Decision-feedback Detection Research Articles

Secrecy Outage Probability and Secrecy Diversity Order of Alamouti STBC with Decision Feedback Detection over Time-Selective Fading Channels

Secrecy Outage Probability and Secrecy Diversity Order of Alamouti STBC with Decision Feedback Detection over Time-Selective Fading Channels

Adaptive Activity-Aware Iterative Detection for Massive Machine-Type Communications

This letter studies the uplink of grant-free low data-rate massive machine-to-machine communications (mMTC) where devices are only active sporadically, which requires a joint activity and data detection at the receiver. We develop an adaptive decision feedback detector along with an $l_{0}$ -norm regularized activity-aware recursive least-squares algorithm that only require pilot symbols. An iterative detection and decoding scheme based on low-density parity-check (LDPC) is also devised for signal detection in mMTC. Simulations show the performance of the proposed approaches against existing schemes.

Non-Coherent Detection for Diffusive Molecular Communication Systems

We study non-coherent detection schemes for molecular communication (MC) systems with negligible inter-symbol interference that do not require knowledge of the channel state information (CSI). In particular, we first derive the optimal maximum likelihood (ML) multiple-symbol (MS) detector for MC systems. As a special case of the optimal MS detector, we show that the optimal ML symbol-by-symbol (SS) detector can be equivalently written in the form of a threshold-based detector, where the optimal decision threshold is constant and depends only on the statistics of the MC channel. The main challenge of the MS detector is the complexity associated with the calculation of the optimal detection metric. To overcome this issue, we propose an approximate MS detection metric that can be expressed in closed form. In addition, we develop a non-coherent decision-feedback detector, which introduces a lower detection delay compared with the optimal MS detector, and a suboptimal blind detector, which has a significantly lower complexity than the optimal MS detector. Finally, we derive analytical expressions for the bit error rate (BER) of the optimal SS detector, as well as upper and lower bounds for the BER of the optimal MS detector. Simulation results confirm the analysis and reveal the effectiveness of the proposed optimal and suboptimal detection schemes compared with the benchmark scheme that assumes perfect CSI knowledge, particularly, when the number of observations used for detection is sufficiently large. Simulation results are also presented that show the performance of the proposed detectors, when inter-symbol interference is non-negligible.

On Signal Detection for OSTBC-Based Amplify-and-Forward Cooperative Relaying System Over Time Selective Channel

The orthogonal space-time block code (OSTBC) and cooperative relaying schemes are the two major spatial diversity techniques to achieve high link reliability in any wireless communication system. Typically, the OSTBC based cooperative amplify-and-forward relaying system achieves optimal diversity gain with simple linear decoding by assuming that the channels remain quasi-static during the length of the Alamouti code-word symbol periods. However, due to time selective fading or relay node’s mobility, the quasi-static assumptions does not hold good and causes severe error floor in the system. In this paper, at first, we have investigated various conventional signal detection methods to improve the system performance which include Alamouti and quasi-ML detection (QMLD) methods. Then, we derive the exact closed-form signal-to-interference-plus-noise ratio expression for the Alamouti method and signal-to-noise ratio for the QMLD method. Based on this performance analysis, we propose a decision feedback detection (DFD) which is a hybrid of Alamouti and QMLD methods. The simulation results show that the proposed DFD method significantly outperforms the conventional QMLD method under high mobility channel environment.

Decision-feedback detection strategy for nonlinear frequency-division multiplexing

By exploiting a causality property of the nonlinear Fourier transform, a novel decision-feedback detection strategy for nonlinear frequency-division multiplexing (NFDM) systems is introduced. The performance of the proposed strategy is investigated both by simulations and by theoretical bounds and approximations, showing that it achieves a considerable performance improvement compared to previously adopted techniques in terms of Q-factor. The obtained improvement demonstrates that, by tailoring the detection strategy to the peculiar properties of the nonlinear Fourier transform, it is possible to boost the performance of NFDM systems and overcome current limitations imposed by the use of more conventional detection techniques suitable for the linear regime.

Signal Processing and Coding System for TDMR Data From Grain-Flipping Probability Model

