Maximum Likelihood Detection Algorithm Research Articles

A Sphere Detection Based Adaptive MIMO Detection Algorithm for LTE-A System

An adaptive MIMO detection algorithm for LTE-A system which is based on sphere detection is proposed in this paper. The proposed algorithm uses M-algorithm for reference to remove unreliable constellation candidates before search, and the number of constellation reservation is adaptively adjusted according to SNR. Simulations of LTE-A downlink show that the BER performance of the proposed detection algorithm is nearly the same as maximum likelihood (ML) detection algorithm. However, the complexity is reduced by about 30% compared with full constellation sphere detection.

Design of Signature Sequences for Overloaded CDMA and Bounds on the Sum Capacity With Arbitrary Symbol Alphabets

In this paper, we explore some of the fundamentals of synchronous Code Division Multiple Access (CDMA) as applied to wireless and optical communication systems under very general settings (of any size) for the user symbols and the signature matrix entries. The channel is modeled by real/complex additive noise of arbitrary distribution. Two problems are addressed. The first problem concerns whether uniquely detectable overloaded matrices exist in the absence of additive noise under these general settings, and if so, whether there are any practical optimum detection algorithms. The second one is about the bounds for the sum channel capacity when user data and signature matrices employ any real or complex alphabets (finite or infinite). In response to the first problem, we have developed practical maximum likelihood detection algorithms for overloaded CDMA systems for a large class of alphabets. In response to the second problem, a general theorem has been developed in which the sum capacity lower bounds with respect to the number of users, spreading gain, and signal-to-noise ratio can be derived. To show the power and utility of the main theorem, a number of sum capacity bounds for special cases are evaluated. An important conclusion of this paper is that the lower and upper bounds of the sum capacity for small/medium-size CDMA systems depend on both the input and the signature symbols; this is contrary to the asymptotic results for large-scale systems reported in the literature (also confirmed in this paper) where the signature symbols and statistics disappear for signature matrices and input vectors with i.i.d. entries. Furthermore, upper and asymptotic bounds are derived and compared to other derivations.

Asymptotically maximum likelihood detection for subcarrier mapping-induced cyclostationary signatures in cognitive radio networks

In cognitive radio networks based on orthogonal frequency division multiplexing, the subcarrier mapping-induced cyclostationary signatures are utilized for robust network coordination. However, the effect of network coordination depends on the reliable detection of such cyclostationary signatures. Moreover, doubly selective fading increases the difficulty of reliable detection. In this paper, we first derive an exact covariance matrix to characterize the statistics of the cyclostationary signatures induced by subcarrier mapping. Using the exact covariance matrix rather than its estimation adopted in conventional algorithms, we propose an asymptotically maximum likelihood detection algorithm. The detection performance of the proposed asymptotically maximum likelihood detection algorithm is analyzed and evaluated both in flat fading and doubly selective fading channels. Simulation results show that the proposed algorithm outperforms the conventional low-complexity single-cycle signature detection algorithm in terms of less observation period or smaller mapping set size. Copyright © 2011 John Wiley & Sons, Ltd.

A Per-Survivor Processing Receiver for MIMO Transmission Systems With One Unknown Channel Order Per Output

The order of a communications channel is the length of its impulse response. Recently, several works have tackled the problem of estimating the order of a frequency-selective multiple-input-multiple-output (MIMO) channel. However, all of them consider a single order, despite the fact that a MIMO channel comprises several subchannels (specifically, as many as the number of inputs times the number of outputs), each one possibly with its own order. In this paper, we introduce an algorithm for maximum-likelihood sequence detection (MLSD) in frequency- and time-selective MIMO channels that incorporates full estimation of the MIMO channel impulse response (CIR) coefficients, including one channel order per output. Simulation results following the analytical derivation of the algorithm suggest that the proposed receiver can achieve significant improvements in performance when transmitting through a MIMO channel that effectively comprises subchannels of different lengths.

