Channel estimation

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Variational channel estimation with tempering: an artificial intelligence algorithm for wireless intelligent networks

Variational channel estimation with tempering: an artificial intelligence algorithm for wireless intelligent networks

To overcome the problems with the existing algorithms in channel state inference in WSNs, such as high complexity, poor generalization, impracticability and so on, we develop a new channel estimation method with variational tempering for MIMO-OFDM scenario. A ground truth information extraction algorithm based on variational tempering is proposed and implemented. Our CEVTI provides insights that account for multiple factors in inferring truth and can generalize to higher dimensions and finds better local optimum. As a variation of SVI, CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of CDMA and uplink massive MIMO easily. As can be seen in Section 5 , CEVTI can iteratively minimize the objective function with multiple dimensions. We demonstrate the performance of CEVTI through numerical simulation. The BER, convergence rate, and mutual information comparisons with the five existing CE algorithms show that CEVTI outperforms others under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters that are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees better local optimum. Therefore, this paper has contributed to the quest for developing efficient algorithms in artificial advanced sensor networks. Possible future research directions include investigation of how the graph structure can impact the performance of CEVTI, and constructing inference algorithms that can suit more complex situations.
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Effect of residual channel estimation errors in random access methods for satellite communications

Effect of residual channel estimation errors in random access methods for satellite communications

The problem to be addressed in this paper is the impact of residual channel estimation errors on recent TDMA based RA methods. The main issue is to be able to estimate the channel parameters in the case of multiple superimposed signals and to achieve performance close to the perfect knowledge case. This challenge has already been addressed in part in the existing literature. In [5] a method based on the Expectation- Maximization (EM) algorithm is presented to estimate channel parameters simultaneously. In [6], another approach uses the autocorrelation to derive channel amplitude and frequency offsets from packets that did not experience collision. In [7], channel estimation using EM is evaluated for a network coded diversity protocol (NDCP). We have also presented a first con- tribution of our work in [8], where we have used an EM based channel estimation method and evaluated experimentally the effect of imperfect interference cancellation on the decoding of the remaining packet.
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Blind Channel Estimation for STBC Systems Using Higher-Order Statistics

Blind Channel Estimation for STBC Systems Using Higher-Order Statistics

transmitter employs Alamouti Coding. Regarding the proposed methods, Table I shows that the set of ambiguity matrices after channel estimation is Θ = {M 1 (𝜃), M 2 (𝜃)}. Figure 5 displays the NMSE versus SNR for a receiver composed of 𝑛 𝑟 = 3 antennae. Without multistart initialization, the geodesic SD clearly outperforms the classical SD since the latter exhibits an error floor at SNR greater than 4dB. This error floor is due to the fact that the Euclidean SD can lead to undesired suboptimal solutions even at high SNR [47], [48]. It should be observed that the multistart initialization strategy removes the error floor and improves the NMSE performances of the two proposed algorithms. Figure 6 compares the SER with the one obtained with a coherent ML receiver. As previously discussed, without multistart initialization, the performances of the Euclidean SD lead to an error floor at SNR greater than 4dB. However, it should be observed that algorithms 1 and 2 achieve near-optimal performance when a multistart ini- tizalization is used. A comparison of the average computation times is shown in Table III. It should be noted that classical SD is less computationally demanding than the geodesic SD at low-SNR, but this trend is reversed at high SNR.
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A Fast Adaptive Method for Subspace Based Blind Channel Estimation

A Fast Adaptive Method for Subspace Based Blind Channel Estimation

20500 Mondragon, Spain ABSTRACT In this paper, a new fast adaptive blind channel estimation method is proposed using the subspace information from the correlation matrix. The algorithm is fully adaptive in the sense that both the subspace information and the optimization which leads to the channel estimation are computed adaptively. It is based on the re- cently proposed YAST subspace tracker which has been shown to outperform other methods both in terms of speed of convergence and computational complexity. A discussion on the convergence properties of the proposed algorithm is presented. We also propose a hybrid method which makes use of the YAST subspace tracker for initial fast convergence and the subspace information is then updated using the numerically stable OPAST subspace tracker.
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Nonlinear MIMO communication systems : channel estimation and information recovery using Volterra models

Nonlinear MIMO communication systems : channel estimation and information recovery using Volterra models

