Maximum Likelihood Detection Research Articles

A Reduced-Complexity Maximum Likelihood Detection with A Sub Optimal Ber Requirement

Maximum likelihood (ML) detection is an optimal signal detection scheme, which is often difficult to implement due to its high computational complexity, especially in a multiple input multiple-output (MIMO) scenario. In a system with Nt transmit antennas employing M-ary modulation, the ML-MIMO detector requires MNt cost function (CF) evaluations followed by a search operation for detecting the symbol with the minimum CF value. However, a practical system needs the bit-error ratio (BER) to be application-dependent which could be sub-optimal. This implies that it may not be necessary to have the minimal CF solution all the time. Rather it is desirable to search for a solution that meets the required sub-optimal BER. In this work, we propose a new detector design for a SISO/MIMO system by obtaining the relation between BER and CF which also improves the computational complexity of the ML detector for a sub-optimal BER.

A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems.

In this paper, we revisit a two-user space-time line coded uplink non-orthogonal multiple access (STLC-NOMA) system for Internet-of-things (IoT) networks and propose a novel low-complexity STLC-NOMA system. The basic idea is that both IoT devices (stations: STAs) employ amplitude-shift keying (ASK) modulators and align their modulated symbols to in-phase and quadrature axes, respectively, before the STLC encoding. The phase distortion caused by wireless channels becomes compensated at the receiver side with the STLC, and thus each STA’s signals are still aligned on their axes at the access point (AP) in the proposed uplink STLC-NOMA system. Then, the AP can decode the signals transmitted from STAs via a single-user maximum-likelihood (ML) detector with low-complexity, while the conventional uplink STLC-NOMA system exploits a multi-user joint ML detector with relatively high-complexity. We mathematically analyze the exact BER performance of the proposed uplink STLC-NOMA system. Furthermore, we propose a novel expectation-maximization (EM)-based blind energy estimation (BEE) algorithm to jointly estimate both transmit power and effective channel gain of each STA without the help of pilot signals at the AP. Somewhat interestingly, the proposed BEE algorithm works well even in short-packet transmission scenarios. It is worth noting that the proposed uplink STLC-NOMA architecture outperforms the conventional STLC-NOMA technique in terms of bit-error-rate (BER), especially with high-order modulation schemes, even though it requires lower computation complexity than the conventional technique at the receiver.

Detection of Data over wireless mobile channels based on Maximum Likelihood Technique

Next generation wireless systems are characterized by very high transmission bit rates which gives rise to severe Intersymbol interference (ISI) and this makes the detection process very challenging. Hence, assessment of performance of near-optimal detectors like Near Maximum Likelihood Detectors (NMLD) over such channels assumes great importance. This paper deals with the detection of data in the presence of Noise and ISI with NMLD. Performance improvement of NMLD, as compared to nonlinear equalization, has been assessed in terms of BER versus SNR curves obtained through computer simulation. A number of different cases of mobile radio channels have been simulated in this work. In current world the lot of congestion leads severe fading and interference in the channel which may give the pause in data streaming and call drop in voice calling. These channels have different number of reflected paths with different power distribution in the respective paths. The aim of this paper is to take a ‘worst case’ model of a mobile radio channel in terms of rapidity of fading and ISI, and then to investigate the performance of detectors in the receivers.

Optimal Low Complexity Detector for Signed-Quadrature Spatial Modulation MIMO System

Very recently, signed quadrature spatial modulation (sQSM) is developed as a competent technique that expands the spatial constellation diagram of QSM system by adding a bipolar dimension. Despite the enhanced spectral efficiency, the existing optimal maximum likelihood (ML) presents a serious computational challenge for large scale sQSM systems. Therefore, developing a reduced complexity detector for sQSM schemes is of significant importance to enable implementing and enjoying the inherent advantages of this promising system. Toward this end, a Tree Search (TS) optimal low complexity detector, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSopt</i> , for sQSM Multiple Input Multiple Output (MIMO) system is proposed and analyzed in this paper. The proposed detector expands the computationally complex ML detector for sQSM into a tree-structure representation. The idea of the suggested algorithm is to employ an efficient searching strategy that can expeditiously find the branch corresponding to the minimum error without tracing the entire nodes as in the ML case. It is reported that the proposed TSopt algorithm achieves the exact error performance as ML detector but with substantial reduction in computational complexity. Besides, complexity analysis in terms of the number of visited nodes of the TSopt algorithm is analyzed and a closed-form expression for the expected complexity at high SNR values is derived. Reported results disclose agreement between simulation and expected analytical complexity with substantial gains of around 60–80% in complexity reduction for different system parameters.

