Channel estimation with array processing for the uplink of UMTS-TDD

In cellular communications, recently Multi In Multi Out (MIMO) and Massive MIMO research is getting attention for the need of high data rates in Long Term Evolution Advanced(LTE-A) and 5G Communications. In MIMO baseband signal processing at physical layer, both the channel estimation and the detection algorithms play a crucial role. In this paper it is discussed the estimation algorithms least square (LS) and minimum mean square error (MMSE) and the channel detection algorithms Zero Forcing (ZF) and MMSE. Currently none of the channel estimation algorithms of LTE-A offers twin advantages of low battery consumption and very low latency, which is a key requirement of 5G. It is expected the massive MIMO with 128 or more antennas will be a norm at 5G base stations. To achieve the ultra-low latency, the matrix computations for massive MIMO are the very big bottleneck in realizing the channel estimation and massive MIMO detection algorithms. For the optimization of the 5G Massive MIMO channel estimation and detection algorithms, the prerequisite is massive complex matrix inversion speed. In this paper, a parallel processing based coding scheme is proposed by using Gauss-Jordan elimination kernel algorithm on a single instruction multiple data (SIMD) stream vector processor to realize a complex matrix inverse with optimum speed which is the need of 5G channel estimation and detection.

After a few decades’ evolution of wireless communication systems, to ensure reliable highspeed communication over unreliable wireless channels is still one of the major challenges facing researchers and engineers. The use of multiple antennas at transmitter and receiver, known as multiple-input multiple-output (MIMO) communications, is one promising technology delivering desired wireless services. The main goal of this thesis is to study two important issues in wireless MIMO communication systems: receiver design for coded MIMO systems, and diversity-multiplexing tradeoff analysis in general fading channels. In the first part of this thesis, we decompose the receiver design problem into two subproblems: MIMO channel estimation and MIMO detection. For the MIMO channel estimation, we develop an expectation-maximization (EM) based semi-blind channel and noise covariance matrix estimation algorithm for space-time coding systems under spatially correlated noise. By incorporating the proposed channel estimator into the iterative receiver structure, both the channel estimation and the error-control decoding are improved significantly. We also derive the modified Cramer-Rao bounds (MCRB) for the unknown parameters as the channel estimation performance metric, and demonstrate that the proposed channel estimation algorithm can achieve the MCRB after several iterations. For the MIMO detection, we propose a novel lowcomplexity MIMO detection algorithm, which has only cubic order computational complexity, but with near-optimal performance. For a 4×4 turbo-coded system, we show that the proposed detector had the same performance as the maximum a posteriori (MAP) detector for BPSK modulation, and 0.1 dB advantage over the approximated MAP detector (list sphere decoding algorithm) for 16-QAM modulation at BER = 10−4. In the second part of this thesis, we derive the optimal diversity-multiplexing tradeoff for

Conventional time-division duplex (TDD) orthogonal frequency division multiplexing (TDD-OFDM) system is vulnerable to the pilot spoofing attack as it mainly relies on employing the deterministic pilot to capture the channel state information (CSI). To solve the abovementioned issue, we in this paper propose an independent component analysis (ICA) based channel identification and estimation (ICA-CIE) mechanism which uses randomized pilot to paralyze the pilot spoofing attack. Specifically, we first convert the pilot spoofing attack to the pilot jamming attack by exploiting pilot randomization. Then, an eigenstructure-based detector (EID) is proposed to realize efficient pilot jamming attack detection. In particular, on one hand, Least Square (LS) method is adopted if no jamming attack is detected. On the other hand, if jamming attack is detected, we further design a channelseparation oriented joint approximate diagonalization of eigenmatrices (CS-JADE) algorithm for channel estimation and identification. Therein, an optimization problem is first formulated to optimize the fourth-order statistical information of the observation data, after which a pilot signal reconstruction scheme is further developed to help identify true sub-channels from the optimization output. Simulation results demonstrate that our proposed ICA-CIE mechanism can achieve highly-accurate channel estimation for the legitimate transceiver pair only by using 6 OFDM symbols within the coherence time. Moreover, we also observe that the estimation accuracy of our proposed mechanism is immune to Eve's transmit power, which further verifies superiority of our proposed mechanism.

In recent years, an Ultra Wideband (UWB) system has attracted much attention in short range high speed wireless communications. Meanwhile, the single-carrier transmission with frequency domain equalization (SC-FDE) is also attracting much attention. It is reported that the SC-FDE system obtains an excellent performance as well as orthogonal frequency division multiplexing (OFDM), because the SC-FDE system has the advantage of frequency diversity gain even in strong frequency selective channels. However, in the SC-FDE system, cyclic extension reduces the bandwidth efficiency. To improve the bandwidth efficiency, we proposed UWB-IR and DS-UWB with Frequency-Domain Equalization (FDE) and Cyclic Prefix (CP) reconstruction, and showed that when the channel estimation is ideal, the proposed system achieves the good performance. Both FDE and CP reconstruction use channel gains and thus the performance is expected to depend largely on the channel estimation accuracy. In this paper, we evaluate the effect of imperfect channel estimation on the performance of UWB-IR with MMSE-FDE and CP Reconstruction. We show that CP reconstruction is effective for UWB-IR with MMSE-FDE in the IEEE UWB multipath channels, CM3, CM4.

