Blind Sequence Detection Research Articles

Blind Signal Detection Under Synchronization Errors for FSO Links With High Mobility

We consider the use of free-space optical communication for fast moving platforms such as high-speed trains, where the sampling clock offset is randomly changing, in addition, the receiver does not have any information on the instantaneous channel fading coefficient. By employing multiple samplers at the receiver, we propose a class of sequence detection methods for the case of On-Off keying (OOK) signaling without using any training sequence. First, we study maximum likelihood-based detection, which has a relatively high-computational complexity. Second, by employing generalized likelihood ratio test, we propose a more practical blind sequence detection method of reduced complexity. To further reduce the computational complexity, third, we propose a novel scheme that uses two wavelengths at the transmitter and differential blind detection at the receiver. Fourth, to benefit from diversity gain with this differential scheme, we consider the use of sufficiently different wavelengths along with sufficient spatial separation between the transmitters and/or the receivers, where we propose an efficient blind detection method. The pros and cons of the proposed detection methods are contrasted through numerical results and their processing loads are compared.

Blind sequence detection using reservoir computing

The performance of M-ary quadrature amplitude modulation (QAM) can seriously be degraded by inter-symbol interference (ISI) as the number of levels increases. To mitigate ISI, blind sequence detection (BSD) has very important applications in data transmission systems, particularly where sending a training sequence is disruptive or costly. A new BSD approach of short data in QAM systems using reservoir computing (RC) is presented, together with the detailed theoretical derivation of the algorithm. Its convergence can be guaranteed within a short data packet and, therefore, it works in systems with a much shorter data record and faster time-varying channels. A RC network is constructed to solve the special issue of BSD, with reservoir weight matrix generated via the reduced QR decomposition from the view of receiving signal subspace instead of being selected randomly. The design methods of the activation function and readout function, the variation rule of initial vector which is changed by reservoir weight, and complexity of the proposed algorithm are described, respectively. The readout weight of the RC network is trained and updated by support vector regression (SVR) with a Gaussian insensitive loss function. The correctness and effectiveness of the new approach are verified by simulations, and some special simulation phenomena of the algorithm are discussed.

Combined Coding and Training for Unknown ISI Channels

The traditional method of sending a training signal to identify a channel, followed by data, may be viewed as a simple code for the unknown channel. Results in blind sequence detection suggest that performance similar to this traditional approach can be obtained without training. However, for short packets and/or time-recursive algorithms, significant error floors exist due to the presence of sequences that are indistinguishable without knowledge of the channel. In this paper, we reconsider training-signal design in light of recent results in blind sequence detection. Specifically, we consider the tradeoff between the complexity of receiver processing and the amount of training overhead required. More generally, we design training codes which combine modulation and training. In order to design these codes, we find an expression for the pairwise error probability of the joint maximum-likelihood (JML) channel and sequence estimator. This expression motivates a pairwise distance for the JML receiver based on principal angles between the range spaces of data matrices. The general code-design problem (generalized sphere packing) is formulated as the clique problem associated with an unweighted, undirected graph. We provide optimal and heuristic algorithms for this clique problem. For both long and short packets, we demonstrate that significant improvements are possible by jointly considering the design of the training, modulation, and receiver processing.

Innovations and singular value decomposition for blind sequence detection in wireless channels

A blind sequence detection algorithm based on the innovations approach is proposed and its performance in a slow and flat Rayleigh fading environment is evaluated. For a blind sequence detection algorithm, the main goal is to estimate the source correlation from the observed received samples. The Viterbi algorithm is then used to detect the input symbols. The proposed detector is a blind algorithm since it does not require any channel parameter information in order to detect the input symbols. A comparison between the innovations and the singular value decomposition (SVD) [14] based blind sequence detection algorithm is also presented. Our study considers only linearly modulated signals which are useful for diversity receivers. Simulation results show that the innovations blind sequence (BSD) detector has about a 3 dB gain over the SVD BSD in the fading model studied in this paper. Various performance measures will be presented to explain the behaviour of these BSDs in both the additive white Gaussian noise and fading environments.

Blind sequence detection without channel estimation

This paper proposes a new blind sequence estimation method for single-input single-output (SISO) systems utilizing an optimal trellis search, which is performed by a channel-independent Viterbi algorithm (CIVA). In contrast to the traditional Viterbi algorithm that requires accurate channel estimation, CIVA does not require channel coefficients. Instead, the metrics are calculated from a bank of test vectors designed off-line. The proposed algorithm has outstanding performance under most of the channel conditions. Specifically, it does not suffer from ill-conditioned channels. In addition, it does not depend on channel correlation estimation and, therefore, has fast convergence. Simulations demonstrate its superior performance over even most training-based equalization algorithms.

Smart antennas in wireless systems: uplink multiuser blind channel and sequence detection

Space-division multiple-access (SDMA) schemes have been proposed to increase the capacity of wireless communication systems by simultaneously transmitting and receiving multiple co-channel signals through different spatial channels. We address the uplink (remote users to the base station antenna array) blind channel and sequence identification problem for an SDMA system. We show that multiuser blind identification can be accomplished by exploiting the spatial and temporal diversities of an antenna array system. In particular, a recursive estimation algorithm is developed to recover multiple signals from intersymbol interference (ISI) and co-channel interference (CCI) by taking advantage of a special structure of the array output and the finite-alphabet property of digital communication signals. The implementation of the proposed approach in practical applications is discussed, and field experiments have been conducted to demonstrate the effectiveness of the proposed algorithm.

A blind sequence detection and its application to digital mobile communication

A method of sequence detection without the knowledge of the channel response is proposed. The Viterbi algorithm is extended to a blind form by introducing a new branch-metric, defined as the minimum of the short time-average of the squared error (y/spl circ/k-yk)/sup 2/, where yk and y/spl circ/k are the received signal and its replica, respectively. All possible candidate sequences contained in the short time squared error are defined as trellis states, for which the short time squared error is minimized in respect to a variable of the unknown channel response. The proposed implicit blind sequence detection need not keep each variable after the branch metric is calculated. The consistency of the algorithm is justified by proving that a unique sequence is detected in noise free case. The proof is accomplished under condition that the period of the time-average is longer than the channel response. If the additive white Gaussian noise is assumed, the short time squared errors are minimized beyond the desired minimum by the standard Viterbi algorithm using an apriori known channel response. We call this phenomenon over-minimization. The over-minimization is a major reason of the unavoidable error rate degradation in the blind receiver. An objective of this paper is to establish blind sequence detection for digital mobile communication. Provided the channel response can be regarded as time-invariant during the period of the short time-average of the squared error, the blind sequence detection keeps the same performance as in time-invariant case. In order to improve the error rate performance, an algorithm based on the fractional sampling scheme is introduced. Several error rate performances and behaviors of error events are investigated by computer simulation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>