Candidate Symbols Research Articles

On the Detection of Multiple‐Access Spatial Modulations

The detection problem for the Multiple-access Spatial modulations (M-SM) is investigated in this paper, where multiple transmitters adopting spatial modulation communicate to the receiver at the same time. The optimal Maximum-likelihood (ML) detection suffers from the high computational complexity, while Sphere decoding (SD) can not reduce the complexity effectively because of the multiple active antennas in M-SM. In order to avoid the high complexity, a Space-alternating generalized expectation-maximization (SAGE) algorithm aided List-projection (S-LP) detector is proposed and applied to M-SM systems. The received signal vector is firstly projected onto the subspaces spanned by the columns of channel matrices corresponding to the possible active antennas. Then some combinations of antenna indices with the largest projections are selected as candidate index sets, based on which, a modified SAGE algorithm is applied to update the candidate symbols. Both analysis and simulation results show that the proposed S-LP detector achieves a near-optimum performance with a significantly reduced complexity compared with ML and SD detection.

ML Symbol Detection for MIMO Systems in the Presence of Channel Estimation Errors

In wireless communication, the multiple-input multiple-output (MIMO) system is a well-known approach to improve the reliability as well as the data rate. In MIMO systems, channel state information (CSI) is typically required at the receiver to detect transmitted signals; however, in practical systems, the CSI is imperfect and contains errors, which affect the overall system performance. In this paper, we propose a novel maximum likelihood (ML) scheme for MIMO systems that is robust to the CSI errors. We apply an optimization method to estimate an instantaneous covariance matrix of the CSI errors in order to improve the detection performance. Furthermore, we propose the employment of the list sphere decoding (LSD) scheme to reduce the computational complexity, which is capable of efficiently finding a reduced set of the candidate symbol vectors for the computation of the covariance matrix of the CSI errors. An iterative detection scheme is also proposed to further improve the detection performance.

Online recognition of sketched arrow-connected diagrams

We introduce a new, online, stroke-based recognition system for hand-drawn diagrams which belong to a group of documents with an explicit structure obvious to humans but only loosely defined from the machine point of view. We propose a model for recognition by selection of symbol candidates, based on evaluation of relations between candidates using a set of predicates. It is suitable for simpler structures where the relations are explicitly given by symbols, arrows in the case of diagrams. Knowledge of a specific diagram domain is used--the two domains are flowcharts and finite automata. Although the individual pipeline steps are tailored for these, the system can readily be adapted for other domains. Our entire diagram recognition pipeline is outlined. Its core parts are text/non-text separation, symbol segmentation, their classification and structural analysis. Individual parts have been published by the authors previously and so are described briefly and referenced. Thorough evaluation on benchmark databases shows the accuracy of the system reaches the state of the art and is ready for practical use. The paper brings several contributions: (a) the entire system and its state-of-the-art performance; (b) the methodology exploring document structure when it is loosely defined; (c) the thorough experimental evaluation; (d) the new annotated database for online sketched flowcharts and finite automata diagrams.

The Sarason sub-symbol and the recovery of the symbol of densely defined Toeplitz operators over the Hardy space

While the symbol map for the collection of bounded Toeplitz operators is well studied, there has been little work on a symbol map for densely defined Toeplitz operators. In this work a family of candidate symbols, the Sarason sub-symbols, is introduced as a means of reproducing the symbol of a densely defined Toeplitz operator. This leads to a partial answer to a question posed by Donald Sarason in 2008. In the bounded case the Toeplitzness of an operator can be classified in terms of its Sarason sub-symbols. This justifies the investigation into the application of the Sarason sub-symbols on densely defined operators. It is shown that analytic closed densely defined Toeplitz operators are completely determined by their Sarason sub-symbols, and it is shown for a broader class of operators that they extend closed densely defined Toeplitz operators (of multiplication type).