Channel estimation techniques applied to cognitive radio networks (CRN) are analyzed for simultaneously primary and secondary channel estimations operating in underlay cognitive radio networks (uCRN). A complete base-band transmission including pilot sequence transmission, channel matrix estimation and optimal precoder matrix generation based on imperfect channel estimation are described. Also, the effect of imperfect channel estimation has been studied to provide means of developing techniques to overcome problems while enhancing the MIMO communication performance.

In this paper, the performances of DS-UWB and TH-UWB for a single-user link are compared for a data rate of 100 Mbps using the newly proposed, realistic UWB channel model from IEEE P802.15. The performance of suboptimal, fractionally spaced (FS), coherent Rake receivers with a pulse-matched filter is numerically evaluated with two channel estimation algorithms. DS-UWB and TH-UWB with antipodal modulation perform basically the same. The FS-Rake receiver performs much better than chip- and symbol-spaced Rake receivers. The best evaluated channel estimation algorithm gives only a fractional loss compared with the perfectly estimated channel. However, since TH-UWB is more difficult to synchronize, the conclusion is that DS-UWB is more suitable for high-speed indoor links.

B.Steiner channel estimation algorithm is commonly used in TD-SCDMA system, The algorithm is simple. However, due to the impact of background noise Makes the channel impulse response of the amplitude and delay relative to the true value have the error, reducing the system performance. Based on the B.Steiner channel estimation algorithm a channel estimation algorithm for using threshold value processing the channel impulse response has been proposed. The algorithm can effectively reduce the doppler frequency shift and noise. This article discusses the channel estimation based training sequence In the TD-SCDMA system. The mathematical model of the channel estimation is given, And describes the composition of the training sequence, the simulation model of the Channel estimation is given. using joint detection technology to simulation the channel estimation method. Experimental results showed that, based on training sequence channel estimation algorithm is close to the true value, Can significantly improve system performance. And reached the standard call.

In this paper, the channel estimation in uplink multiple access relay networks (MARNs) with analog network coding protocol has been researched. We apply the superimposed training (ST) scheme where each relay puts a separate training sequence on the top of the received one before forwarding to destination, and design a maximum likelihood based channel estimation algorithm for the composite source-relay-destination and individual relay-destination links. The optimal training sequences as well as the superimposed training power are also derived in closed forms. To make our study more complete, the channel estimation in the time-selective fading environment is further considered, and a correlation-based channel estimation (CBCE) algorithm is developed by taking advantage of time-domain channel autocorrelation nature. Simulation results show that the presented ST scheme can effectively improve the performance of multi-user detection in MARNs, and the proposed CBCE algorithm significantly outperforms the existing channel estimation methods.

We study the effect of imperfect channel estimation (ICE) on the error probability performance of M-level quadrature amplitude modulation ( M-QAM) with maximal-ratio combining and equal-gain combining diversity formats in Nakagami fading channels. We provide a novel formulation of the bit-error rate (BER) of M-QAM with ICE in terms of the signal constellation-dependent effective signal-to-noise ratios (SNRs) or amplitudes, which allows us to derive the general, accurate, and easy-to-evaluate BER formulas for square and rectangular diversity M-QAM with channel estimation errors. Our result shows that the performance loss caused by ICE may be manifested by the signal decision space distortion and a scaling of the effective SNR. Using our analytical result, we evaluate the performance of M-QAM with pilot-symbol assisted modulation and present some insightful findings

Orthogonal frequency division multiplexing (OFDM) technology has been increasingly applied to power line carrier communication (PLC). Discrete Fourier Transformation (DFT)‐based channel estimation algorithm is suitable for OFDM‐based PLC due to its low complexity. In view of the problem that the traditional DFT channel estimation does not consider the influence of noise inside cyclic prefix (CP), an improved DFT channel estimation based on signal‐to‐noise ratio (SNR) estimation is proposed. First, least‐square (LS) algorithm is performed and the frequency domain channel estimation is converted to the time domain through inverse DFT, and the average SNR of the system is estimated according to the pilot sequence. Second, the substitute SNR for each sample point inside CP is defined and used to filter impulse noise inside CP. Third, the average SNR is converted into the threshold of the useful signal energy inside CP, and the widespread background noise inside CP is filtered. The simulation results show that the proposed algorithm can obtain more accurate channel estimation than other DFT channel estimation algorithms because it can effectively filter out noise inside CP. In addition, compared with other similar algorithms, the proposed algorithm dose not result in a significant increase in complexity.

