Receiver diversity with blind fir SIMO channel estimates

Blind channel estimation in CDMA are studied in this paper. MSE (mean squared error) of parameter estimation is important for evaluating the estimation algorithm and its lower bound is CRB (Cramer Rao bound). In this paper, FIM and CRB for CDMA complex channel estimation are analyzed. Furthermore FIM and CRB for CDMA blind complex channel estimation under linear constraints is studied in complex Gaussian signal model. In the end, subspace based blind channel estimation in CDMA are simulated and compared with CRB under linear constraints.

Channel estimation can be classified into pilot‐assisted channel estimation, blind channel estimation, and decision DDCE. This chapter focuses on pilot‐assisted channel estimation in OFDM systems. There are two main design issues for channel estimation in OFDM systems. The first one is pilot design and the second one is channel estimator design with both low complexity and accuracy. An interpolation technique should be performed to obtain a channel estimation value at each data subcarrier. Channel estimation for MIMO‐OFDM systems is more complicated than single antenna systems but the basic principle is similar to the single antenna systems. The purpose of equalizers is to find the inverse function of the channel response and eliminate an Inter‐Symbol Interference (ISI) caused by the dispersive nature of the channel. The channel estimator and the equalizer are designed according to the system requirements such as pilot assignment, antenna array, system complexity, latency, and power.

Delay diversity is an effective transmit diversity technique to combat adverse effects of fading. Thus far, previous work in delay diversity assumed that perfect estimates of current channel fading conditions are available at the receiver and training symbols are required to estimate the channel from the transmitter to the receiver. However, increasing the number of the antennas increases the required training interval and reduces the available time within which data may be transmitted. Learning the channel coefficients becomes increasingly difficult for the frequency selective channels. In this paper, with the subspace method and the delay character of delay diversity, a channel estimation method is proposed, which does not use training symbols. It addresses the transmit diversity for a frequency selective channel from a single carrier perspective in the form of a simple equivalent flat fading model. Monte Carlo simulations give the performance of channel estimation and the performance comparison of our channel-estimation-based detector with decision feedback equalization, which uses the perfect channel information.

In the conventional pilot-symbol-aided channel estimation for the MIMO-OFDM system, the pilots should be orthogonal in frequency, time, code, or spatial domain to mitigate the inter- antenna interference. In the frequency domain multiplexed (FDMed) pattern, the training pilots transmitted from different antennas conventionally occupy different sub-carriers exclusively within the same OFDM symbol duration. However, this method dramatically increases the pilot overhead as the number of the transmit antennas increases. In this paper a joint pilot-aided and blind decision-directed channel estimation (PA-DDCE) scheme is proposed to achieve a good tradeoff between the pilots overhead and the system performance. The proposed scheme utilizes the initial estimated channel obtained by the conventional channel estimation to detect the transmitted data symbol in the middle sub-carrier between any two adjacent pilot sub-carriers. Then these symbols are regarded as the "extra pilot symbols" and are applied to estimate the channel on the whole frequency band. From our simulation results the proposed PA-DDCE scheme can outperform the conventional channel estimation significantly in both the BER and the throughput performances.

Block transmission has recently been considered as an alternative to the conventional continuous transmission technique. In particular, block transmission techniques with zero padding (ZP) and cyclic prefix (CP) are becoming attractive procedures for their ability to eliminate both inter-symbol interference (ISI) and inter-block interference (IBI). In this paper, we present a unified approach to blind channel estimation and interference suppression for block transmission using ZP or CP in both single-carrier (SC) and multicarrier (MC) systems. Our approach uses a generalized multichannel minimum variance principle to design an equalizing filterbank. Channel estimate is then obtained from an asymptotically tight lower bound of the filterbank output power. Numerical examples show that the proposed schemes approach the CRB as the signal-to-noise ratio (SNR) increases. Additionally, they exhibit low sensitivity to unknown narrowband interference and compare favorably with subspace blind channel estimators.

Blind channel estimation (BCE) plays a critical role in wireless systems, aiming to recover channel information without relying on pilot signals. To enable accurate joint recovery of transmitted symbols and estimation of multipath channels, this work proposes a novel online algorithm using deep reinforcement learning (DRL), specifically the Deep Deterministic Policy Gradient (DDPG) framework. The method directly outputs equalized symbols, with the multipath channel response modeled as trainable parameters. Simulation results show that the proposed algorithm outperforms advanced methods in bit error rate (BER) and channel estimation mean squared error (MSE), and is highly competitive with conventional pilot-based methods.

In this paper, we propose a non-redundant preceding-assisted blind channel estimation scheme for the improper orthogonal frequency-division multiplexing (OFDM) systems. In the proposed scheme, a modified cross-correlation matrix of the augmented received signal is obtained by dividing the cross- correlation matrix of the even-indexed and odd-indexed OFDM received signal by the precoding matrix element-wise. The channel coefficient vector is estimated as a principal eigenvector of the modified cross-correlation matrix, and it is obtained by the computationally simple power method without the need of the complicate eigenvalue decomposition. Simulation results show that the channel estimation and bit error rate (BER) performance of the proposed scheme is superior to that of the conventional preceding-assisted blind channel estimation scheme.

This letter considers a joint user activity, channel and data estimation problem in an uplink grant-free massive MIMO systems with low-precision analog-to-digital converters (ADCs). This joint estimation is firstly formalized as a non-overlapping group sparse problem, in which the components of compound channel follow independent conditional distribution. To address this problem, a new algorithm consists of the celebrated bilinear generalized approximate message passing (BiG-AMP) and loop belief propagation (LBP) is then proposed, where the strong connection of compound channel can be decoupled in LBP part. By exchanging the information between BiG-AMP part and LBP part, the proposed algorithm improves the performance of channel estimation compared with HyGAMP based method, in which the estimated payload data of proposed algorithm are utilized to aid channel estimation and it leads to relatively few pilot symbols to achieve equivalent channel and data estimation performances. The simulation results confirm that our proposed joint estimation algorithm improves the performance of the existing works in terms of user activity, channel and data estimation.

