CDMA blind channel estimation: performance analysis

Widely distributed multiple-input multiple-output (MIMO) radar systems offer parameter estimation improvement for target localization, proportional to the product of the number of transmitting and receiving radars and the total transmitted power. Thus far, power allocation has been uniformly distributed between the system transmitters. For a large number of radars, the achievable localization mean-square error (MSE), with full resource allocation, may extend beyond the system predetermined performance goals, such as target localization accuracy (i.e., minimum estimation MSE) and total power radiation. In this study, power allocation schemes are developed, taking into account system constraints. The first is concerned with minimizing the total transmitted power such that a predefined estimation MSE objective is met, while keeping the transmitted power at each station within an acceptable range. The second, optimally distributes a given power budget among all transmitting radars to maximize performance, i.e., minimize the attainable localization MSE. As the Cramer-Rao bound (CRB) is known to be asymptotically tight to the maximum likelihood estimator (MLE) MSE at high SNR, it is used as a metric for the estimation MSE. The CRB is derived for a signal model that incorporates the propagation path loss, the target radar cross section (RCS), and the transmitters' powers. It is shown that uniform or equal power allocation is not in general optimal and that the proposed allocation algorithms result in a local optimum that provided either better localization MSE for the same power budget or requires less power to establish the same performance in terms of estimation MSE. A physical interpretation of these conclusions is offered.

We present techniques of using repeated training blocks for maximum-likelihood (ML) and least-squares (LS) based channel estimation of orthogonal frequency division multiplexing (OFDM) systems over slow quasi-static fading channels. Both frequency-selective and frequency-nonselective channels are considered. Explicit analytical expressions of channel estimators and the corresponding estimator mean square errors (MSEs) are derived for both MLand LS estimations. For the MLchannel estimators, closed-form expressions of Cramer-Rao lower bounds (CRLBs) are also obtained to compare with the MSEs. Both theoretical and numerical results are presented and compared.

In this paper, we evaluate the Cramer-Rao bounds (CRB) for the estimation of the carrier frequency offset (CFO) for general QAM modulations with no knowledge of the transmitted sequence (blind operation) in frequency hopping (FH) based short burst mode mobile radios. We investigate the blind CFO estimation problem in FH based mobile system that uses multiple short narrowband bursts in different frequency channels for the transmission of one packet. Joint blind channel and CFO estimation over multiple frequency bursts is considered. In the first part of the paper, we determine the limiting performance by finding the CRB for blind estimation, and compare it to the CRB for known data (genie aided) case. The CRB relates the reliability of CFO estimation to the number of radio frequency bursts (RFB) used, constellation and SNR level, and is an important measure for system design without the need to simulate specific algorithms. It is shown that the linear combination (in MMSE sense) of multiple frequency estimates, each obtained from a single RFB independently, achieves the joint CRB over multiple bursts. In the second part of the paper, some practical blind estimation methods are proposed. They are shown to exhibit close performance to the true CRB.

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.

Blind channel estimation exploiting transmission filter knowledge

This paper addresses the problem of optimal training for joint carrier frequency offset (CFO) and channel estimation in multiuser MIMO OFDM systems, where each user can utilize all available subcarriers. To choose the training sequence with the goal of providing the smallest estimation mean square error (MSE), the Cramer-Rao bound (CRB) is derived in close-form. However, little insight on the sequence selection can be obtained from the CRB directly due to its complicated dependence on training sequence and channel parameters. To proceed, asymptotic CRB, which has a much simpler dependence on the training, is derived. It is found that the optimal training sequences should be individually white and uncorrelated with each other. Simulation results illustrate the merits of the proposed training design.

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.

Traditionally, the performance of blind SIMO channel estimates has been characterized in a deterministic fashion, by identifying those channel realizations that are not blindly identifiable. In this paper, we focus instead on the performance of Linear Equalizers for fading channels when they are based on blind channel estimates. Our analysis shows that with Zero Forcing Linear Equalizer (ZF-LE) at least one order of the diversity is lost depending on the way by which the scalar ambiguity that results from the blind channel estimation is resolved. However, in some Tx scenarios we are able to recover the diversity with MMSE-LE. Various Tx scenarios are considered in detail.

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 is an important issue in wireless communication context. It has been a hot topic in cognitive radio and communication surveillance. Also, it is regarded as the key technique to improve the capacity of the communication systems. In this work, the blind channel estimation problem for short-burst amplitude and phase modulated signal is discussed. The decision feedback iterative structure is designed based on maximum likelihood function (MLF) model and the finite alphabet (FA) property. Two equivalent parametric models are used alternately to obtain stable estimates for both channel coefficients and transmitted symbols. As a result, the estimation with high accuracy is realized while using only a few symbols, making it suitable for short burst signal. In addition, the Cramer-Rao bound (CRB) is considered in this issue. Simulation verifies the superiority of proposed method.