Evaluation of Conventional and Imaging MIMO OWC Systems Using Linear Array Design

Multiple-input-multiple-output (MIMO) optical wireless communication systems are potential candidates for high-speed data transmission in indoor environments. In this work, we present an experimental evaluation of two different MIMO systems, i.e., conventional-MIMO (c-MIMO) and imaging-MIMO (i-MIMO) in terms of design considerations. Both systems use the same transmitter configuration featuring a one-dimensional (light-emitting diode) LED transmitting array. On the receiver side, the c-MIMO system employs a similar one-dimensional receiving avalanche photodetector (APD)-based array, whereas the i-MIMO system uses a customized imaging camera-based receiver design. To assess the performance of the two systems, the channel matrix and the bit-error rate are calculated based on the experimental data. At distances shorter than 50 m, the proposed c-MIMO system demonstrates a good transmission performance, particularly when the maximum likelihood detection (MLD) technique is applied. Whereas, at long distances ranging from 40 m to 65 m, the proposed i-MIMO system can ensure an error-free transmission due to the efficient spatial separation between the emitted optical beams. Furthermore, the experimental results confirm that our systems have the potential for high data rates over longer distances in indoor environments.

Threshold Regions in Frequency Estimation

This article addresses the problem of threshold region characterization of the maximum likelihood (ML) sinusoid frequency estimation. We first study on the exact analytical expression of the probability of detection for the ML mean square error for all the signal-to-noise ranges. Then, we propose a simple asymptotic expression to this ML detection probability and propose a model for the characterization of the variance of the frequency estimation. We also provide asymptotic closed-form expressions to the threshold and the no-information signal-to-noise ratio breakdowns for the ML frequency estimation, respectively. Outcomes of extensive numerical simulations verify our proposed theoretical derivations.

A Low Complexity Two-Stage LLR Detector for Downlink OFDM-IM NOMA

OFDM-IM based Non-Orthogonal Multiple Access is a recently developed scheme, where flexibility is provided by both NOMA and OFDM-IM with an adjustable power allocation coefficient and an adjustable subcarrier activation ratio, respectively. However, the computational complexity of the Maximum Likelihood (ML) detector, used in the OFDM-IM NOMA scheme is very high. In this letter, a low complexity “two-stage Log-Likelihood Ratio (LLR)” detector is proposed for the OFDM-IM NOMA scheme. Computer simulation results and complexity analysis verify that the proposed detector performs almost the same as the ML-based detector in all the various conditions, with much reduced computational complexity.

Reordered Amplitude Phase Shift Keying Aided Differential Spatial Modulation: DFDD-Based Low-Complexity Detector and Performance Analysis Over Fading Channels

Differential spatial modulation (DSM) is a multiple input multiple output (MIMO) wireless transmission technique that achieves additional data bit transmission by utilizing antenna activation indexes. Combined with amplitude phase shift keying (APSK) constellations, the spectrum efficiency of DSM system can be further improved. However, the theoretically optimal maximum likelihood (ML) detection suffers from a high complexity that grows exponentially with the number of transmit antennas (TAs). Besides, the imperfect channel state information in fast fading channels leads to a severe deterioration of the detection performance. To address these problems, we propose a novel transmission scheme named reordered amplitude phase shift keying aided differential spatial modulation (RAPSK-DSM) and its low-complexity decision-feedback differential detection (DFDD) based detection algorithm. The proposed RAPSK-DSM scheme provides a better solution for addressing the error propagation problem and its detector utilizes the preceding channel gain vectors from previous detection results to mitigate the performance loss caused by time-selective channel fading. Furthermore, by decomposing and pre-computing the detection process, the complexity of proposed detector is effectively reduced due to the avoidance of redundant computations. Numerical analyses and simulation results show that the proposed RAPSK-DSM system is able to achieve substantial bit error rate (BER) performance improvement under fast fading channel conditions at the expense of low complexity.