In this article, we present two clustered sparse Bayesian learning (Cluster-SBL) channel estimation algorithms for millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) systems. Different from prior literature, channel path correlations at both the transmitter and the receiver are considered and exploited for estimation performance enhancement. Specifically, we first propose a Kronecker product-based multivariate Gaussian (KP-Gaussian) prior model for mmWave massive MIMO channels, which captures not only sparsity but also the widely existing clustering channel property in mmWave communications. Then, a channel approximation method is derived and leveraged to overcome the challenge caused by channel priors' uncertainty due to the varying propagation environments and improve the robustness of the channel estimation algorithm against channel sparsity. After that, the Cluster-SBL channel estimation algorithm, which is developed based on the expectation maximization (EM) framework and has superior estimation accuracy and high robustness, is proposed. Moreover, to reduce the computation complexity in the channel estimation for practical applications, we also propose an efficient Cluster-SBL (eCluster-SBL) channel estimation algorithm. It is highly beneficial to mmWave massive MIMO systems, especially when the angular resolution is relatively high. Numerical results reveal that, compared with the existing compressed sensing-based and Bayesian criterion-based algorithms, the proposed two channel estimation algorithms exhibit the better performance from both the aspect of estimation accuracy and the aspect of robustness.

Millimeter wave (mmWave) channels can provide new frequency bands for communications together with improved accuracy and resolution for sensing and target localization. Those require parametric channel estimation methods capable of operating with very short coherence times in case of large Doppler spread. In this paper, we address this problem in orthogonal frequency division multiplexing (OFDM) uplink channel estimation. We first observe that it is a variation of the multidimensional harmonic retrieval (MHR) problem in which one has no direct access to the channel tensor due to the strongly time-varying channel. We then derive an improvement of the OFDM space-alternating generalized expectation-maximization (SAGE) algorithm for channel estimation. It is able to solve the problem under intense Doppler spreads and carrier frequency offset (CFO). The proposed method exploits the shift-invariance properties of the OFDM channel tensor to perform efficient coordinate descent steps. We also derive a threshold with probability of false alarm (PFA) guarantees to probe the quality of path initializations. The algorithm estimates the number of significant multipath components by progressively adding paths until the likelihood function reaches a threshold value derived from the noise distribution. We show via numerical examples that the proposed method outperforms the state-of-the-art methods both in channel tensor estimation accuracy as well as in the model order estimation performance.

High-accuracy angle of arrival (AoA) and angle of departure (AoD) estimation is critical for cell search, stable communications and positioning in millimeter wave (mmWave) cellular systems. Moreover, the design of low-complexity AoA/AoD estimation algorithms is also of major importance in the deployment of practical systems to enable a fast and resource-efficient computation of beamforming weights. Parametric mmWave channel estimation allows to describe the channel matrix as a combination of direction-dependent signal paths, exploiting the sparse characteristics of mmWave channels. In this context, a fast Transformed Spatial Domain Channel Estimation (TSDCE) algorithm was recently proposed to perform parametric channel estimation with low complexity, which in turn results in a full characterization of the transmitting and receiving angles for dominant signal paths. In this paper, we analyze the AoA/AoD estimation capability and accuracy of the TSDCE algorithm in detail. We find that the TSDCE algorithm has a significant performance advantage with respect to the traditional approach, which is based on frequency domain processing, in complexity-constrained environments, especially at high signal-to-noise ratios.

ABSTRACTIn amplify‐and‐forward (AF)‐based cooperative spectrum sensing system, the bit‐error‐rate (BER) performance and detection probability will decrease because of the existence of channel estimation error. In this paper, the influence of channel estimation error on system performance is firstly deduced, and then, linear minimum mean‐square error (LMMSE) channel estimation algorithm with filtering delay time‐domain windowing (LMMSE‐filtering‐DTW) technique and modified singular value decomposition‐based LMMSE algorithm are proposed to improve the channel estimation performance for code division multiple access system and orthogonal frequency division multiplexing system in AF cooperative scenario, respectively. Simulation results verify the effectiveness of the two proposed channel estimation algorithms in cooperative spectrum sensing, and whenEb/N0is bigger than 20 dB, given the required false alarm probability smaller than 15%, the difference of detection probability between the channel obtained using the proposed channel estimation algorithms and the ideal channel is less than 2.5%, respectively. Copyright © 2012 John Wiley & Sons, Ltd.

Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep Learning (DL) based OFDM channel estimators are only applicable to Signal-to-Noise Ratios (SNRs) in a small range, and the estimation performance of the existing algorithms is greatly limited when the channel model or the mobile speed at the receiver does not match. To solve this problem, this paper proposes a novel network model NDR-Net that can be used for channel estimation under unknown noise levels. NDR-Net consists of a Noise Level Estimate subnet (NLE), a Denoising Convolutional Neural Network subnet (DnCNN), and a Residual Learning cascade. Firstly, a rough channel estimation matrix value is obtained using the conventional channel estimation algorithm. Then it is modeled as an image and input to the NLE subnet for noise level estimation to obtain the noise interval. Then it is input to the DnCNN subnet together with the initial noisy channel image for noise reduction to obtain the pure noisy image. Finally, the residual learning is added to obtain the noiseless channel image. The simulation results show that NDR-Net can obtain better estimation results than traditional channel estimation, and it can be well adapted when the SNR, channel model, and movement speed do not match, which indicates its superior engineering practicability.