In this paper, we consider the problem of blind channel estimation for single-input single-out (SISO) orthogonal frequency-division multiplexing (OFDM) systems via second order statistics (SOS) only. Based on the assumption that the transmitted symbols are independent and identically distributed (i.i.d), we develop a simple subspace based blind channel estimation technique by utilizing a non-redundant linear block precoding. The proposed method offers improved performance over the existing method using similar precoding technique. Additionally, the design criteria for the precoders by which the performance can be greatly improved is discussed, and the stochastic Cramer-Rao bound (CRB) is derived. The numerical results clearly show the effectiveness of our proposed algorithm

Nowadays, modern mobile telecommunication systems use multiple input multiple output (MIMO) with orthogonal frequency division multiplexing (OFDM) because of its robustness and huge spectrum efficiency.The most significant issues in MIMO-OFDM are noises occurred during communication and precise channel estimation.In this paper, the double density dual-tree complex wavelet transform (DDDTCWT) based denoising is proposed for eliminating noises occurred over the MIMO-OFDM.The fast independent component analysis using negentropy (FICAN) is used for performing blind channel estimation according to the statistics obtained from the received signal.Therefore, an effective denoising and channel estimation accomplished in the MIMO-OFDM is used to reduce the errors during the transmission.The performance of DDDTCWT-FICAN is analyzed using mean square error (MSE), bit error rate (BER) and symbol error rate (SER).The existing research used to evaluate the efficiency of DDDTCWT-FICAN are pilot-based interpolation (PBI) technique, adaptive optimized fast blind channel estimation (AOFBCE) and discrete fourier transform (DFT) with denoise model based least square (LS) Wiener namely DFT-LS-WIENER.The BER of the DDDTCWT-FICAN for 10 dB of SNR is 0.0053, it is less when compared to the PBI.

This work proposes a novel pilot structure (PS) to improve channel estimation (CE) in massive multiple-input multiple-output filter bank multi-carrier (mMIMO-FBMC) communications using offset quadrature amplitude modulation (OQAM). Accurate CE is essential to fully benefit from the corresponding spectral efficiency (SE) advantages and robustness against dispersive channels. Unlike orthogonal frequency division multiplexing (OFDM), FBMC/OQAM shows only real-field orthogonality. Therefore, corresponding signals suffer from intrinsic interference, motivating the use of alternative CE techniques. Furthermore, typical CE methods for mMIMO-FBMC systems require large training overheads, particularly for a large number of users or for flat-fading channel conditions over each subcarrier band. To address these issues, this paper proposes a PS that reduces the training overhead by interleaving user pilots in frequency while using conventional OFDM CE techniques, particularly the least squares (LS) method in the context of single-user (SU) and multi-user (MU) scenarios. Analytical expressions for the mean square error (MSE) and the Cramer-Rao lower bound (CRLB) are derived for the estimation technique considering the proposed PSs. Furthermore, performed simulations over the 5G QuaDRiga channel show that the proposed PS improves SE while delivering optimal performance in both preamble-based MU-mMIMO and SU systems.

This paper considers the problem of the joint channel and carrier frequency offset (CFO) estimation for asynchronous amplify-and-forward (AF) two-way relay networks (TWRNs). This problem has not been solved to the best of our knowledge, and the presence of timing offsets between the two terminals, in addition to the frequency offsets, makes it necessary to develop signal sampling and joint estimation schemes that explicitly take into account both offsets. As potential solutions, we investigate two estimation strategies. The first strategy is pilot-based estimation, and in this context, we develop the maximum-likelihood joint CFO and channel estimator. Then, we consider the semi-blind estimation strategy, proposing a novel estimator based on expectation maximization (EM). As an alternative to the EM, we also investigate decision-directed (DD) semi-blind estimation. The semi-blind Cramer-Rao bound (CRB) is also obtained as a benchmark. Extensive experiments are used to compare the different estimation methods, showing that the proposed EM algorithm yields significantly lower mean-squared error than both the pilot-based estimator and the DD estimator and converges to the obtained CRB. It also yields much better symbol error rates. This is achieved at a reasonable computational complexity, and only a limited number of EM iterations are sufficient for convergence. The main contribution of our work is the development of the EM estimator, the corresponding CRB, and the use of simulations to demonstrate the superior performance of the proposed EM algorithm compared to other potential solutions.

The Cramer-Rao lower bound (CRLB) is derived for the channel estimation of direct sequence-code division multiple access ultra-wideband (DS-CDMA UWB) communication system. We have modified the channel model and reduced the number of estimation parameters. Furthermore, a low-complexity data-aided Least Square (LS) channel estimation algorithm is proposed for the modified model. In addition, the problem of designing orthogonal training sequence is discussed. Finally, numerical examples are provided to evaluate both the derived CRLB and the proposed channel estimation algorithm.