On-line structure-based handwritten Chinese phonetic symbol recognition

This paper proposes a high-accuracy on-line structure-based handwritten Chinese phonetic symbol recognition system. Each reference symbol consists of visible and hidden line segments, where two line segments intersecting each other form an X-shape structure, and those forming a crucial T-shape structure mark a T-shape structure. The first-stage recognition performs the structure-based dynamic programming to match the input handwriting with reference symbols. The second-stage recognition performs the partialmerge dynamic programming to reevaluate the similarity between the input handwriting and the candidate symbols in a confusing set. The third-stage recognition performs the structure-constrained confirmation to determine the best candidate.

Chaos Based IQ Encryption for PAPR Reduction and Security Enhancement in OFDMA PON System

This paper proposed a quadrature amplitude modulation (QAM) encryption technique in orthogonal frequency-division multiple access-based passive optical network (OFDMA-PON) systems. This scheme combines chaos based IQ-encryption with the SLM technique. The system can benefit of simultaneous low PAPR and high physical layer security. For IQ encrypting, the real (I) and imaginary (Q) parts of the QAM symbols are separately coded with key sequences, which are generated by a logistic map. A bank of OFDM symbol candidates is obtained by repeating this process. Then the candidate with the lowest PAPR is transmitted to the ONU sides. The iteration parameters of the mapping formula are set as the security keys, which are securely transmitted between OLT and ONUs. The research has utilized this method to successfully transmit 10 Gb/s 16QAM OFDM signal along 30km standard single mode fiber (SSMF).

Frequency-Domain Diversity Combining Receiver for ACO-OFDM System

This paper proposes a new receiver based on frequency-domain diversity combining (FDDC) for an asymmetrically clipped optical orthogonal frequency-division multiplexing (ACO-OFDM) system. Compared with its time-domain diversity combining (TDDC) counterpart, the FDDC receiver is capable of more effectively exploiting the frequency selectivity of the channel to further improve the detection performance, which is proved by postcombining signal-to-noise-ratio (SNR) analysis. An enhanced version of the FDDC receiver, which is called eFDDC, is also proposed subsequently. It allows for optimal selection among multiple sets of candidate symbol vectors based on signum matrix estimation. Simulation results show that the proposed FDDC and eFDDC receivers outperform the TDDC receiver. With a sufficient number of candidate symbol vector sets, the eFDDC receiver even outperforms a kind of iterative receiver, which is known to have superior performance in current literature, yet the complexity of eFDDC is lower due to the avoidance of matrix inversion. Those facts suggest that the FDDC and eFDDC receivers are strong receiver candidates for the ACO-OFDM system.

A reduced-complexity near-ML detection for broadband single-carrier transmission

In our recently proposed reduced-complexity near-maximum likelihood block signal detection employing QR decomposition and Malgorithm (QRM-MLBD) for broadband single-carrier (SC) block transmission, unreliable symbol candidates are removed from the symbol tree based on the hard-decision minimum mean square error based frequencydomain equalization (MMSE-FDE). In this paper, to further remove the unreliable symbol candidates and consequently reduce the computational complexity, we introduce a soft-decision MMSE-FDE based selection of reliable symbol candidates into reduced-complexity QRM-MLBD. It is shown that the soft-decision MMSE-FDE based selection significantly reduces the computational complexity while achieving a bit error rate (BER) performance similar to the original QRM-MLBD.

Efficient list‐sphere detection scheme for joint iterative multiple‐input multiple‐output detection

List-sphere detection (LSD) is a sub-optimal multiple-input multiple-output (MIMO) detection scheme which searches candidate symbol vectors that lie within a sphere of a given radius. This study presents an efficient LSD based method for a joint iterative MIMO detection scheme. The proposed method utilises a channel condition in order to define the list size. During the search process, the radius is adaptively updated to reduce the computational complexity. Owing to the list size and corresponding radius are adaptively determined by the channel condition, the authors can operate the detector at the most appropriate complexity to produce the required performance. Simulation results show that the proposed methods provide substantial complexity reduction without bit error rate performance degradation.