Low-Complexity Detection for Multidimensional Codebooks Over Fading Channels

Signal space diversity (SSD) introduced by Boutros and Viterbo tremendously improves the error performance over fading channels without any power or bandwidth sacrifice. Maximum benefit is obtained with full diversity (FD) multidimensional codebooks and the rather complex maximum likelihood (ML) detection. In the present work, we propose a generalized combinatorial representation and an associated low-complexity list-based detection algorithm that works for any non-full or full diversity multidimensional codebook. The algorithm includes three options of lists offering different complexity-performance trade-offs. One of the lists has random size and is guaranteed to contain the ML point, while the other two lists have fixed or limited size. Some analytical results are presented for the list sizes. The algorithm also includes a smart search to find the most probable codebook point in the list. Numerical results show that the proposed algorithm yields optimal or close-to-optimum performance with a noteworthy detection complexity reduction. Therefore, its adoption can be of practical interest, especially in applications where powerful error-correcting codes are not supported.

The Multi-Channel Maximum-Likelihood (MCML) method for infrasound detection and wave parameter estimation: Evaluation, implementation, and first applications

The detection capability of the International Monitoring System (IMS) infrasound network is a key concern since ubiquitous coherent noise interfere with the identification of explosive events. The need to distinguish between incoherent wind noise and real coherent infrasonic wave signals has motivated the development of a new parametric stochastic model. We establish the expressions of both the Generalized Likelihood Ratio Test (GLRT) and the Maximum Likelihood Estimator (MLE). The major results are the expression of the asymptotic distribution of the GLRT under null hypothesis, leading to the p-value computation, and the expression of the asymptotic covariance. The Multi-Channel Maximum-Likelihood (MCML) detection is implemented in the time-frequency domain in order to discriminate between interfering signals. Extensive simulations with synthetic signals show that MCML outperforms the state-of-the-art multi-channel correlation detector algorithms in poor signal-to-noise ratio scenarios. We illustrate the performance of MCML on real IMS data, highlighting its capability to characterize the coherent ocean ambient infrasound noise in high resolution through comparisons with theoretical state-of-the-art numerical wave models as implemented at the LOPS research unit of IFREMER in the DATARMOR HPC center.

Efficient QAM Signal Detector for Massive MIMO Systems via PS/DPS-ADMM Approaches

In this paper, we design two efficient quadrature amplitude modulation (QAM) signal detectors for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM). The content of the paper is summarized as follows: first, we transform the maximum-likelihood detection model to a non-convex sharing optimization problem for massive MIMO-QAM systems, where a high-order QAM constellation is decomposed to a sum of multiple binary variables, integer constraints are relaxed to box constraints, and quadratic penalty functions are added to the objective function to result in a favorable integer solution; second, a customized ADMM algorithm, called PS-ADMM, is presented to solve the formulated non-convex optimization problem. In the implementation, all variables in each vector can be solved analytically and in parallel; and third, in order to solve the penalty-sharing distributively, we improve the proposed PS-ADMM algorithm to a distributed one, named DPS-ADMM. In the end, performance analyses of the proposed two algorithms, including convergence properties and computational cost, are provided. Simulation results demonstrate the effectiveness of the proposed approaches.

The multichannel maximum-likelihood (MCML) method: a new approach for infrasound detection and wave parameter estimation

SUMMARY We are presenting a new and novel approach to the detection and parameter estimation of infrasonic signals. Our approach is based on the likelihood function derived from a multisensor stochastic model expressed in different frequency channels. Using the likelihood function, we determine, for the detection problem, the generalized likelihood ratio (GLR) and for the estimation of the slowness vector, the maximum likelihood estimation (MLE). We establish new asymptotic results (i) for the GLR under the null hypothesis leading to the computation of the corresponding p-value and (ii) for the MLE by focusing on the two wave parameters: backazimuth and horizontal trace velocity. The multichannel maximum-likelihood (MCML) detection and estimation method is implemented in the time–frequency domain in order to avoid the presence of interfering signals. Extensive simulations with synthetic signals show that MCML outperforms the state-of-the-art multichannel correlation detector algorithms like the progressive multichannel correlation in terms of detection probability and false alarm rate in poor signal-to-noise ratio scenarios. We also illustrate the use of the MCML on real data from the International Monitoring System and show how the improved performances of this new method lead to a refined analysis of events in accordance with expert knowledge.