Memory-Constrained Tree Search Detection and New Ordering Schemes

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Hardware implementations of tree search-based multiple-input multiple-output (MIMO) detection often have limited performance due to large memory requirement or high computational complexity of sophisticated MIMO detection algorithms. In this paper, we propose new tree search-based detection algorithms that achieve maximum-likelihood (ML) performance under any given memory constraints and with reduced computational complexity. To this end, we make two main contributions. First, we propose a memory-constrained tree search (MCTS) algorithm that bridges the gap between the sphere decoding (SD) and stack algorithms. Our MCTS algorithm dynamically adapts to any pre-specified memory constraint and offers a graceful tradeoff between computational complexity and memory requirement while maintaining the ML performance. When the memory size is set as the minimum, our MCTS algorithm is similar to the SD algorithm. As the memory size increases, the average computational complexity of our MCTS algorithm decreases. When the memory size becomes large, our MCTS algorithm is similar to the stack algorithm, having similar average computational complexity but requiring significantly less memory. To further reduce the computational complexity of tree search-based ML detection algorithms, we propose novel ordering schemes that can be easily embedded in the QR decomposition and take into account both the channel matrix and the received signal (noise); simulation results show that our ordering schemes lead to reduced average computational complexity for the SD and MCTS algorithms, and the reduction is significant at low to medium signal-to-noise ratio region. </para>

Maximum likelihood detection with beat noise estimation for minimizing bit error rate in OCDM-based system

We propose a maximum likelihood detection (MLD) technique that incorporates beat noise estimation (BNE). MLD can minimize a bit error rate theoretically because a bit pattern with the maximum posteriori probability is selected as the detected signals. Also, BNE can extract a specific beat noise from mixed multiple signals using a correlation. By combining these techniques, the influence of beat noise is reduced and the bit error rate becomes lower in an OCDM-based system. This paper describes the MLD algorithm and the BNE design. And numerical simulation results confirm the validity and performance of this technique.

Generalized Parallel Interference Cancellation With Near-Optimal Detection Performance

The maximum-likelihood detection (MLD) algorithm is the optimal detection scheme for vertical Bell Laboratories layered space-time (V-BLAST) systems. However, it is generally infeasible to implement the MLD algorithm in practical systems because of its high computational complexity. In this paper, we used two parallel interference cancellation techniques, including precancellation and postcancellation, to simplify the MLD algorithm into a class of computationally efficient suboptimal detection algorithms, herein called the generalized parallel interference cancellation (GPIC) algorithm. Numerical analysis shows that the GPIC algorithm can approximately achieve the MLD performance with considerably low and stable computational complexity. Furthermore, it can be significantly accelerated via parallel processing methods. With near-optimal performance, low and stable complexity, and high running speed, the GPIC algorithm is very suitable for real-time communication systems.

Dynamic Nulling-and-Canceling for Efficient Near-ML Decoding of MIMO Systems

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> It is known that conventional nulling-and-canceling (NC) detection for multiple-input/multiple-output (MIMO) systems cannot exploit all of the available diversity, and, thus, its performance is significantly inferior to that of maximum likelihood (ML) detection. Conventional NC employs the layerwise postequalization signal-to-noise ratios (SNRs) as reliability measures for layer sorting. These SNRs are &lt;emphasis emphasistype="boldital"&gt;average&lt;/emphasis&gt; quantities that do not depend on the received vector. In this paper, we propose the novel &lt;emphasis emphasistype="boldital"&gt;dynamic nulling-and-canceling&lt;/emphasis&gt; (DNC) technique that uses approximate &lt;emphasis emphasistype="boldital"&gt;a posteriori&lt;/emphasis&gt; probabilities as measures of layer reliability. The DNC technique is a minimum mean-square error (MMSE) nulling scheme combined with an improved “dynamic” layer sorting rule that exploits the information contained in the current received vector. We calculate the error probability of DNC for a simple special case and show that it is upper bounded by the error probability of conventional NC. Simulation results are presented for spatial multiplexing systems and for systems using linear dispersion codes. It is demonstrated that the DNC technique can yield near-ML performance for a wide range of system sizes and channel SNRs at a fraction of the computational complexity of the sphere-decoding algorithm for ML detection. </para>

