영역에서 최소 자승(LS) 및 Bussgang 선형 최소 평균 제곱 오차(BLMMSE) 채널 추정기보다 성능이 우수함을 보여줍니다. [1] 본 논문에서는 채널 추정을 위해 Bussgang 선형 최소 평균 제곱 오차(BLMMSE) 추정기를 사용할 때 1비트 양자화기 설계의 문제를 연구합니다. [2]
Conventional Linear Minimum
In contrast to existing work, we propose an machine learning (ML)-enhanced MU-MIMO receiver that builds on top of a conventional linear minimum mean squared error (LMMSE) architecture. [1] However, under uplink-heavy traffic, the conventional linear minimum mean-square-error (LMMSE)-based detectors suffer significant performance loss and thus cannot be applied. [2]nan [1] 그러나 상향링크 트래픽이 많은 경우 기존 LMMSE(Linear Minimum Mean-Square Error) 기반 검출기는 성능 손실이 커서 적용할 수 없었다. [2]
Novel Linear Minimum
We derive a novel linear minimum mean square error (LMMSE) channel estimator with correlated Rician fading channel, incorporating dynamic resolution ADCs/DACs and RF impairments. [1] The proposed method employs a novel linear minimum mean-square error (LMMSE) filtering mechanism in the frequency domain to reduce the noise in the LS CFR estimates and then, depending on the DMRS density, uses particularly designed LMMSE filtering or polynomial interpolation in the time domain to obtain the desired CFR estimates for the data subcarriers. [2]Employ Linear Minimum
The receiver is assumed to operate in two sequential stages that employ Linear Minimum Mean Squared Error (LMMSE) receivers. [1] For small numbers of observations, both the eavesdropper and the receiver are modeled to employ linear minimum mean-squared error (LMMSE) estimators, and for large numbers of observations, the expectation of the conditional Cramér-Rao bound (ECRB) metric is employed for both the receiver and the eavesdropper. [2]Unbiased Linear Minimum
According to the minimax robust estimation principle, and the unbiased linear minimum variance (ULMV) optimal estimation rule, based on the worst-case conservative system with the conservative upper bounds of noise variances, two robust Kalman state smoothing algorithms are presented by the augmented and non-augmented state approaches, respectively. [1] According to the unbiased linear minimum variance (ULMV) optimal estimation rule, the robust time-varying Kalman estimators are presented. [2]최소값 강건 추정 원리와 ULMV(편향되지 않은 선형 최소 분산) 최적 추정 규칙에 따라 노이즈 분산의 보존적 상한을 갖는 최악의 보존 시스템을 기반으로 하는 두 개의 강건한 칼만 상태 평활 알고리즘이 증강 및 비증강 상태 접근 방식. [1] nan [2]
Traditional Linear Minimum
Unlike the Gaussian approximate process of the input symbols of the traditional linear minimum mean square error equalization, the discrete independent random variable form of the input symbols of the JMTE algorithm is retained to avoid input information loss. [1] Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%, compared with the traditional linear minimum mean square error (LMMSE) algorithm, and can approach to the bit error rate (BER) performance bound of perfectly known channel state information within 0. [2]Local Linear Minimum 로컬 선형 최소값
In the basic estimation process, an NSCT shrinkage method that is suitable for Poisson-Gaussian noise characteristics is developed by optimizing the local linear minimum mean square error estimator in the NSCT domain. [1] local linear minimum mean-squared error correction, followed by a variance stabilizing transform, and finally adaptive bilateral filtering. [2]기본 추정 과정에서 NSCT 영역에서 국소 선형 최소 평균 제곱 오차 추정기를 최적화하여 Poisson-Gaussian 노이즈 특성에 적합한 NSCT 수축 방법을 개발합니다. [1] 로컬 선형 최소 평균 제곱 오류 수정, 분산 안정화 변환 및 마지막으로 적응형 양방향 필터링이 뒤따릅니다. [2]
linear minimum mean 선형 최소 평균
In this brief, we investigate the widely linear minimum mean square error (WLMMSE) successive interference cancellation (SIC) detector in multiple-input-multiple-output systems with rectilinear or quasi-rectilinear transmit signals. [1] In particular, we apply the linear minimum mean square error technique to estimate the time-varying channel gain and adopt extended Kalman filtering to track the time-varying phase noise. [2] Closed-form linear minimum mean square error (LMMSE) channel estimation and achievable spectral efficiency (SE) expressions with maximal ratio combining (MRC) receiver filters are derived. [3] Assuming linear minimum mean squared error (LMMSE) multiuser detection, we provide an analytical framework that can be used to specify the required power spectral density (PSD) mask for phase noise for a target system performance. [4] Specifically, we first introduce a quantization-aware technique for channel estimation based on linear minimum mean-square error (LMMSE) theory. [5] In particular, our algorithm provides a significant improvement over the practical least squares (LS) estimation method and provides performance that approaches that of the ideal linear minimum mean square error (LMMSE) estimation with perfect knowledge of channel statistics. [6] We firstly investigate widely-linear minimum mean square error (WL-MMSE) estimation for the effective channel which is the product of the IQI coefficients and the wireless channel. [7] Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). [8] We show that this system outperforms the traditional 2-D linear minimum mean squared error (2-D-LMMSE) equalizer. [9] We apply SPADE to beamspace linear minimum mean square error (LMMSE) spatial equalization in all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-output (MU-MIMO) systems. [10] A multi-linear minimum mean square error (MMSE) receiver using the tensor framework is also presented. [11] Linear minimum mean square error (LMMSE) receivers are often applied in practical communication scenarios for single-input-multiple-output (SIMO) systems owing to their low computational complexity and competitive performance. [12] The performance of the LMMSE (Linear Minimum Mean Square Error) algorithm is equivalent to the proposed algorithm also in terms of bit error rate. [13] The proposed NN architecture uses fully connected layers for frequency-aware pilot design, and outperforms linear minimum mean square error (LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices utilizing convolutional NN layers. [14] The Monte Carlo simulations are used to corroborate the theoretical results, which demonstrate that the proposed PC estimators are robust to the uncertainties compared to the nominal linear minimum mean squared error (LMMSE) estimator and the nominal best linear unbiased estimator (BLUE), and are less conservative compared to the traditional minimax robust estimator. [15] In this study we propose a novel correction scheme that filters Magnetic Resonance Images data, by using a modified Linear Minimum Mean Square Error (LMMSE) estimator which takes into account the joint information of the local features. [16] We derive closed form expression for the sum rate considering the Rician channel model, linear minimum mean squared error (LMMSE) is considered for uplink channel estimation, while maximum ratio transmission (MRT) is considered for the downlink. [17] By utilizing dynamic programming and linear minimum mean square biased estimate (LMMSUE), we propose a new type of online state-feedback control policy and characterize the behavior of regret in a finite-time regime. [18] Unlike the Gaussian approximate process of the input symbols of the traditional linear minimum mean square error equalization, the discrete independent random variable form of the input symbols of the JMTE algorithm is retained to avoid input information loss. [19] We propose two turbo detector algorithms: orthogonal approximate message passing with linear minimum mean squared error (OAMP-LMMSE) and Gaussian approximate message passing with expectation propagation (GAMP-EP). [20] The linear minimum mean square error (LMMSE) channel estimation yields the optimal performance in terms of the mean square error (MSE). [21] For sensing matrices with low-to-moderate condition numbers, CAMP can achieve the same performance as high-complexity orthogonal/vector AMP that requires the linear minimum mean-square error (LMMSE) filter instead of the MF. [22] First, we describe the linear minimum mean-squared error (LMMSE) estimator and its associated mean-squared error (MSE) for the general mixed-resolution model. [23] We also describe a time domain low complexity linear minimum mean square error (MMSE) equalization and successive interference cancellation (SIC) receiver