MIMO spread spectrum GFDM communication systems with index modulation

SummaryIn the fourth‐generation communication system (4G), orthogonal frequency division multiplexing (OFDM) is widely used. However, due to the cyclic prefix (CP) being added, the low spectrum efficiency and high out‐of‐band radiation (OOB) cause OFDM not to be suitable for the beyond fifth‐generation (B5G) communication system. Therefore, to meet the rigorous demands in B5G for the Internet of Things (IoT) and massive machine‐type communications (MMTC), generalized frequency division multiplexing (GFDM) technology has received significant attention. On the other hand, index modulation (IM) can provide flexible settings of different parameters to achieve the ideal system goals. Therefore, this paper considers the combination of spread spectrum (SS) and IM under the framework of GFDM, called SS‐IM‐GFDM. In this scheme, the information bits are jointly conveyed by the indices of spreading codes and the conventional M‐ary modulated symbols, which increase the spectrum efficiency and improve the bit error rate performance. Furthermore, a low‐complexity maximum likelihood (ML) detector is proposed to recover the transmitted signal effectively at the receiver. Finally, the space‐time block code (STBC) is employed to construct the multi‐input multi‐output SS‐GFDM‐IM (MIMO SS‐GFDM‐IM) system to improve the system's reliability. The simulation results show that the proposed scheme is superior to the existing systems.

Low‐complexity and robust MLP‐mixer aided detector design based on deep learning for differential subcarrier index modulation

Differential subcarrier index modulation (DSIM) system provides resource efficient information transmission without the need of channel state information and equalisation. However, maximum likelihood detector applied in DSIM possesses high complexity, which obstructs its practical application. To address this issue, in this letter, an intelligent detector is proposed to learn the transmission patterns of DSIM to demodulate the received signals with lower complexity. In this design, the received signals are pre-processed based on the domain knowledge at first, and then input to the deep neural network composed of multi-layer perceptrons for further bit data estimation. Complexity analysis is provided to demonstrate the superiority of the proposed design, and simulation results validate that the intelligent detector can achieve similar average bit error rate with higher robustness compared with the benchmark system.

Spatial time index non‐orthogonal multiple access for beyond 5G wireless networks

AbstractMultiple input multiple output (MIMO) based non‐orthogonal multiple access (NOMA) has been an evolving technique for 5G and beyond 5G wireless networks in the last decade. The goal of the proposed technique is to achieve improved spectral efficiency, with reduced complexity in the transceiver design. A novel time‐based spatial modulation‐MIMO based NOMA (SM‐MIMO‐NOMA) known as space time index‐based NOMA (STIN) has been proposed, which brings down the number of RF chains used at the transmitter end and improves the spectral efficiency in terms of bits per channel use. In the STIN algorithm, Joint GPS enabled channel estimation and threshold rate‐based power allocation are performed for user clustering and enhancing capacity. The transmit information bits are conveyed by the active antenna index as well as amplitude phase modulation by incorporating the block user‐symbol ratio at the base station. On the receiver side, user‐timeslot‐based successive interference cancellation with a maximum likelihood detector is implemented in the user equipment. STIN improves the bits per channel use with increasing modulation order and number of users by 33%. This novel algorithm has shown better performance in comparison with the existing NOMA‐based spatial modulation techniques in terms of average bit error rate, spectral efficiency, and complexity.

