Suboptimal Detection Algorithm Research Articles

Concurrent Transmission and Multiuser Detection of LoRa Signals

This article investigates a new model to improve the scalability of low-power long-range (LoRa) networks by allowing a group of multiple end devices (EDs) to communicate with multiple multi-antenna gateways simultaneously (i.e., in the same time slot) on the same frequency band and using the same spreading factor. The maximum-likelihood (ML) decision rule is first derived for noncoherent detection of information bits transmitted by multiple devices in a group. To overcome the high complexity of the ML detection, we propose a suboptimal two-stage detection algorithm to balance the computational complexity and error performance. In the first stage, we identify transmitted chirps (without knowing which EDs transmit them). In the second stage, we determine the EDs that transmit the specific chirps identified from the first stage. To improve the detection performance in the second stage, we also optimize the transmit powers of EDs to minimize the similarity, measured by the Jaccard coefficient, between the received powers of any pair of EDs in the same group. As the power control optimization problem is nonconvex, we use concepts from successive convex approximation to transform it to an approximate convex optimization problem that can be solved iteratively and guaranteed to reach a suboptimal solution. Simulation results demonstrate and justify the tradeoff between transmit power penalties and network scalability of the proposed LoRa network model. In particular, by grouping two or three EDs in each group for concurrent transmission, the uplink capacity of the proposed network can be doubled or tripled over that of a conventional LoRa network, albeit at the expense of additional 3.0 or 4.7 dB transmit power.

Low-Complexity GSM Detection Based on Maximum Ratio Combining

Generalized spatial modulation (GSM) technology is an extension of spatial modulation (SM) technology, and one of its main advantages is to further improve band efficiency. However, the multiple active antennas for transmission also brings the demodulation difficulties at the receiver. To solve the problem of high computational complexity of the optimal maximum likelihood (ML) detection, two sub-optimal detection algorithms are proposed through reducing the number of transmit antenna combinations (TACs) detected at the receiver. One is the maximum ratio combining detection algorithm based on repetitive sorting strategy, termed as (MRC-RS), which uses MRC repetitive sorting strategy to select the most likely TACs in detection. The other is the maximum ratio combining detection algorithm, which is based on the iterative idea of the orthogonal matching pursuit, termed the MRC-MP algorithm. The MRC-MP algorithm reduces the number of TACs through finite iterations to reduce the computational complexity. For M-QAM constellation, a hard-limited maximum likelihood (HLML) detection algorithm is introduced to calculate the modulation symbol. For the M-PSK constellation, a low-complexity maximum likelihood (LCML) algorithm is introduced to calculate the modulation symbol. The computational complexity of these two algorithms for calculating the modulation symbol are independent of modulation order. The simulation results show that for GSM systems with a large number of TACs, the proposed two algorithms not only achieve almost the same bit error rate (BER) performance as the ML algorithm, but also can greatly reduce the computational complexity.

A Novel Low Complexity Faster-Than-Nyquist Signaling Detector Based on the Primal-Dual Predictor-Corrector Interior Point Method

Faster-than-Nyquist signaling (FTNS) is a promising non-orthogonal physical layer transmission technique to improve the spectral efficiency of next-generation communication systems but at the expense of increased transciever complexity to remove the artificially introduced intersymbol-interference (ISI). In this paper, we investigate the detection problem of FTNS and propose a low-complexity sub-optimal detection algorithm, stemming in operations research, that is based on convex relaxation, primal-dual predictor-corrector interior point method, and quantization. The proposed convex quadratic relax-and-quantize sequence estimation (CQRAQSE) algorithm is suitable for low rates FTNS and demonstrates an excellent trade-off between the detection performance and computational effort.

Generalized Multistream Spatial Modulation With Signal Constellations Based on Hurwitz Integers and Low-Complexity Detection

This letter proposes two contributions to improve the performance of transmission with generalized multistream spatial modulation (SM). In particular, a modified suboptimal detection algorithm based on the Gaussian approximation method is proposed. The proposed modifications reduce the complexity of the Gaussian approximation method and improve the performance for high signal-to-noise ratios. Furthermore, this letter introduces signal constellations based on Hurwitz integers, i.e., a 4-D lattice. Simulation results demonstrate that these signal constellations are beneficial for generalized SM with two active antennas.