A new ML detection algorithm for Orthogonal Multicode system in Nakagami fading channel

Based on the Maximum-Likelihood (ML) criterion, this paper proposes a novel noncoherent detection algorithm for Orthogonal Multicode (OM) system in Nakagami fading channel. Some theoretical analysis and simulation results are presented. It is shown that the proposed ML algorithm is at least 0.7 dB better than the conventional Matched-Filter (MF) algorithm for uncoded systems, in both non-fading and fading channels. For the consideration of practical application, it is further simplified in complexity. Compared with the original ML algorithm, the simplified ML algorithm can provide significant reduction in complexity with small degradation in performance.

Blind channel estimation of space-time Frequency-Shift Keying

The decoupled coherent Maximum Likelihood (ML) detection algorithm presented in this letter can sharply reduce the complexity of the receiver as well as provide better error performance under the precondition that channel should be estimated first. Considering the bandwidth inefficiency of Frequency Shift Keying (FSK), the acquisition of channel state information through training sequences will further decrease the transmission efficiency. This letter presents a blind channel estimation algorithm based on noise subspace theory which can acquire channel information without any training symbols. The simulation shows that the algorithm brings about fewer channel estimation errors while the frequency efficiency can be increased.

Blind ML detection of orthogonal space-time block codes: efficient high-performance implementations

Orthogonal space-time block codes (OSTBCs) have attracted much attention owing to their simple code construction, maximal diversity gain, and low maximum-likelihood (ML) detection complexity when channel state information (CSI) is available at the receiver. This paper addresses the problem of ML OSTBC detection with unknown CSI. Focusing on the binary and quaternary PSK constellations, we show that blind ML OSTBC detection can be simplified to a Boolean quadratic program (BQP). From an optimization viewpoint the BQP is still a computationally hard problem, and we propose two alternatives for dealing with this inherent complexity. First, we consider the semidefinite relaxation (SDR) approach, which leads to a suboptimal, but accurate, blind ML detection algorithm with an affordable worst-case computational cost. We also consider the sphere decoding approach, which leads to an exact blind ML detection algorithm that remains computationally expensive in the worst case, but generally incurs a reasonable average computational cost. For the two algorithms, we study implementation methods that can significantly reduce the computational complexity. Simulation results indicate that the two blind ML detection algorithms are competitive, in that the bit error performance of the two algorithms is almost the same and is noticeably better than that of some other existing blind detectors. Moreover, numerical studies show that the SDR algorithm provides better complexity performance than the sphere decoder in the worst-case sense, and vice versa in the average sense.

Low-Complexity Maximum-Likelihood Decoder for Four-Transmit- Antenna Quasi-Orthogonal Space–Time Block Code

This letter proposes a very low-complexity maximum-likelihood (ML) detection algorithm based on QR decomposition for the quasi-orthogonal space-time block code (QSTBC) with four transmit antennas, called the LC-ML decoder. The proposed algorithm enables the QSTBC to achieve ML performance with significant reduction in computational load for any high-level modulation scheme.

Very-Low-Complexity Maximum-Likelihood Decoders for Four-Transmit-Antenna Quasi-Orthogonal Space-Time Code

This letter proposes two very-low-complexity maximum-likelihood (ML) detection algorithms based on QR decomposition for the quasi-orthogonal space-time code (QSTBC) with four transmit antennas [3]-[5], called VLCMLDec1 and VLCMLDec2 decoders. The first decoder, VLCMLDecl, can be used to detect transmitted symbols being extracted from finite-size constellations such as phase-shift keying (PSK) or quadrature amplitude modulation (QAM). The second decoder, VL-CMLDec2, is an enhanced version of the VLCMLDecl, developed mainly for QAM constellations. Simulation results show that both of the proposed decoders enable the QSTBC to achieve ML performance with significant reduction in computational load.