for LDPC (low density parity check) coded CP-OTFS in this work. [24] Moreover, we unveil and prove that conventional elementary signal estimator (ESE) and linear minimum mean square error (LMMSE) receivers are special cases of EP and VEP, respectively, thus bridging the gap between classic linear receivers and message passing based nonlinear receivers. [25] The BS uses either least squares (LS) or linear minimum mean square error (LMMSE) estimators for channel estimation and utilizes either maximum-ratio-combining (MRC) or zero-forcing (ZF) decoding vectors. [26] In this study, we first derive linear minimum mean-square-error channel estimator for cyclic-prefix (CP) free single carrier MIMO (SC-MIMO) under frequency-selective channel. [27] Simulation results show that the proposed art significantly outperforms both classic receivers, such as the linear minimum mean square error (LMMSE), and recent CS-based state-of-the-art (SotA) alternatives, such as the sum-of-absolute-values (SOAV) and the sum of complex sparse regularizers (SCSR) detectors. [28] Specifically, we first introduce a technique based on linear minimum mean-square to perform channel estimation. [29] Then a linear minimum mean square error estimator is constructed using the nonlinear uncorrelated MT, and a Bayesian filter under Gaussian assumptions is derived. [30] We develop a linear minimum mean squared error (LMMSE) channel estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. [31] Numerical tests show that the DCCN receiver can outperform the legacy channel estimators based on ideal and approximate linear minimum mean square error (LMMSE) estimation and a conventional CP-enhanced technique in Rayleigh fading channels with various delay spreads and mobility. [32] We consider Rician fading and maximum ratio processing based on either linear minimum mean-squared error (LMMSE) or least-squares (LS) channel estimation. [33] Then, we develop an approximation for the spectral efficiency of various detectors such as maximum-ratio combining (MRC), zero-forcing (ZF), and linear minimum mean square error (LMMSE). [34] This article studies the distributed linear minimum mean square error (LMMSE) estimation problem for large-scale systems with local information (LSLI). [35] It comprises of three modules: 1) Dynamic estimation of the temporal response functions (TRF) in every trial using a sequential linear minimum mean squared error (LMMSE) estimator, 2) Extract the N1-P2 peak of the estimated TRF that serves as a marker related to the attentional state and 3) Obtain a probabilistic measure of the attentional state using a support vector machine followed by a logistic regression. [36] We introduce a joint weighted Neumann series (WNS) and Gauss–Seidel (GS) approach to implement an approximated linear minimum mean-squared error (LMMSE) detector for uplink massive multiple-input multiple-output (M-MIMO) systems. [37] Then a linear minimum mean squared error (MMSE) method is applied with these taps to estimate residual CE error value for each unique scenario, assuming Gaussian distribution of tap amplitudes and antenna noise. [38] We use the estimated covariance matrix as a plug-in to the linear minimum mean square estimator to obtain the channel estimate. [39] In this study, we propose a low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator for such low-resolution MIMO receivers. [40] Then, the ML-based channel predictor using the linear minimum mean square error (LMMSE)-based noise pre-processed data is developed. [41] The signal is modulated on the scatter coefficient of a single eigenvalue and linear minimum mean square error (LMMSE) estimator is used to reduce the noise. [42] This paper proposed a system based on the special multiplexing (SM) technique and linear minimum mean square error (LMMSE) detection method with the assistance of the hamming code as well as the interleaving techniques for a better enhanced performance of an audio transmission. [43] In addition to objective quality assessments, the subjective evaluations carried out by radiologist and neurologists show the relatively better visual quality of the proposed