This paper presents a signal processing and coding system for processing realistic two-dimensional magnetic recording (TDMR) data generated by a grain-flipping probability model. The data set was generated at the Data Storage Institute, Singapore, and will be referred to as the DSI data. Three types of 2-D intersymbol interference (2-D-ISI) detectors with varying complexity are proposed. The first detector is a two-sided feedback (TSF) detector with known boundaries, which simultaneously detects the three tracks of received data. This detector uses only one of the two samples provided by the reader for each bit. The second detector is a joint TSF (JTSF) detector which uses both samples provided by the reader for each bit. The third detector is a 1-D state-input decision-feedback (ODSIDF) detector which detects the data based on a 2-D-ISI mask. Each of the 2-D-ISI detectors is utilized in a turbo iterative approach with an irregular repeat-accumulate (IRA) decoder in the proposed system. We use a coset coding approach in our IRA decoder for decoding the received data based on a known block of the data. A TDMR log-likelihood ratio (LLR) function is used to pass LLRs from the 2-D-ISI detector to the IRA decoder. The read head sensitivity function (2-D-ISI mask) is estimated based on the first sample alone (for TSF and ODSIDF detectors) and both samples (for JTSF detector) from the reader by using the least squares approach based on known data bits for a given set of reader outputs. The best simulation results show that the proposed signal processing and coding approach can achieve up to 1.7 Tb/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> density at 18 nm track pitch (TP). Using a squeeze margin to account for the imperfect tracking of the track center by the read and write heads in a practical system, the proposed system achieves a realistic areal density of 1.25 Tb/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at TP = 18 nm.

Multi-Track Joint Detection for Shingled Magnetic Recording on Bit Patterned Media With 2-D Sectors

Shingled writing on bit patterned media (BPM) is a promising technology to further increase the areal density beyond 1 Tbit/in2 without drastic changes in writer design. In this paper, a shingled writer is modeled for writing a 2-D sector on BPM, and in particular, we investigate the potential benefit of leaving the last track in the 2-D sector untrimmed, making it fatter than the other tracks in that 2-D sector. During detection, the more reliable information from the last untrimmed (i.e., the fat) track is used as a priori information for detecting the data in the other tracks in that 2-D sector. The simulation results indicate that for 2-D sector with ten tracks (at 3.64 Tbits/in2 channel bit density) and 5% media noise and at a target bit error rate of $10^{\mathrm {-3}}$ , using the fat track information, 2-D equalizer +2-D target + symbol-based pattern-dependent noise-predictive detector detecting a single track can provide about 2.9 dB SNR gain compared with that with the same joint detection method using uniform tracks at the same areal density. However, the latency increases due to the decision feedback detection. We can reduce this latency penalty by detecting three tracks at a time, but the SNR gain then reduces to 0.9 dB.

簡易な1次伝送路予測による判定帰還多重遅延検波とその音響通信への応用

簡易な1次伝送路予測による判定帰還多重遅延検波とその音響通信への応用

Single Antenna Interference Cancellation Algorithm Based on Lattice Reduction

—Single antenna co-channel interference (CCI) cancellation is a challenge in mobile communication systems, especially for downlink systems. A novel single antenna CCI cancellation algorithm based on lattice reduction (LR) and decision feedback equalization scheme is proposed in this paper. The idea behind the proposed algorithm is to form a multichannel multi-user detection model through over-sampling and then adopts a decision feedback detection method based on LR. Complexity analysis results show that the complexity of the proposed algorithm is quadratic with the memory length of the channels and is nearly independent of the modulation order. Furthermore, simulation results are also presented to indicate the bit error rate performance of the proposed algorithm and its robustness with respect to channel estimation errors.

Adaptive and Iterative Multi-Branch MMSE Decision Feedback Detection Algorithms for Multi-Antenna Systems

In this work, decision feedback (DF) detection algorithms based on multiple processing branches for multi-input multi-output (MIMO) spatial multiplexing systems are proposed. The proposed detector employs multiple cancellation branches with receive filters that are obtained from a common matrix inverse and achieves a performance close to the maximum likelihood detector (MLD). Constrained minimum mean-squared error (MMSE) receive filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive implementation of the proposed MB-MMSE-DF detector is developed along with a recursive least squares-type algorithm for estimating the parameters of the receive filters when the channel is time-varying. A soft-output version of the MB-MMSE-DF detector is also proposed as a component of an iterative detection and decoding receiver structure. A computational complexity analysis shows that the MB-MMSE-DF detector does not require a significant additional complexity over the conventional MMSE-DF detector, whereas a diversity analysis discusses the diversity order achieved by the MB-MMSE-DF detector. Simulation results show that the MB-MMSE-DF detector achieves a performance superior to existing suboptimal detectors and close to the MLD, while requiring significantly lower complexity.

Reduced-Dimension Multiuser Detection

We present a reduced-dimension multiuser detector (RD-MUD) structure for synchronous systems that significantly decreases the number of required correlation branches at the receiver front end, while still achieving performance similar to that of the conventional matched-filter (MF) bank. RD-MUD exploits the fact that, in some wireless systems, the number of active users may be small relative to the total number of users in the system. Hence, the ideas of analog compressed sensing may be used to reduce the number of correlators. The correlating signals used by each correlator are chosen as an appropriate linear combination of the users' spreading waveforms. We derive the probability of symbol error when using two methods for recovery of active users and their transmitted symbols: the reduced-dimension decorrelating (RDD) detector, which combines subspace projection and thresholding to determine active users and sign detection for data recovery, and the reduced-dimension decision-feedback (RDDF) detector, which combines decision-feedback matching pursuit for active user detection and sign detection for data recovery. We derive probability of error bounds for both detectors, and show that the number of correlators needed to achieve a small probability of symbol error is on the order of the logarithm of the number of users in the system. The theoretical performance results are validated via numerical simulations.

