Sphere Detection Research Articles

A Low-Complexity Interference-Aware Sphere Detector for Imperfect Channel Estimates

In practical applications, an imperfect channel estimate over a noisy channel is inevitable. This paper investigates an interference-aware receiver in which colored-estimation-noise-based maximum-likelihood (CEN-ML) detection is employed to mitigate the impact of imperfect channel estimates. It is shown that the CEN-ML detection, which treats the channel estimation errors as colored noise, has superior performance and is robust to imperfect channel estimation. However, a CEN-ML interference-aware detector usually requires computational efforts that are too complex for multiple-input–multiple-output (MIMO) interference-limited systems. To reduce the complexity, this paper proposes new methods for both the preprocessing and tree-search stages of an interference-aware sphere detector (SD). First, a channel extended matrix is constructed using the information from channel estimation errors in a preprocessing stage. The channel extension model can reduce the complexity without performance loss. Second, two tree pruning (TP) criteria are developed in a tree-search stage to make the implementation of the CEN-ML SD feasible. The first criterion is based on a lower bound of the CEN-ML metric and can achieve the same result as the optimal brute-force method. The second criterion is derived from an approximated metric and can further reduce the complexity with small performance loss. Simulation results for typical Third-Generation Partnership Project Long-Term Evolution (LTE)/LTE-Advanced scenarios show that the new methods provide the ability to deal with the channel estimation errors and to achieve a better tradeoff between complexity and performance (e.g., in terms of throughput or error rate) in interference-limited scenarios. The proposed low-complexity CEN-ML SD can have a gain of more than 10 dB than the other investigated detection for 16-ary quadrature amplitude modulation (QAM) or 64-QAM at a target coded block error rate (BLER) of $\mbox{10}^{-2}$ .

Soft-Decision Multiple-Symbol Differential Sphere Detection and Decision-Feedback Differential Detection for Differential QAM Dispensing with Channel Estimation in the Face of Rapidly Fading Channels

Turbo detection performed by exchanging extrinsic information between the soft-decision QAM detector and the channel decoder is beneficial for the sake of exploring the bit dependency imposed both by modulation and by channel coding. However, when the soft-decision coherent QAM detectors are provided with imperfect channel estimates in rapidly fading channels, they tend to produce potentially unreliable LLRs that deviate from the true probabilities, which degrades the turbo detection performance. Against this background, in this paper, we propose a range of new soft-decision multiple-symbol differential sphere detection (MSDSD) and decision-feedback differential detection (DFDD) solutions for differential QAM (DQAM), which dispense with channel estimation in the face of rapidly fading channels. Our proposed design aims for solving the two inherent problems in soft-decision DQAM detection design, which have also been the most substantial obstacle in the way of offering a solution for turbo detected MSDSD aided differential MIMO schemes using QAM: 1) how to facilitate the soft-decision detection of the DQAM’s amplitudes, which—in contrast to the DPSK phases—do not form a unitary matrix, and 2) how to separate and streamline the DQAM’s soft-decision amplitude and phase detectors. Our simulation results demonstrate that our proposed MSDSD aided DQAM solution is capable of substantially outperforming its MSDSD aided DPSK counterpart in coded systems without imposing a higher complexity . Moreover, our proposed DFDD aided DQAM solution is shown to outperform the conventional solutions in literature. Our discussions on the important subject of coherent versus noncoherent schemes suggest that compared to coherent square QAM relying on realistic imperfect channel estimation, MSDSD aided DQAM may be deemed as a better candidate for turbo detection assisted coded systems operating at high Doppler frequencies.

Reducing Computational Complexity and Enhancing Performance of IKSD Algorithm for Encoded MIMO Systems

<p>The main challenge in MIMO systems is how to design the MIMO detection algorithms with lowest computational complexity and high performance that capable of accurately detecting the transmitted signals. In last valuable research results, it had been proved the Maximum Likelihood Detection (MLD) as the optimum one, but this algorithm has an exponential complexity especially with increasing of a number of transmit antennas and constellation size making it an impractical for implementation. However, there are alternative algorithms such as the K-best sphere detection (KSD) and Improved K-best sphere detection (IKSD) which can achieve a close to Maximum Likelihood (ML) performance and less computational complexity. In this paper, we have proposed an enhancing IKSD algorithm by adding the combining of column norm ordering (channel ordering) with Manhattan metric to enhance the performance and reduce the computational complexity. The simulation results show us that the channel ordering approach enhances the performance and reduces the complexity, and Manhattan metric alone can reduce the complexity. Therefore, the combined channel ordering approach with Manhattan metric enhances the performance and much reduces the complexity more than if we used the channel ordering approach alone. So our proposed algorithm can be considered a feasible complexity reduction scheme and suitable for practical implementation.</p>