The main objective of this thesis is to propose techniques for channel estima- tion and information recovery in multiple-input-multiple-output (MIMO) Volterra communication systems. This kind of MIMO model is able of modeling nonlinear communication channels with multiple transmit and receive antennas, as well as multi-user channels with a single transmit antenna for each user and multiple re- ceive antennas. Channel estimation and equalization techniques are developed for three types of nonlinear MIMO communication systems: OFDM, ROF and Code division multiple access (CDMA)-ROF systems. According to the considered com- munication systems, different kinds of MIMO Volterra models are used. In the case of OFDM systems, we develop receivers that exploit the diversity provided by a proposed transmission scheme. In the case of time and space division multiple access (TDMA-SDMA) systems, a set of orthonormal polynomials is developed for increasing the convergence speed of a supervised adaptive MIMO Volterra es- timation algorithm. Moreover, in order to develop signal processing techniques for MIMO Volterra communication channels in a blind scenario, we make use of tensor decompositions. By exploiting the fact that Volterra models are linear with respect to their coefficients, blind estimation and equalization of MIMO Volterra channels are carried out by means of the Parallel Factor (PARAFAC) tensor decomposition, considering TDMA-SDMA and CDMA communication systems.
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Low-Complexity Approximations for LMMSE Channel Estimation in OFDM/OQAM

Low-Complexity Approximations for LMMSE Channel Estimation in OFDM/OQAM

Avenue de la Boulaie, 35576 Cesson - S´evign´e Cedex, France. Emails: yves.louet@centralesupelec.fr, and faouzi.bader@supelec.fr Abstract—In this paper, the authors describe and compare two low-complexity approximations of the linear minimum mean square error (LMMSE) channel estimation method for orthogo- nal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) systems. Simulations reveal that we are able by proposed approximations to reduce the complexity of the LMMSE estimator without degrading the overall BER system performance.

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Improved channel estimation for interference cancellation in random access methods for satellite communications

Improved channel estimation for interference cancellation in random access methods for satellite communications

Fig. 1: Packets transmission with collision on slot 1 II. S YSTEM M ODEL In order to illustrate the main issues raised by imperfect channel estimation in interference cancellation based RA meth- ods, we consider the following example (see Fig. 1). Each user (1 and 2) sends two replicas (a and b) of the same packet on two different time slots on the frame. We suppose that the receiver first detects packet 1b as it is a clean packet, decodes it correctly and removes its corresponding signal from Slot 4. Then, using the known decoded bits of packet 1b, the signal corresponding to packet 1a is reconstructed and suppressed from Slot 1. Thus, packet 2a becomes collision free, and has a bigger probability to be decoded successfully.
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Channel estimation strategy for LPWA transmission at low SNR: application to Turbo-FSK

Channel estimation strategy for LPWA transmission at low SNR: application to Turbo-FSK

linear N ⊥ -carrier FSK modulation [6]. Efficient digital imple- mentation can be realized by inverse Fast Fourier Transform (FFT) followed by a cyclic prefix insertion as for Orthogonal Frequency Division Multiplexing (Fig. 1). At the receiver, a soft FSK-detector estimates the probabilities of each possible Turbo-FSK codeword. These probabilities are then fed to the decoder, which uses them as channel observation, while output of the other decoders will be used as a priori information. A modified version of the algorithm proposed by Bahl, Cocke, Jelinek and Raviv (BCJR) [7] is used to decode the trellis, and derive the a posteriori probabilities of the information bits. The association of the encoding with a non-linear modulation (FSK) allows to operate at very low levels of SNR. It has been demonstrated that the choice of the PSK is the optimal modulation minimizing channel capacity gap to Shannon’s limit [8]. Practical implementations of the FSK-detector consist of a FFT combined with a frequency domain equalizer that relies on accurate channel estimation.
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Performance Analysis of Extended RASK under Imperfect Channel Estimation and Antenna Correlation

Performance Analysis of Extended RASK under Imperfect Channel Estimation and Antenna Correlation

and 4, σ H = 0 and 0.2. inaccuracies, and fixing the correlation factor to ρ = 0.1. First comparing the curves with N t = 8, it is verified that the larger the RA array, the stronger the degradation of the performance. Indeed, as the number of RAs increases, the amount of inter- antenna interference becomes higher at the receiver side and the defect of interference cancellation of the ZF scheme due the channel estimation errors becomes more significant. This fact corroborates the analysis made on Eq. (26). Reversely, analyzing the curves with N r = 2 but changing N t leads to the
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Channel estimation with a priori position for aeronautical communications via a satellite link

Channel estimation with a priori position for aeronautical communications via a satellite link