Adaptive decision feedback detection with parallel interference cancellation and constellation constraints for multiuser multi‐input–multi‐output systems

In this study, a novel low-complexity adaptive decision feedback (DF) detection with parallel DF (P-DF) and P-DF constellation constraints (P-DFCC) is proposed for multiuser multi-input–multi-output (MIMO) systems. The authors propose a constrained constellation map which introduces a number of selected points served as the feedback candidates for interference cancellation. By introducing a reliability checking, a higher degree of freedom is introduced to refine the unreliable estimates. The P-DFCC is followed by an adaptive receive filter to estimate the transmitted symbol. To reduce the complexity of computing the filters with time-varying MIMO channels, an adaptive recursive least squares algorithm is employed in the proposed P-DFCC scheme. An iterative detection and decoding (Turbo) scheme is considered with the proposed P-DFCC algorithm. Simulations show that the proposed technique has a complexity comparable to the conventional P-DF detector while it obtains a performance close to the maximum-likelihood detector at a low to medium signal-to-noise ratio range. .

Energy Spreading Transform Approach to Achieve Full Diversity and Full Rate for MIMO Systems

Full-diversity full-rate (FDFR) space-time codes achieve both high data rate and good reliability at the cost of high decoding complexity. In this paper, we propose a low-complexity MIMO scheme that achieves both full diversity and full rate over flat fading channels for a sufficiently large number of transmit and receive antennas. The proposed scheme is constructed by applying energy spreading transforms (EST's) to multiple data streams and spatially multiplexing the streams to multiple transmit antennas. Simulation results show that the proposed FDFR scheme outperforms the threaded algebraic space-time (TAST) code, which is a FDFR code based on maximum likelihood (ML) detection, when the number of transmit antennas (with the same number of receive antennas) are three and four. However, its detection complexity is only that of a decision-feedback detector.

General Closed-Form Expressions for the SER and BER of Decision-Feedback Detection With Error Propagation Over MIMO Fading Channels

The symbol error rate (SER) and bit error rate (BER) of the multiple-input-multiple-output (MIMO) system with decision-feedback detection (DFD) are analyzed over a Rayleigh fading channel. By using the concepts of “event” and “state” related to error propagation due to the decision-feedback process, we present general closed-form expressions for the SER and BER of each layer over quadrature-amplitude modulation (QAM) or phase-shift keying (PSK) for an arbitrary number of transmit and receive antennas. We also derive the asymptotic BER and SER performances of each layer. Numerical results show that our analysis matches very well with the Monte Carlo simulation. Our approach can be extended to the exact performance analysis of various wireless communication systems.

Adaptive Decision-Feedback Detection With Constellation Constraints for MIMO Systems

A novel low-complexity decision-feedback detection algorithm with constellation constraints (DFCC) is proposed for multi-input-multi-output (MIMO) systems. An enhanced interference cancellation is achieved by introducing multiple constellation points as decision candidates. A complexity reduction strategy is also developed to avoid redundant processing with reliable decisions. For time-varying channels, the proposed receiver updates the filter weights using a recursive least-squares (RLS)-based algorithm. This efficient detector is also incorporated in a multiple-branch (MB) structure to achieve a higher detection diversity order. A soft-output DFCC detector is also proposed as a component of an iterative detection and decoding receiver scheme. Simulations show that the proposed DFCC technique has complexity as low as the adaptive decision-feedback (DF) detector while it significantly outperforms ordered successive interference cancellation (OSIC) processing.

A Novel Computationally Efficient Algorithm for Multiuser Detection in Long-Code CDMA System

Computation of the Cholesky decomposition of the autocorrelation matrix is essential in developing zero-forcing, minimum mean square error (MMSE), and decision feedback detectors for a long code-division multiple-access (CDMA) system. However, computing the Cholesky decomposition of the time-varying correlation matrix requires many computer operations. This is because the computation of the filter weights is segment specific (varies from one symbol interval to next). In this paper, a novel and fast algorithm is developed to compute the Cholesky decomposition of the autocorrelation matrix for a long-code CDMA system. The smart update algorithms, to compute the solution (filter weights) of one segment from the solution of the previous segment, reduce the computational complexity, particularly for large-sized autocorrelation matrices. We exploit the fact that the channel matrix of a long-code CDMA system is block banded with a shift structure. Although the channel matrix is not Toeplitz for a long-code CDMA system, the shift structure of the matrix, along with its banded structure, allows the development of fast smart update recursive algorithms for data estimation. The relevance of this algorithm is discussed for channels with multipath delays extending over many symbol intervals. Simulations show that the proposed algorithm performs the same as competing multiuser detector algorithms for long-code CDMA at much reduced computational complexity.