Enhancing the Performance of the Quasi-ML Receiver (Detector Plus Decoder) for Coded MIMO Systems Via Statistical Information

We propose a statistical information-based approach to enhance the performance of the noniterative quasi-maximum-likelihood (QML) receiver (detector plus decoder) recorded at the output of the channel decoder for coded multiple-input–multiple-output (MIMO) systems. As an illustrative example, the QR decomposition with an $M$ -algorithm (QRD-M)-based noniterative QML receiver is employed in this paper. We first illustrate that there is a large discrepancy between the amplitude of the raw log-likelihood ratio (LLR) generated by the complexity-constrained QRD-M detector and its reliability, particularly for LLRs with high amplitudes. The discrepancy is shown to be the main underlying cause of degraded performance in coded MIMO systems. Then, we propose a reliability-aware transformation technique, which exploits the statistical relationship between the raw LLR's amplitude and its reliability to adjust the raw LLR's amplitude to a level commensurate with its reliability. By using the proposed statistical knowledge-based technique, the performance of the receiver (detector plus decoder) recorded at the output of the channel decoder is significantly enhanced for high-order modulations. The extension of the proposed method to other QML receivers such as the list sequential (LISS) detector and list sphere detector (LSD)-based receivers is straightforward.

Correction about rear of aperture serves as sphere wall in effective area method

To eliminate the non-uniformity response of integrating sphere detector, which is brought by the back surface of the measured main aperture of the solar irradiance absolute radiometer serving as a part of the integrating sphere wall in effective area method, a correction optical model is designed to tackle the issue in TracePro program. By simulations, the radiant flux responded by the integrating sphere detector is given. Then the curve of the relative change about the radiant flux is deduced and the experiment measurement data are corrected on the basis of relative change curve. Eventually, the uncorrected and the corrected main aperture areas are calculated. Within a series of repeated measurements, the relative uncertainties of the uncorrected results and the corrected results are both 7 × 10−5 (n = 7). The relative uncertainty of the differences between the raw results and corrected results is 9.65 × 10−5 which proves that the optical model can evaluate the deviation, efficiently.

Unified low-complexity decision feedback equalizer with adjustable double radius constraint

The maximum likelihood sequence estimation (MLSE) is an optimal equalization method to suppress Inter-Symbol Interference (ISI) in communication and storage systems. The Viterbi Algorithm (VA) provides an exact solution of MLSE. To reduce the complexity of VA, MLSE-DFE, which combines the VA within a decision feedback equalizer (DFE), is widely used in practical designs; however, the computing complexity is still too high. In this paper, we propose the SDVA-DFE, a unified DFE combining the concept of sphere detector (SD) and VA. The computing complexity of the SDVA-DFE can be reduced by proposed double radius constraints, upper radius (UR) and lower radius (LR). By adjusting the values of the two radiuses, the SDVA-DFE also provides a trade-off between performance and complexity. Simulation shows that this method is suitable for high-order modulation and long-length channel impulse response. When applied to a Lorentzian channel and channels of different eigenvalue spread, the SDVA-algorithm can reduce the complexity by over 90% at high SNR compared with MLSE-DFE.

A review: detection techniques for LTE system

Long term evolution (LTE) has become the fastest developing mobile system technology and is considered as the fourth generation of wireless communication systems. Multiple-input multiple-output spatial multiplexing is a key technique employed in LTE system to boost the system capacity. With this gain comes the challenge of designing efficient detectors at the receiver side to decouple the transmitted data streams. The exhaustive search detector is optimum in terms of performance but it has a high computational load in terms of time and power. Owing to its high computational load, many detection techniques have been proposed in order to achieve an efficient detection performance with low complexity in terms of computational power. This paper reviews the detection techniques used in LTE including the maximum likelihood (ML) detection, zero forcing (ZF) detection, minimum mean square error (MMSE) detection, successive interference cancellation (SIC) detection, sphere decoding detection, lattice reduction and list decoding. It is shown that Maximum Likelihood detection has the best performance, but the complexity is too high. Other detectors like ZF or MMSE and nonlinear detectors employing SIC have much lower complexity, however, they do not provide detection performance close to that of the ML detection. The sphere detection algorithm has been shown to have low average computational complexity and achieves a quasi-ML performance which gives it an advantage over to other types of detection techniques.