Maximum-Likelihood Detector for Differential Amplify-and-Forward Cooperative Networks

The exact maximum-likelihood (ML) detector for amplify-and-forward (AF) cooperative networks employing M-ary differential phase-shift keying (DPSK) in Rayleigh fading is derived in a single-integral form, which serves as a benchmark for differential AF networks. Two algorithms are then developed to reduce the complexity of the ML detector. Specifically, the first algorithm can eliminate a number of candidates in the ML search, while causing no loss of optimality of ML detection. In high signal-to-noise ratios (SNRs), this algorithm almost surely identifies a single candidate that amounts to the ML estimate of the signal. For low to medium SNRs with multiple candidates determined, we then derive an accurate closed-form approximation for the integral involved in the likelihood function, which only requires a five-sample evaluation per symbol candidate. Finally, combining these algorithms, we propose a closed-form approximate ML detector, which achieves an almost identical bit-error-rate (BER) performance to the exact ML detector at practical complexity. In particular, it is shown that the proposed approximate ML detector is far less complex than the well-known diversity combiner in high SNRs, while achieving approximately 1.7-dB gain in the 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> BER when the relay is closer to the destination.

MIMO 시스템에서 16-QAM의 Semi-Definite Relaxation에 기반을 둔 선형 검출 기법

This paper suggests the detecting method in which it uses the candidate symbol obtained through PI-SDR, the little computational complexity is required. By using the candidate symbol matrices obtained through PI-SDR, ZF and MMSE method was applied and the received signal was detected. The linear detecting method using PI-SDR candidate symbol is out of the performance than ML detecting method but the complexity is low. Because of using the symbol come close to the solution of ML, the proposed method`s performance is better than the existing ZF and MMSE method.

2-Step QRM-MLBD for Broadband Single-Carrier Transmission

Maximum likelihood block signal detection employing QR decomposition and M-algorithm (QRM-MLBD) can significantly improve the bit error rate (BER) performance of single-carrier (SC) transmission while significantly reducing the computational complexity compared to maximum likelihood detection (MLD). However, its computational complexity is still high. In this paper, we propose the computationally efficient 2-step QRM-MLBD. Compared to conventional QRM-MLBD, the number of symbol candidates can be reduced by using preliminary decision made by minimum mean square error based frequency-domain equalization (MMSE-FDE). The BER performance achievable by 2-step QRM-MLBD is evaluated by computer simulation. It is shown that it can significantly reduce the computational complexity while achieving almost the same BER performance as the conventional QRM-MLBD.

A New Reduced-Complexity Conditional-Mean Based MIMO Signal Detection Using Symbol Distribution Approximation Technique

In this correspondence, we propose a new conditional-mean based approach for a reduced-complexity suboptimal multiple-input multiple-output (MIMO) detector. In general, the optimal (error-minimizing) metric should take all possible symbol candidates into account and thus exhaustive computations are required. On the other hand, our new approach makes use of approximating the distributions of distinct symbol candidates by a continuous random variable such that the exhaustive summation is replaced by an integration that can be reduced to a simple closed form. The resulting metric depends only on a subset of symbol candidates and thus the overall complexity is reduced considerably. It is found that uniform ring approximation in combination with phase shift keying (PSK) and amplitude-modulated phase shift keying (APSK) achieves a performance close to that of maximum likelihood detection (MLD), while its complexity is a linear order of the modulation multiplicity when the number of transmit antennas is two.

An Efficient Soft-Output MIMO Detection Method Based on a Multiple-Channel-Ordering Technique

In this paper, we propose an efficient soft-output signal detection method for spatially multiplexed multiple-input multiple-output (MIMO) systems. The proposed method is based on the ordered successive interference cancellation (OSIC) algorithm, but it significantly improves the performance of the original OSIC algorithm by solving the error propagation problem. The proposed method combines this enhanced OSIC algorithm with a multiple-channel-ordering technique in a very efficient way. As a result, the log likelihood ratio values can be computed by using a very small set of candidate symbol vectors. The proposed method has been synthesized with a 0.13-μm CMOS technology for a 4×4 16-QAM MIMO system. The simulation and implementation results show that the proposed detector provides a very good solution in terms of performance and hardware complexity.