Power-Efficient Wideband Spectrum Sensing for Cognitive Radio Systems

This paper proposes a wideband spectrum sensing architecture by utilizing a one-bit quantization at the cognitive radio receiver. A window-based autocorrelation is utilized to provide the power spectral density of the quantized signal. Closed-form expressions are derived for the one-bit quantized correlation, where the critical impact of the ultralow resolution is quantified. It is shown that the introduced method can still provide enough information about the sparse spectrum even at low signal-to-noise ratios (SNR). A suboptimal detection algorithm is presented to sense whether individual subbands are occupied or vacant. To evaluate the detection algorithm, the probability of false alarm and probability of detection are simulated for various system parameters. The sensing performance and the accuracy of the offered expressions are justified through comparisons with respective results from computer simulations. When compared to other methods, in addition to the significant saving in power, results indicate that, with the proper parameter selection and SNR down to $-$ 12 dBs, the proposed method provides low complexity, low sensing period, and better performance for less sparsity levels even though an aggressive quantization has been applied.

Performance Evaluation of Non-redundant Error Correcting Scheme Using Logistic Chaotic Map

In this paper an efficient error-correcting scheme based on logistic chaotic map for non-coherent chaos communication system without redundancy is presented and its performance is evaluated. The scheme uses one chaotic generator to generate two sequences one for each data bit value in a way that the end of the current chaotic sequence sets the initial value of the next sequence of the same symbol. With this arrangement, successive chaotic sequences having the same chaotic dynamics are created for the purpose of error correction. A suboptimal detection algorithm based on shortest distance calculation between the received sequence and the chaotic trajectories over nth-dimension is used for detecting the transmitted symbol and performing the errors-correction. The simulation results show that the scheme offers improvement in Eb/N0 over the method before the error-correcting scheme and this improvement is increased as the trajectory dimension is increased. At bit-error probability of 10?3, a gain of 0.5 dB is obtained in Eb/No over the method before the error-correcting scheme when the dimension is 4. The power point in the scheme is that error correction is based on chaotic dynamics and no redundant bits are needed. This would make the scheme a good candidate for applications that require high data transmission rates.

Denoising Detection for the Generalized Spatial Modulation System Using Sparse Property

Generalized spatial modulation (GSM) is a novel scheme developed from the conventional single-active antenna spatial modulation (SA-SM). The challenge of the GSM lies at the receiving end. Since more than one antenna is activated, the complexity of ML detection for GSM is much higher than that for SA-SM, especially if the number of antennas is large. Low complexity sub-optimal detections, such as ZF detection or MMSE detection, can be used to detect GSM. However, the performance of these detections is poor and cannot yet be applied in an underdetermined system. Spatial modulation has an inherent property of sparsity. However, to the best of our knowledge, this property has not been used for GSM. In this paper, we exploit this property and propose a sub-optimal detection algorithm. The traditional algorithm using sparsity is sensitive to noise and not suitable for the application to communication systems. Therefore, we employ a denoising scheme to counter the effect of the background noise.

Sequential Monitoring of Water Distribution Network

Monitoring drinking water is a public health problem because water is essential for human life. A typical modern drinking water distribution system includes a water purification plant, water towers, water tanks, pumps and pipes. Many procedures are developed for monitoring water quality in water treatment plants. Monitoring of water distribution systems has received less attention.The goal of this paper is to study the problem of drinking water safety by ensuring the monitoring of the distribution network from water tower to private residences. The proposed approach is based on the observation of residual chlorine concentrations which are provided by sensors network. By using the hydraulic model, it is possible to compute the nominal residuals chlorine concentrations under assumption that quality of the water at the water tower output is known. If a significant reduction of the residual chlorine concentrations is observed then it can be interpreted as presence of a dangerous water contamination. This event should be detected as reliable as possible provided an acceptable detection delay and false alarm rate. The traditional abrupt change detection theory is well developed in the case when the duration of the post-change mode is infinite. Unfortunately, in the discussed case the transient signal duration is short. Hence, the usage of traditional change detection algorithm is compromised.A criterion based on the minimization of the missed detection probability provided that the false alarm rate is upper bounded is used in the paper. A suboptimal detection algorithm is designed. The theoretical analysis and the results of simulation are provided.