A Reduced-Complexity Signal Detection Scheme Employing ZF and K-Best Algorithms for OFDM/SDM

This paper proposes a reduced-complexity signal detection scheme for Orthogonal Frequency Division Multiplexing with Space Division Multiplexing (OFDM/SDM) systems that utilize Zero-Forcing (ZF) and K-best algorithms. It is known that Maximum Likelihood Detection (MLD) with exhaustive search achieves mathematically optimal performance for SDM signal detection. However, it also suffers from exponential computational complexity against the number of transmit antennas and modulation order. In order to reduce the computational complexity of MLD, we apply the K-best algorithm for signal detection. It is known that the K-best algorithm itself inherently reduces the computational complexity of MLD because it avoids exhaustive search. In this paper, we propose the modified K-best algorithm, which exploits the ZF algorithm for initial symbol estimation. This initial symbol estimation improves the decoding accuracy of the original K-best algorithm. We evaluate the performance of the proposed scheme through computer simulations. The computer simulation results show that the performance degradation from the MLD algorithm is suppressed to just 1 dB or so in terms of the required E b /N 0 for packet error rate (PER) = 10 -2 , When either 16 Quadrature Amplitude Modulation (16QAM) or 64QAM is applied with three transmit and three receive antennas. In these cases, 87% and 99% fewer metric computations are required than the MLD algorithm. It is confirmed that the proposed MLD algorithm offers a significant reduction in the computational complexity from the MLD algorithm while suppressing the performance degradation.

Near optimum joint detection for CTDMA system

A near optimum joint detection decision feedback maximum likelihood detection algorithm (DFMLD) is developed by the authors to support the recently proposed code time division multiple access (CTDMA) scheme for third generation mobile communication systems. The analytical and simulation study shows that the proposed detection scheme may be used to improve the spectra efficiency of CTDMA systems.

Alternative algorithm for maximum likelihood DOA estimation and detection

The majority of algorithms developed for the narrowband direction of arrival (DOA) estimation problem rely on an eigenvalue decomposition (EVD) to determine both the number of signals and their respective DOAs. An alternative algorithm is presented that solves both the DOA detection and estimation problems without resorting to an EVD. The algorithm is shown to have asymptotically equivalent performance to that of the (unconditional) maximum likelihood method, and hence it yields asymptotically minimum variance DOA estimates. The computational complexity required to update the DOA estimates in response to additional data from the array is investigated, and the algorithm is shown to be somewhat simpler than other methods with comparable performance. In addition, the asymptotic distribution of the algorithm's cost function is derived, and is shown to be composed of the sum of two differently scaled chi-squared random variables. A hypothesis test for determining the number of signals based on this result is then presented.

A fast maximum likelihood estimation and detection algorithm for Bernoulli-Gaussian processes

In this correspondence, we propose a fast maximum likelihood detection and estimation algorithm, called a multiple-mostlikely-replacement (MMLR) detector, for Bernoulli-Gaussian processes which are distorted by a linear time-invariant system and contaminated by a white Gaussian noise. This new detector works as well as the well-known single-most-likely-replacement (SMLR) detector. However, the former is computationally faster than the latter. We discuss two examples which demonstrate the computational advantage of the proposed algorithm using synthetic data.

MLD and mse algorithms for adaptive detection of digital signals in the presence of interchannel interference

Adaptive mean-square error (mse) and maximum-likelihood detection (MLD) algorithms for a dual-channel digital communication system in the presence of interchannel interference and white Gaussian noise are presented. The mse algorithm forms estimates of the transmitted symbols from a linear combination of received symbols using weights that minimize the mse between transmitted and estimated symbols. The nonlinear MLD algorithm minimizes the probability of symbol error by maximizing the probability of the received signal samples on the two channels over ail possible transmitted symbol pairs. The probability of error is derived for the two algorithms when quadrature phase-shift keying (QPSK) is used as a modulation technique, and is compared with that of a dual-channel QPSK system having no compensation for the crosstalk.