method compared to the methods such as linear minimum mean square error (LMMSE) and bilateral filtering (BF). [44] We consider linear minimum mean square error (LMMSE) reception for a multiuser MIMO uplink, and provide performance guarantees based on two key concepts: (a) summarization of the impact of per-antenna nonlinearities via a quantity that we term the “intrinsic SNR”, (b) using linear MMSE performance in an ideal system without nonlinearities to bound that in our non-ideal system. [45] Furthermore, we propose novel least squares (LS) and linear minimum mean square error (LMMSE) channel estimators by considering the energy concentration and spectral compaction properties of DCT-I for the uplink NB-IoT system. [46] The linear minimum mean square error (LMMSE) scheme is very effective in estimating the channel but introduces massive complexity because of having complex matrix inversion. [47] A linear Minimum Mean Squared Error (MMSE) based estimator is then employed to position the flying object passively. [48] The analytical results of the linear minimum mean square error (MMSE) channel estimation show that there is nonzero floor on the estimation error with respect to the RF impairments, ADC/DAC precision and the pilot power of the eavesdropper which is different from the conventional case with perfect transceiver. [49] We propose iterative linear minimum mean-square error (MMSE) precoding along with optimal and uniform power allocation. [50]이 요약에서는 직선 또는 준직선 전송 신호가 있는 다중 입력 다중 출력 시스템에서 광범위하게 선형인 WLMMSE(최소 평균 제곱 오차) SIC(연속 간섭 제거) 검출기를 조사합니다. [1] 특히, 선형 최소 평균 제곱 오차 기법을 적용하여 시변 채널 이득을 추정하고 확장 칼만 필터링을 채택하여 시변 위상 잡음을 추적합니다. [2] nan [3] 선형 최소 평균 제곱 오차(LMMSE) 다중 사용자 감지를 가정하면 대상 시스템 성능에 대한 위상 노이즈에 필요한 전력 스펙트럼 밀도(PSD) 마스크를 지정하는 데 사용할 수 있는 분석 프레임워크를 제공합니다. [4] nan [5] nan [6] nan [7] 다중 입력 다중 출력(MIMO) 무선 센서 네트워크(WSN)에서 베이지안 학습(BL) 기반 희소 매개변수 벡터 추정을 위한 최적의 선형 최소 평균 제곱 오차(MMSE) 송수신기 설계 기술이 제안됩니다. [8] 우리는 이 시스템이 기존의 2D 선형 최소 평균 제곱 오차(2-D-LMMSE) 등화기를 능가한다는 것을 보여줍니다. [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15] nan [16] nan [17] 동적 계획법과 선형 최소 평균 제곱 편향 추정(LMMSUE)을 활용하여 새로운 유형의 온라인 상태 피드백 제어 정책을 제안하고 유한 시간 체제에서 후회의 행동을 특성화합니다. [18] nan [19] nan [20] nan [21] nan [22] nan [23] 우리는 또한 이 작업에서 LDPC(저밀도 패리티 검사) 코딩된 CP-OTFS를 위한 시간 영역 저복잡도 선형 최소 평균 제곱 오차(MMSE) 등화 및 연속 간섭 제거(SIC) 수신기에 대해 설명합니다. [24] nan [25] nan [26] nan [27] nan [28] 구체적으로, 우리는 먼저 채널 추정을 수행하기 위해 선형 최소 평균 제곱을 기반으로 하는 기술을 소개합니다. [29] nan [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [37] nan [38] 추정된 공분산 행렬을 선형 최소 평균 제곱 추정기에 대한 플러그인으로 사용하여 채널 추정치를 얻습니다. [39] nan [40] nan [41] nan [42] nan [43] nan [44] nan [45] nan [46] nan [47] nan [48] nan [49] 우리는 최적의 균일한 전력 할당과 함께 반복적인 선형 최소 평균 제곱 오차(MMSE) 프리코딩을 제안합니다. [50]
linear minimum variance 선형 최소 편차
Using a canonical form of descriptor and measurement differencing, the linear optimal filter in the linear minimum variance (LMV) sense is designed based on an innovation analysis method. [1] According to the minimax robust estimation principle, and the unbiased linear minimum variance (ULMV) optimal estimation rule, based on the worst-case conservative system with the conservative upper bounds of noise variances, two robust Kalman state smoothing algorithms are presented by the augmented and non-augmented state approaches, respectively. [2] Furthermore, under the linear minimum variance optimal fusion criterion, an optimal weighted fusion descriptor incremental Kalman estimator is proposed. [3] For the considered system with the complex correlated noises, using the measurement differencing method and an innovation analysis approach, the linear optimal estimators including filter, predictor and smoother in the linear minimum variance (LMV) sense is proposed. [4] Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. [5] Based on this model, a recursive distributed Kalman fusion estimator (DKFE) is derived by optimal weighted fusion criterion in the linear minimum variance sense. [6] Using an innovation analysis approach, a recursive nonaugmented optimal estimator is proposed in the linear minimum variance (LMV) sense. [7] Then the linear optimal filter with linear minimum