Low-Complexity MAP-Based Successive Data Detection for Coded OFDM Systems Over Highly Mobile Wireless Channels

This paper is concerned with the challenging and timely problem of data detection for coded orthogonal frequency-division multiplexing (OFDM) systems in the presence of frequency-selective and very rapidly time varying channels. New low-complexity maximum a posteriori probability (MAP) data detection algorithms are proposed based on sequential detection with optimal ordering (SDOO) and sequential detection with successive cancellation (SDSC). The received signal vector is optimally decomposed into reduced dimensional subobservations by exploiting the banded structure of the frequency-domain channel matrix whose bandwidth is a parameter to be adjusted according to the speed of the mobile terminal. The data symbols are then detected by the proposed algorithms in a computationally efficient way by means of the Markov chain Monte Carlo (MCMC) technique with Gibbs sampling. The impact of the imperfect channel state information (CSI) on the bit error rate (BER) performance of these algorithms is investigated analytically and by computer simulations. A detailed computational complexity investigation and simulation results indicate that, particularly, the algorithm based on SDSC has significant performance and complexity advantages and is very robust against channel estimation errors compared with existing suboptimal detection and equalization algorithms proposed earlier in the literature.

Signal position-based adaptive QR decomposition-based M detection algorithm for multiple-input multiple-output systems

The QR decomposition-based M (QRD-M) algorithm is a suboptimal detection algorithm that achieves near-maximum-likelihood detection (MLD) performance with low complexity. Some conventional QRD-M algorithms that directly determine the number of surviving paths have simple structures and are consequently cost effective and easy to implement. However, it is possible that surviving paths are chosen that have unnecessarily low reliability. The authors propose an algorithm, termed signal position-based QRD-M, to solve this problem using position estimation of the received signal. The position estimation process is accomplished using the most reliable candidate symbol to minimise additional processing. Computer simulations demonstrated that the proposed algorithm can achieve near-MLD performance with lower complexity than the fixed QRD-M algorithm.

Low-complexity joint data detection and channel equalisation for highly mobile orthogonal frequency division multiplexing systems

This study is concerned with the challenging and timely problem of channel equalisation and data detection for orthogonal frequency division multiplexing (OFDM) systems in the presence of frequency-selective and very rapidly time-varying channels. The algorithm is based on the space alternating generalised expectation-maximisation (SAGE) technique which is particularly well suited to multicarrier signal formats and can be easily extended to multi-input multi-output-OFDM systems. In fast fading channels, the orthogonality between subcarriers is destroyed by the time variation of a fading channel over an OFDM symbol duration which causes severe inter-carrier interference (ICI) and, in conventional frequency-domain approaches, results in an irreducible error floor. The proposed joint data detection and equalisation algorithm updates the data sequences in series leading to a receiver structure that also incorporates ICI cancellation, enabling the system to operate at high vehicle speeds. A computational complexity investigation as well as detailed computer simulations indicate that this algorithm has significant performance and complexity advantages over existing suboptimal detection and equalisation algorithms proposed earlier in the literature.

Adaptive iterative detectors for phase-uncertain channels via variational bounding

The problem of iterative detection/decoding of data symbols transmitted over an additive white Gaussian noise (AWGN) channel in the presence of phase uncertainty is addressed in this paper. By modelling the phase uncertainty either as an unknown deterministic variable/process or random variable/ process with a known a priori probability density function, a number of non-Bayesian and Bayesian detection algorithms with various amount of suboptimality have been proposed in the literature to solve the problem. In this paper, a new set of suboptimal iterative detection algorithms is obtained by utilizing the variational bounding technique. Especially, applying the generic variational Bayesian (VB) framework, efficient iterative joint estimation and detection/decoding schemes are derived for the constant phase model as well as for the dynamic phase model. In addition, the relation of the VB-based approach to the optimal noncoherent receiver as well as to the classical approach via the expectation-maximization (EM) algorithm is provided. Performance of the proposed detectors in the presence of a strong dynamic phase noise is compared to the performance of the existing detectors. Furthermore, an incremental scheduling of the VB (or EM) algorithm is shown to reduce the overall complexity of the receiver.

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.

A suboptimal bootstrap method for structure detection of non-linear output-error models with application to human ankle dynamics

Identification of non-linear systems involves estimating unknown parameters and structure detection, selection of a subset of candidate terms that best describe the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. For non-linear systems simple output additive noise can generate multiplicative terms between the input, output and noise. The terms associated with noise need to be modelled to obtain unbiased parameter estimates, significantly increasing the number of candidate terms to be estimated and considered. In special cases, it may be possible to use an output error (OE) model structure and the instrumental variable (IV) estimator to obtain unbiased parameters without modelling the noise. This significantly reduces the dimensionality of the structure computation problem. Therefore, in this paper, we propose a suboptimal bootstrap structure detection (SOBSD) algorithm for non-linear OE models. Performance of this SOBSD algorithm was evaluated by using it to estimate the structure of (i) a simulated NARMAX model describing ankle dynamics and (ii) application to experimental data. The results demonstrate that the SOBSD method is simple to use and provides good results for non-linear OE models.