In this paper, we are interested in the estimation of the aeronautical multipath channel. Based on parametric models for the impulse response of the multipath channel, we propose simple but efficient channel estimation methods that benefit from a known position of the mobile user. The proposed meth- ods exploit both the particular form of the channel impulse response and a priori knowledge of some parameters (mainly delays) that can be inferred from geometrical considerations based on geolocation. The first estimation method is based on a parametric multipath channel model while the second method tries to exploit the relative sparsity of the channel impulse response. In both cases, this reduces the number of variables to be estimated and it provides better performance compared to a direct classical least-square estimation of the discrete equivalent channel impulse response.
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Turbo-FSK, a physical layer for LPWA: Synchronization and Channel estimation

Turbo-FSK, a physical layer for LPWA: Synchronization and Channel estimation

In terms of PER, performance is even closer when LS filtered and LMMSE are compared (Fig. 8). For these simulation scenarios, the pilot sequence is repeated 64 times and averaged to improve the performance of the estimator. Except the LS estimator that has a loss of 1.4 dB in comparison to the reference (perfect channel estimation), the other estimators have a very limited loss between 0.2 dB (LMMSE) and 0.3 dB (LMMSE with uniform assumption). Consequently, the LS filtered solution is chosen, as it is a good compromise between complexity and performance.
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EM Channel Estimation in a Low-cost UWB Receiver based on Energy Detection

EM Channel Estimation in a Low-cost UWB Receiver based on Energy Detection

Fig. 7. BER for different channel models using BICM(23,35) at rate 1/2 with K = 3 in non perfect CSI. VI. C The EM algorithm has been studied to estimate the channel parameters of an M-PPM UWB communication. The parameters are composed of the noise signal and energy coefficients corresponding to K inter-symbol interferences caused by high data-rate communica- tions in dispersive channels. The channel estimation is used iteratively and jointly with a probabilistic equalization and a channel decoder. At 100 Mbits/s the EM is capable of a good estimation of parameters
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Robust Relay Beamforming for MIMO Multi-Relay Networks with Imperfect Channel Estimation

Robust Relay Beamforming for MIMO Multi-Relay Networks with Imperfect Channel Estimation

and α RZF . The advantage of the proposed robust MMSE-RZF can be observed in Fig. 2. Note that MF, MF-RZF and ZF are all special cases of MMSE-RZF, which are not optimized with the system condition. The poor performance of ZF comes from the inverse Wishart distribution term in its power control factor at the relays, especially when M = N . We find that the ergodic capacities still satisfy the scaling law in [3], i.e., C = (M/2) log(K) + O(1) for large K with channel estimation errors, which is also consistent with the asymptotic capacities of robust MMSE-RZF. Fig. 3 compare the ergodic rate capacities versus the power of CSI error. we set K = 3 and PNR= 10dB, QNR= 10dB. MMSE-RZF outperforms others as the power of CSI error changes.
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Efficient Threshold based non-sample spaced sparse channel estimation in OFDM system

Efficient Threshold based non-sample spaced sparse channel estimation in OFDM system

Stopping criterion is essential for CS based non- sample spaced sparse channel estimation. Similar with the stopping criterion for the traditional LS or DFT based sparse channel estimation and sample spaced sparse channel estimation with CS, the channel statistics (power delay profile of the channel, channel sparsity et al), noise standard deviation (STD) or signal to noise ratio (SNR) can be employed as the basic parameters for obtaining effective stopping criteriaon[7-8].

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Sparse Preamble Design for Polarization Division Multiplexed CO-OFDM/OQAM Channel Estimation

Sparse Preamble Design for Polarization Division Multiplexed CO-OFDM/OQAM Channel Estimation

of a specific filter bank applied on the sub-carriers. As a consequence, there exists intrinsic imaginary interference (IMI) among the neighboring sub-carriers (frequency-domain) and symbols (time-domain). Being interfered by the IMI, the estimation and compensation of channel dispersions for CO- OFDM/OQAM systems becomes more complicated compared to the conventional CO-OFDM ones, especially when polar- ization division multiplexing (PDM) is taken into account. Therefore, the quest for an optimal channel estimation and equalization for PDM CO-OFDM/OQAM systems is crucial. Recent researches in channel dispersion compensation for CO-OFDM/OQAM mainly focus on the equalizer design while assuming that the channel transfer function is known [7], [8] or perfectly estimated [9]. In [10] and [11], the estimation of channel response by using preamble was investigated. Both methods focus on the preamble design to minimize the effect of IMI by allocating zeros around the target pilots and count on interpolation to find the channel response at the sub-carriers associated with the zero pilots. In contrast, there exists a channel estimation method, called Interference approximation method (IAM), that even exploits IMI as an advantage. Indeed, IAM is a well-known channel estimation method for OFDM/OQAM, which was originally proposed for radio communications in [12], [13]. This method was introduced for PDM CO-OFDM/OQAM in [14]–[16]. In fact, the shifted versions of the CO-OFDM/OQAM prototype filter in frequency and time domains can be viewed as a grid of filters for different frequency-time (FT) positions. To recover the symbol at a given FT position, the filter at that position must be slided and multiplied with all filters in the filter grid before summed up. Since the filters are designed to be orthogonal in real field only, the multiplication between filters in different FT locations result in a imaginary value called IMI coefficients. Fortunately, the pattern of IMI coefficients in the FT grid is unchanged for any reference FT position. The IAM method relies on this characteristic to design the preamble.
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Efficient time domain threshold for sparse channel estimation in OFDM system