variance meaning is developed via the projection theory and the innovation analysis technique. [8] In order to solve this problem, the multi-sensor optimal information fusion under the criterion of linear minimum variance is utilized to correct the estimated probability in the SIMMUKF algorithm. [9] Under the framework of distributed fusion, we utilize the linear minimum variance fusion criterion to minimize the trace of fusion error covariance. [10] Moreover, linear minimum variance criterion is utilised to fuse local estimates together in distributed fusion architectures of UWSNs. [11] Then, based on the linear minimum variance criterion, the corresponding globally optimal event-triggered fusion estimators are proposed by a matrix-weighted combination of all available local estimates from sensor subsystems. [12] Here, we propose an ensemble method by applying a linear minimum variance estimation (LMVE) between multimodel ensemble (MME) simulations and measurements to derive a more realistic distribution of atmospheric pollutants. [13] This paper is concerned with globally optimal sequential and distributed fusion estimation algorithms in the linear minimum variance (LMV) sense for multi-sensor systems with cross-correlated noises, where the measurement noises from different sensors are cross-correlated with each other at the same time step and correlated with the system noise at the previous time step. [14] According to the unbiased linear minimum variance (ULMV) optimal estimation rule, the robust time-varying Kalman estimators are presented. [15] Applying the projective theory, the incremental Kalman estimation algorithms including filter, predictor and smoother are presented based on the optimal estimation criterion of linear minimum variance. [16] The distributed optimal (linear minimum variance) fusion filtering problem for multi-sensor discrete-time stochastic systems is considered. [17] Interestingly, the linear minimum variance distortionless response (LMVDR) filter, when it exists, shares exactly the same recursion as the KF, except for the initialization. [18] A locally optimal distributed estimator is designed in the linear minimum variance sense, and a stability condition is derived such that the mean square error of the distributed estimator is bounded. [19]표준 형식의 디스크립터 및 측정 차분을 사용하여 선형 최소 분산(LMV) 의미의 선형 최적 필터는 혁신 분석 방법을 기반으로 설계되었습니다. [1] 최소값 강건 추정 원리와 ULMV(편향되지 않은 선형 최소 분산) 최적 추정 규칙에 따라 노이즈 분산의 보존적 상한을 갖는 최악의 보존 시스템을 기반으로 하는 두 개의 강건한 칼만 상태 평활 알고리즘이 증강 및 비증강 상태 접근 방식. [2] 또한, 선형 최소 분산 최적 융합 기준에서 최적 가중 융합 디스크립터 증분 칼만 추정기를 제안합니다. [3] 복잡한 상관 잡음을 가진 고려 시스템에 대해 측정 차분 방법과 혁신 분석 접근 방식을 사용하여 선형 최소 분산(LMV) 의미에서 필터, 예측 및 평활기를 포함하는 선형 최적 추정기가 제안됩니다. [4] 선형 최소 분산 기준을 기반으로 하는 이 다중 센서 정보 융합 방법은 2계층 아키텍처를 갖습니다. 첫 번째 계층에서 시변 잡음 공분산에 대한 새로운 적응형 UKF 체계가 개발되고 개선을 위한 로컬 필터 역할을 합니다. 상태 추정과 분리된 중복 측정 잡음 공분산 추정을 적용하여 추정된 측정 잡음 공분산과 함께 적응성; 두 번째 계층은 최적 행렬 가중치를 계산하고 최종 최적 상태 추정을 제공하는 융합 구조입니다. [5] 이 모델을 기반으로 선형 최소 분산 의미에서 최적 가중 융합 기준에 의해 재귀적 분산 칼만 융합 추정기(DKFE)가 도출됩니다. [6] 혁신 분석 접근 방식을 사용하여 선형 최소 분산(LMV) 의미에서 재귀적 비증강 최적 추정기가 제안됩니다. [7] 그런 다음 투영 이론과 혁신 분석 기법을 통해 선형 최소 분산 의미를 갖는 선형 최적 필터를 개발합니다. [8] 이 문제를 해결하기 위해 SIMMUKF 알고리즘에서 추정된 확률을 보정하기 위해 선형 최소 분산 기준에 따른 다중 센서 최적 정보 융합을 활용한다. [9] 분산 융합의 프레임워크에서 선형 최소 분산 융합 기준을 사용하여 융합 오류 공분산의 흔적을 최소화합니다. [10] 또한 선형 최소 분산 기준은 UWSN의 분산 융합 아키텍처에서 로컬 추정치를 함께 융합하는 데 사용됩니다. [11] 그런 다음 선형 최소 분산 기준에 따라 해당 전역 최적 이벤트 트리거 융합 추정기가 센서 하위 시스템에서 사용 가능한 모든 로컬 추정값의 행렬 가중 조합으로 제안됩니다. [12] 여기서 우리는 보다 현실적인 대기오염물질 분포를 도출하기 위해 다중모델 앙상블(MME) 시뮬레이션과 측정 사이에 선형최소분산추정(LMVE)을 적용하여 앙상블 방법을 제안한다. [13] 이 논문은 교차 상관 노이즈가 있는 다중 센서 시스템에 대한 선형 최소 분산(LMV) 의미의 전역 최적 순차 및 분산 융합 추정 알고리즘에 관한 것입니다. 이전 시간 단계의 시스템 노이즈와 상관 관계가 있습니다. [14] nan [15] nan [16] nan [17] nan [18] nan [19]
linear minimum error
The linear minimum error probability (MEP) detector with a given length of pilots (LoP) is derived. [1] The channel estimation algorithms based on least squares (LS), least mean square error (MMSE) and linear minimum error (LMMSE) are studied and simulated respectively. [2]주어진 길이의 파일럿(LoP)을 갖는 선형 최소 오차 확률(MEP) 검출기가 유도됩니다. [1] 최소 자승(LS), 최소 평균 자승 오차(MMSE) 및 선형 최소 오차(LMMSE)를 기반으로 한 채널 추정 알고리즘을 각각 연구하고 시뮬레이션합니다. [2]