Optimal speed of detection in generalized Wiener disorder problems

We define a general Wiener disorder problem in which a sudden change in a time profile of unknown size has to be detected in white noise of small intensity. Since both the time of the change and its size are unknown, this problem is considerably harder than standard Wiener disorder problems where the size of the change is assumed to be known a priori. We formulate the problem within the Bayesian framework of nonlinear filtering theory, and use Strassen's functional law of the iterated logarithm to bound stochastic measures which arise in the nonlinear filtering equations. This leads to explicit expressions for the detection delay in the optimal statistics for small noise intensities, and we indicate how these can be used to analyse the detection delays of recursive suboptimal detection algorithms for this problem.

Reliable detection of faults in measurement systems

The goal of this paper is to discuss two special aspects of fault detection in measurement systems. First, the main goal of fault detection in measurement systems is to detect the system degradation when it leads to an unacceptable growth of the output errors. All other faults are of no importance; moreover, their detection can be considered by the user as a false alarm. Second, the traditional on-line fault detection methods are formulated as that of the quickest detection of abrupt changes in a stochastic process. A sensor fault also should be detected quickly but sometimes (especially for safety-critical applications) the user imposes the constraint on the maximum delay for the detection. Specifically, the user fixes the probability of missed detection within a given time-to-alarm. A new performance criterion for this reliable detection of changes in stochastic systems is proposed and two suboptimal change detection algorithms are considered in the paper. The application of these algorithms as Receiver Autonomous Integrity Monitoring (RAIM) schemes to the problem of the Global Positioning Systems (GPS) integrity monitoring is discussed — these self-contained schemes are of great interest today for many safety-critical applications. Copyright © 2000 John Wiley & Sons, Ltd.

Sub-optimal solutions to track detection problem using graph theoretic concepts

This paper demonstrates how the problem of tracking targets, which appear as either straight or curved lines in two-dimensional display images (or data images) can be formulated in terms of a directed weighted graph model and how dynamic programming techniques can be efficiently applied to reach an optimal or sub-optimal solution. In general, track detection algorithms providing optimal solutions have good detective ability, but most of them suffer from the inability to detect discontinuous lines or to resolve efficiently pairs of crossing lines. A sub-optimal solution is provided that efficiently overcomes these weaknesses. We focus on modeling the track detection problem in terms of a graph, formulating fast sequential/parallel sub-optimal track detection algorithms and testing them on simulated data in order to show their detective ability. Moreover, we specify the conditions under which sub-optimal algorithms can perform at least as well as their corresponding optimal algorithms. This is significant for the track detection problem where fast, accurate and real-time detection is considered a necessity.

On the application of sequence estimation algorithms in the digital compact cassette (DCC)

In the current digital compact cassette (DCC) recorder, a straightforward threshold detection scheme is applied which does not exploit the distance properties of the 8-to-10 modulation (ETM) code. More advanced detection schemes are investigated to see if they improve system performance. Two soft decision sequence estimation algorithms are considered: a fully-fledged Viterbi algorithm realizing maximum likelihood sequence estimation (MISE) and a suboptimal trellis detection algorithm. With either of the algorithms, an asymptotic gain of 3 dB versus threshold detection level in the presence of additive white Gaussian noise (AWGN) is expected. The suboptimal detection algorithm is based on the running digital sum (RDS) trellis underlying the ETM code. Performing the suboptimal algorithm requires a minimum of 48 additions, 19 comparisons, and a single table look-up for both detection and decoding of an ETM codeword, versus a minimum of 4234 additions, 421 comparisons, and 423 table look-ups for the fully-fledged algorithm. Compared to the fully-fledged scheme, the suboptimal algorithm leads to a large reduction in computational complexity. The performance of either of the algorithms in the presence of AWGN is determined by means of a computer simulation. The fully-fledged Viterbi algorithm achieves a gain of about 2.3 dB versus threshold detection level for symbol error rates in the order of 10/sup -4/ to 10/sup -5/. >

A comparison of suboptimal detection algorithms applied to the additive mix of orthogonal sinusoidal signals

A comparison is made of the performance of three algorithms for suboptimal signal detection applied to an additive mix of orthogonal sinusoidal signals. The study considers zero-mean Gaussian noise and a number of different impairments found in related data communications media, including quantization effects, frequency-selective fading, and frequency shifts. It is shown that different procedures ran differently in the presence of different impairments, so that the final selection of the procedure to be implemented will depend on the characteristics of the communications environment. >