Low complexity deep neural network based transmit antenna selection and signal detection in SM-MIMO system

In this paper, with the investigation of the conventional Euclidean distance based transmit antenna selection (ED-AS) and the maximum likelihood detection (MLD) in spatial modulation (SM) multiple-input multiple-output (MIMO) system, we propose the deep neural network (DNN) based transmit antenna selection (DNN-AS) and signal detection (DNN-SD), respectively, to effectively balance the system performance and the complexity. For the proposed DNN-AS, we transform the existing problem of AS into a prediction problem and design a low dimension multi-output classifier to achieve the low complexity solution of AS. For the DNN-SD, we present two sub-DNNs to recover the transmitted SM signal. Numerical results reveal that the proposed DNN-AS scheme gets a better average bit error rate (ABER) performance than the typical AS schemes in SM-MIMO system with lower complexity. In contrast to the ED-AS approach, it attains the optimal and suboptimal ABER performance at low-and-moderate signal-to-noise ratio (SNR) region and high SNR region, respectively. The proposed DNN-AS scheme achieves obvious SNR gains compared with the conventional SM system and gets about 3.5 dB gains over the typical maximum-norm based AS (Norm-AS) algorithm. Furthermore, the proposed DNN-SD scheme obtains a superior detection performance compared with the conventional linear detection methods and provides the same ABER performance as the optimum MLD scheme in the presence of correlated noise.

Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis

This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals’ dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve.

Envelope Distribution of Two Correlated Complex Gaussian Random Variables and Application to the Performance Evaluation of RIS-Assisted Communications

The operation of point-to-point communication assisted with reconfigurable intelligent surface, where the multi-hop channels between the source and the destination are subject to Rician fading but are not necessarily independent, is investigated. Considering binary phase-shift keying signaling for the data transmission and optimal maximum-likelihood detection at the receiver, a system performance evaluation is conducted in terms of reliability and capacity. For such, the distributions of the magnitude of the product of the complex channel gains are first obtained for both, the case when the channel gains are independent and the case when they are correlated. Subsequently, expressions of the system’s error rate and the upper-bounds on the average capacity are obtained for the said correlated and uncorrelated channel cases. Numerical results corroborating the analysis are also presented. In particular, it is revealed that correlation between the channels has negligible effect on the error rate when the fading is Rayleigh distributed, and that an increase in the value of the Rician parameter pertaining to the channel between the receiver pair has reduced impact on the reduction of the error rate, which provides practical guidelines for system deployments with high reliability.

Diversity analysis of ML and ZF detectors in physical layer network coding relay system over frequency selective channels

Diversity analysis of ML and ZF detectors in physical layer network coding relay system over frequency selective channels

Clinical criteria for Mucosal Leishmaniasis diagnosis in rural South America: A systematic literature review.

BackgroundMucosal Leishmaniasis (ML), a neglected tropical disease caused by Leishmania parasites, impairs the quality of life of under-resourced populations in South America. If not treated promptly, this disease progresses to facial deformities and death. The low sensitivity of microscopy results and the unavailability of other accurate tests hamper the diagnosis. As clinical criteria are readily available in any setting, these may be combined in a syndromic algorithm, which in turn can be used as a diagnostic tool. We explore potential clinical criteria for a syndromic diagnostic algorithm for ML in rural healthcare settings in South America.Methodology/Principal findingsThe protocol for this systematic review was pre-registered in PROSPERO with the number: CRD42017074148. In patients with ML, described in case series identified through a systematic retrieval process, we explored the cumulative ML detection rates of clinical criteria. Participants: all patients with active mucosal disease from an endemic area in South America. Any original, non-treatment study was eligible, and case reports were excluded. PUBMED, EMBASE, Web of Science, SCIELO, and LILACS databases were searched without restrictions. The risk of bias was assessed with the JBI checklist for case series. We included 10 full texts describing 192 ML patients. Male gender had the highest detection rate (88%), followed by ulcer of the nasal mucosa (77%), age >15 (69%), and symptom duration >4 months (63%).SignificanceWithin this selection of patients, we found that the male gender, ulcer of the nasal mucosa, age >15, and symptom duration >4 months lead to the highest detection rates. However, higher detection comes -naturally- with a higher rate of false positives as well. As we only included ML patients, this could not be verified. Therefore, the criteria that we found to be most promising should be validated in a well-designed prospective study.