Efficient time domain threshold for sparse channel estimation in OFDM system

A novel efficient time domain threshold based sparse channel estimation technique is proposed for orthogonal frequency division multiplexing (OFDM) systems. The proposed method aims to realize effective channel estimation without prior knowledge of channel statistics and noise standard deviation within a comparatively wide range of sparsity. Firstly, classical least squares (LS) method is used to get an initial channel impulse response (CIR) es- timate. Then, an effective threshold, estimated from the noise coefficients of the initial estimated CIR, is proposed. Finally, the obtained threshold is used to select the most significant taps. Theoretical analysis and simula- tion results show that the proposed method achieves better performance in both BER (bit error rate) and NMSE (normalized mean square error) than the compared methods, has good spectral efficiency and moderate compu- tational complexity.
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Channel estimation with a priori position for aeronautical communications via a satellite link

Channel estimation with a priori position for aeronautical communications via a satellite link

In this paper, we are interested in the estimation of the aeronautical multipath channel. Based on parametric models for the impulse response of the multipath channel, we propose simple but efficient channel estimation methods that benefit from a known position of the mobile user. The proposed meth- ods exploit both the particular form of the channel impulse response and a priori knowledge of some parameters (mainly delays) that can be inferred from geometrical considerations based on geolocation. The first estimation method is based on a parametric multipath channel model while the second method tries to exploit the relative sparsity of the channel impulse response. In both cases, this reduces the number of variables to be estimated and it provides better performance compared to a direct classical least-square estimation of the discrete equivalent channel impulse response.
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Improved channel estimation for interference cancellation in random access methods for satellite communications

Improved channel estimation for interference cancellation in random access methods for satellite communications

2a 2b Fig. 1: Packets transmission with collision on slot 1 II. S YSTEM M ODEL In order to illustrate the main issues raised by imperfect channel estimation in interference cancellation based RA meth- ods, we consider the following example (see Fig. 1). Each user (1 and 2) sends two replicas (a and b) of the same packet on two different time slots on the frame. We suppose that the receiver first detects packet 1b as it is a clean packet, decodes it correctly and removes its corresponding signal from Slot 4. Then, using the known decoded bits of packet 1b, the signal corresponding to packet 1a is reconstructed and suppressed from Slot 1. Thus, packet 2a becomes collision free, and has a bigger probability to be decoded successfully.
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Efficient compressed sensing based non-sample spaced sparse channel estimation in OFDM system

Efficient compressed sensing based non-sample spaced sparse channel estimation in OFDM system

τ ∈T |< f τ , r l−1 >| is not sufficient for accurate delay tracking and channel estimation with high precision. In other words, the delay points within a delay sub- set where the corresponding bases have high coherence with the residual vector, should be considered. With these delay points, the reference delay grid (RDG) guided RNM method is proposed in this paper to effectively fight against the non- uniform pilot arrangement and realize the near optimal delay searching of the l th channel tap, which will be discussed after the DT method in this section.

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Doubly-Selective Channel Estimation for Continuous Phase Modulation

Doubly-Selective Channel Estimation for Continuous Phase Modulation

A common BEM is the (O)CE-BEM only based on complex exponentials. This model does not require any knowledge of the channel statistics. In this case, the BEM channel estimation suffers from the unknown sparsity of the channel. Indeed, we try to estimate a null path by a sum of weighted complex exponentials. Even the LS estimation with positioning a priori exhibits a large BEM modeling error as shown in Fig. 4. We can also observe that for the same number of basis functions, the KL-BEM outperforms the chosen OCE-BEM, which can be explained by the optimally of the KL-BEM. However, we
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