Quantization Error Correction Schemes for Lattice-Reduction Aided MIMO Detectors

Lattice reduction aided (LRA) linear detectors have been known to achieve near optimal performance at low complexity. However, one weakness of LRA detector is that the quantization step in LRA detector is not optimal. Based on simulation results, we show that most of detection errors in LRA linear detectors are due to quantization errors. We then propose two methods to correct the quantization errors. In the first method, sphere detectors are introduced to correct quantization errors at low additional complexity. As a second approach, we propose a list quantization scheme which can generate a list of candidate symbols from the original LRA estimated symbols. From these listed symbols, decisions are made according to the minimum Euclidean distance between the received and estimated points. It is shown by simulations that both methods provide significant BER performance improvements with only a small additional complexity.

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.

A Novel Multistage Group Detection Technique and Applications

In this letter, a multistage group detection (MSGD) approach to nonlinear equalization is proposed. At each stage, symbols are divided into groups, and maximum-likelihood detection (MLD) within the group is performed. The MLD process identifies a number of promising group values. It is constrained to only consider candidate symbol values identified in the previous stage. Groups are formed by combining groups from the previous stage. For example, symbols are detected one at a time, then two at a time, then four at a time, and so on. The last stage is a form of constrained maximum-likelihood sequence detection (MLSD). The overall demodulation process can be viewed as approximate MLSD, and results show that near-MLSD performance is possible with reasonable complexity. While the High Speed Downlink Packet Access (HSDPA) cellular system is used as an example, the approach can be applied other situations, including the Long Term Evolution (LTE) uplink and MIMO.

A new reduced complexity ML detection scheme for MIMO systems

For multiple-input multiple-output (MIMO) systems, the optimum maximum likelihood (ML) detection requires tremendous complexity as the number of antennas or modulation level increases. This paper proposes a new algorithm which attains the ML performance with significantly reduced complexity. Based on the minimum mean square error (MMSE) criterion, the proposed scheme reduces the search space by excluding unreliable candidate symbols in data streams. Utilizing the probability metric which evaluates the reliability with the normalized likelihood functions of each symbol candidate, near optimal ML detection is made possible. Also we derive the performance analysis which supports the validity of our proposed method. A threshold parameter is introduced to balance a tradeoff between complexity and performance. Besides, we propose an efficient way of generating the log likelihood ratio (LLR) values which can be used for coded systems. Simulation results show that the proposed scheme achieves almost the same performance as the ML detection at a bit error rate (BER) of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> with 28% and 15% of real multiplications compared to the conventional QR decomposition with M-algorithm (QRD-M) in 4-QAM and 16- QAM, respectively. Also we confirm that the proposed scheme achieves the near-optimal performance for all ranges of code rates with much reduced complexity. For instance, our scheme exhibits 74% and 46% multiplication reduction in 4-QAM and 16-QAM, respectively, compared to the sphere decoding based soft-output scheme with rate-1/2 convolutional code.

Low-Complexity Decoding via Reduced Dimension Maximum-Likelihood Search

In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood (ML) estimate of symbols by searching a partitioned symbol vector space rather than that spanned by the whole symbol vector. The inevitable performance loss due to a reduction in the search space is compensated by 1) the use of a list tree search, which is an extension of a single best searching algorithm called sphere decoding, and 2) the recomputation of a set of weak symbols, i.e., those ignored in the reduced dimension search, for each strong symbol candidate found during the list tree search. Through simulations on M-quadrature amplitude modulation (QAM) transmission in frequency nonselective multi-input-multioutput (MIMO) channels, we demonstrate that the RD-MLS algorithm shows near constant complexity over a wide range of bit error rate (BER) (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ~ 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> ), while limiting performance loss to within 1 dB from ML detection.

記号の接触を考慮した印刷五線譜の記号認識

To build a music database efficiently, an automatic score recognition system is a critical component. Many previous methods are applicable only to simple music scores. In the case of complex music scores, it is difficult to detect symbols correctly because of noise and connection between symbols included in the scores. In this paper, we propose a score recognition method that is applicable to complex music scores. Symbol candidates were detected by template matching. From these candidates correct symbols were selected by considering their relative positions and mutual connections. In the presence of noise and connected symbols, the proposed method outperformed “Score Maker”, which is optical music score recognition software.