Joint Design of Transceivers for Multiple-Access Channels Using MMSE Decision Feedback Detection

We consider the joint design of transceivers for a multiple-access multi-input-multi-output (MIMO) system communicating over intersymbol interference (ISI) channels. The system we consider is equipped with the minimum mean-square error (MMSE) decision-feedback (DF) detector. Here, we explore a novel perspective of designing a transceiver that minimizes the arithmetic mean-square error (MSE) of MMSE-DF detection and satisfies a fixed-sum Gaussian mutual information constraint. For this optimization problem, a direct explicit solution is obtained. We show that the optimal solution is achieved if and only if uniform user mutual information and uniform symbol mutual information are both achieved. Uniform user mutual information is attained by successively solving a series of problems of minimizing individual user transmission power under fixed mutual information, whereas uniform symbol mutual information is maintained by applying the equal-diagonal QRS decomposition. We also show that, in addition to minimizing the arithmetic MSE of MMSE-DF detection, such uniform mutual information distribution has also other desirable properties: 1) The optimal detection order in terms of signal-to-interference-noise ratios (SINRs) for both the users and the symbols is the natural transmission order of the system; and 2) when the sum Gaussian mutual information is large, the performance of the optimum transceiver using the MMSE-DF receiver asymptotically approaches that of the maximum-likelihood (ML) receiver.

An Efficient Statistical Pruning Algorithm for Fixed-Complexity Sphere Decoder

We create an efficient statistical pruning (SP) algorithm for fixed-complexity sphere decoder (FSD) by utilizing partial decision feedback detection (i.e., SP-FSD). Simulation results show that SP-FSD not only attains the near-optimal performance, but also achieves much lower complexity than the original FSD and its two lately-developed variants: the simplified FSD (SFSD) and the statistical threshold-based FSD (ST-FSD).

Joint Bit Allocation and Precoding for MIMO Systems With Decision Feedback Detection

This paper considers the joint design of bit loading, precoding and receive filters for a multiple-input multiple-output (MIMO) digital communication system employing decision feedback (DF) detection at the receiver. Both the transmitter as well as the receiver are assumed to know the channel matrix perfectly. It is well known that, for linear MIMO transceivers, a diagonal transmission (i.e., orthogonalization of the channel matrix) is optimal for some criteria. Surprisingly, it was shown five years ago that for the family of Schur-convex functions an additional rotation of the symbols is necessary. However, if the bit loading is optimized jointly with the linear transceiver, then this rotation is unnecessary. Similarly, for DF MIMO optimized transceivers a rotation of the symbols is sometimes needed. The main result of this paper shows that for a DF MIMO transceiver where the bit loading is jointly optimized with the transceiver filters, the rotation of the symbols becomes unnecessary, and because of this, also the DF part of the receiver is not required. The proof is based on a relaxation of the available bit rates on the individual substreams to the set of positive real numbers. In practice, the signal constellations are discrete and the optimal relaxed bit loading has to be rounded. It is shown that the loss due to rounding is small, and an upper bound on the maximum loss is derived. Numerical results are presented that confirm the theoretical results and demonstrate that orthogonal transmission and the truly optimal DF design perform almost equally well.

Space-Time Adaptive MMSE Multiuser Decision Feedback Detectors With Multiple-Feedback Interference Cancellation for CDMA Systems

In this paper, we propose a novel space-time minimum mean square error (MMSE) decision feedback (DF) detection scheme for direct-sequence code-division multiple access (DS-CDMA) systems with multiple receive antennas, which employs multiple-parallel-feedback (MPF) branches for interference cancellation. The proposed space-time receiver is then further combined with cascaded DF stages to mitigate the deleterious effects of error propagation for uncoded schemes. To adjust the parameters of the receiver, we also present modified adaptive stochastic gradient (SG) and recursive least squares (RLS) algorithms that automatically switch to the best-available interference cancellation feedback branch and jointly estimate the feedforward and feedback filters. The performance of the system with beamforming and diversity configurations is also considered. Simulation results for an uplink scenario with uncoded systems show that the proposed space-time MPF-DF detector outperforms existing schemes such as linear, parallel DF (P-DF), and successive DF (S-DF) receivers in terms of bit error rate (BER) and achieves a substantial capacity increase in terms of the number of users, compared with the existing schemes. We also derive the expressions for MMSE achieved by the analyzed DF structures, including the novel scheme, with imperfect and perfect feedback and expressions of signal-to-interference-plus-noise ratio (SINR) for the beamforming and diversity configurations with linear receivers.