We present a neural network (NN) approach to the blind channel and symbol estimation problem in portable communications. It is based on deterministic blind estimation methods, which utilize multiple antennas and/or oversampling in order to identify the channel and the data symbols. These deterministic approaches employ a least squares error metric, and then solve the problem algebraically. We use a NN to solve the estimation problem by mapping the quadratic cost function to the NN energy function, which is then minimized by iteratively updating each of its nodes (clusters of neurons called affinity cells). While its performance is found to be comparable to the SVD-based least squares methods, the NN offers significant practical advantages stemming from its distributed and fault-tolerant nature. Another important benefit of the NN approach, in contrast to the algebraic approaches, is its natural ability to yield the best solution in the space of finite word-length parameter vectors.

In this letter, we jointly consider the problems of blind channel estimation (BCE), channel equalization and data detection in underwater visible light communication (UVLC) systems. Specifically, we propose to solve them with a model-driven deep learning (DL) approach. Inspired by the module-based signal processing methodology and communication theory, we devise a new blind receiver architecture called blind detection network (BDNet), which consists of three modules corresponding to BCE, channel equalization, and symbol demapping, respectively. Different from existing BCE schemes, the BDNet does not directly estimate the channel itself, but instead estimates the inverse channel regardless of the scalar ambiguity issue by learning the latent channel features from the received signal only. The UVLC channel effects can then be partially compensated by the zero-forcing equalization module of BDNet. Finally, the symbol demapping module recovers the transmitted symbols with the aid of the learned decision thresholds. Simulation results show the competitive bit error rate performance of BDNet in comparison to conventional pilot-aided schemes.

The application of the expectation-maximization (EM) algorithm to channel estimation results in a well-known iterative channel-and-symbol estimator (ICSE). The EM-ICSE iterates between a symbol estimator based on the forward-backward recursion (BCJR equalizer) and a channel estimator, and may provide approximate maximum-likelihood blind or semiblind channel estimates. Nevertheless, the EM-ICSE has high complexity, and it is prone to misconvergence. In this paper, we propose the extended-window (EW) estimator, a novel channel estimator for ICSE that can be used with any soft-output symbol estimator. Therefore, the symbol estimator may be chosen according to performance or complexity specifications. We show that the EW-ICSE, an ICSE that uses the EW estimator and the BCJR equalizer, is less complex and less susceptible to misconvergence than the EM-ICSE. Simulation results reveal that the EW-ICSE may converge faster than the EM-ICSE.

Multiuser multiple-input multiple-output (MU-MIMO) wireless systems that employ single-carrier frequency-domain equalization (SC-FDE) for uplink transmissions can provide high data rates with increased spectral efficiency under severe channel conditions. However, the availability of accurate channel estimates at the base station (BS) receiver is crucial for achieving peak performance. In this paper, we investigate the use of subspace decomposition and derive a novel algorithm for the blind estimation of MU-MIMO channels in SC-FDE systems, as specified by the 3GPP LTE. By exploiting the long data blocks available in LTE standards, our proposed blind algorithm can obtain accurate estimates of the MU-MIMO channels over every block of transmitted data. This provides for a bandwidth-efficient solution in SC-FDE systems by eliminating (reducing) the need to allocate an entire block of pilot sequences for each active transmitter. Furthermore, since the channel estimation is deployed at the BS, the computational complexity is not considered to be a significant burden for future systems in exchange for the increased spectral efficiency. The results of simulations over fading channels, using realistic system parameters representative of LTE-Advanced, demonstrate the advantages of our proposed blind subspace-based channel estimation algorithm and support the feasibility of the resulting MU-MIMO SC-FDE scheme with reduced training.

Joint blind timing synchronization and channel estimation using receiver diversity are investigated in this paper. If the channel length is smaller than the cyclic prefix length, the diversity based timing synchronization can achieve perfect timing estimator at high SNR. At low SNR, multiple blocks can be used to improve the performance. The proposed algorithm is able to identify channel length, therefore it can facilitate blind channel estimation algorithms that require the knowledge of channel length.

Channel estimation and blind equalization of MIMO (multiple-input multiple-output) communications channels is considered using primarily the second-order statistics of the data. We consider estimation of (partial) channel impulse response and design of finite-length MMSE (minimum mean-square error) blind equalizers. The basis of the approach is the design of a zero-forcing equalizer that whitens the noise-free data. We allow infinite impulse response (IIR) channels. Moreover, the multichannel transfer function need not be column-reduced. Our approaches also work when the subchannel transfer functions have common zeros so long as the common zeros are minimum-phase zeros. The channel length or model orders need not be known. The sources are recovered up to a unitary mixing matrix and are further 'unmixed using higher-order statistics of the data. An illustrative simulation example is provided.

We propose two matched filter bounds (MFBs) to characterize the performance of receivers using reduced-order channel models. The first one, whitened matched filter bound (WMFB), uses the channel model to perform the spatio-temporal matched filtering that yields data reduction from multichannel to single-channel form. The rest of the processing remains optimal. The second one, ISI canceller matched filter bound (ICMFB), on the other hand bounds the performance of the Viterbi algorithm with the reduced channel model. Two methods of obtaining reduced-order channel models are discussed to illustrate these measures: blind channel estimation by deterministic maximum likelihood estimation (which maximizes WMFB) and channel estimation by training sequence.