Property Evaluation of Eccentric Astigmatic Method to Apply Micro Tactile Probe

Displacement detection of a small sphere with high sensitivity is required for realizing the micro tactile probe used in micro-coordinate-measuring machines (CMMs). Therefore, the authors have proposed a new technique for detecting the three-dimensional displacement of a small sphere by a kind of astigmatic method, termed the “eccentric astigmatic method (EAM).” In the EAM, a spherical mirror as a spherical tactile probe is placed on the focus of an objective lens. At this position, three eccentric beams, focused by the objective lens, are incident on the mirror surface at right angles and the reflected rays return on the incident paths. In contrast, when the sphere moves from this position even by a small distance, the return path of each reflected ray changes drastically. This change can be detected by the EAM using a condenser lens and a photodetector. The changes in the spot radius caused by the EAM were calculated using a ray-tracing code. As a result, a change in the spot shape was found to occur only for displacement along one axis. Moreover, simulations based on wave optics were performed, whose results confirmed the feasibility of detection of the three-dimensional displacement of a sphere by the EAM.

Reduced Complexity Optimum Detector for Block Data Transmission System Using<i> A<sub>n</sub> </i>Lattice Sphere Detection Technique

Block Data Transmission Systems (BDTS) are used in high speed wireless communication systems with time dispersive channel characteristics. Since Maximum Likelihood Block Detection (MLBD) requires huge amount of computation, Sphere Detection (SD) technique has been introduced as an alternative. This paper proposes an An Lattice Sphere Detection (AnLSD) technique for detection in block data transmission systems (BDTS). The An lattice structure offers more dense constellation points than the customary Zn lattice in SD technique. Thus, the proposed AnLSD uses An lattice generator matrix as the channel matrix, and uses the Gram matrix which is obtained from the generator matrix as part of the detection process. Simulation results show that the proposed AnLSD performs very close to the Exhaustive search (ES) using block size of 20 bits. It is also performing better than the other renowned methods tested under a channel with spectral nulls. The proposed AnLSD technique offers significant reduction in term of objective functions evaluation as compared to the other renowned methods.

Reduced-Complexity Soft-Decision Multiple-Symbol Differential Sphere Detection

Unlike a generic PSK/QAM detector, which may visit a constellation diagram only once, a depth-first Sphere Decoder (SD) has to re-visit the same constellation diagram multiple times. Therefore, in order to prevent the SD from repeating the detection operations, the Schnorr-Euchner search strategy of Schnorr and Euchner may be invoked for optimizing the nodes' search-order, where the ideal case is for the SD to visit the constellation nodes in a zigzag fashion. However, when the hard-decision Multiple-Symbol Differential Sphere Detection (MSDSD) of Lampe et al. is invoked for using multiple receive antennas $N_R\ge 1$ , the Schnorr-Euchner search strategy has to visit and sort all the $M\textrm{PSK}$ constellation points. A similar situation is encountered for the soft-decision MSDSD of Pauli et al. , when the a priori LLRs gleaned from the channel decoder are taken into account. In order to tackle these open problems, in this paper, we propose a correlation process for the hard-decision MSDSD of Lampe et al. and a reduced-complexity design for the soft-decision MSDSD of Pauli et al. , so that the Schnorr-Euchner search strategy always opts for visiting the $M\textrm{PSK}$ constellation points in a zigzag fashion. Our simulation results demonstrate that a substantial complexity reduction is achieved by our reduced-complexity design without imposing any performance loss . Explicitly, up to 88.7% complexity reduction is attained for MSDSD $(N_w=4)$ aided D16PSK. This complexity reduction is quite substantial, especially when the MSDSD is invoked several times during turbo detection. Furthermore, in order to offer an improved solution and a comprehensive study for the soft-decision MSDSD, we also propose to modify the output of the SD to harmonize its operation with the near-optimum Approx-Log-MAP. Then the important subject of coherent versus noncoherent is discussed in the context of coded systems, which suggests that MSDSD aided DPSK is an eminently suitable candidate for turbo detection assisted coded systems operating at high Doppler frequencies.

Low complexity Breadth First Search Sphere Detector for MIMO Systems

Low complexity Breadth First Search Sphere Detector for MIMO Systems

Simulation of the response functions of an extended range neutron multisphere spectrometer using FLUKA

In this paper, the distribution of radiation field in the CSNS spectrometer hall at Dongguan, China, was simulated by the FLUKA program. The results show that the radiation field of the high energy proton accelerator is dominated by neutron radiation, with a broad range of neutron energies, spanning about eleven orders of magnitude. Simulation and calculation of the response functions of four Bonner spheres with a simplified model is done with FLUKA and MCNPX codes respectively, proving the feasibility of the FLUKA program for this application and the correctness of the calculation method. Using the actual model, we simulate and calculate the energy response functions of Bonner sphere detectors with polyethylene layers of different diameters, including detectors with lead layers, using the FLUKA code. Based on the simulation results, we select eleven detectors as the basic structure for an Extended Range Neutron Multisphere Spectrometer (ERNMS).

Parallel sphere detector algorithm providing optimal MIMO detection on massively parallel architectures

SummaryMultiple‐input multiple‐output (MIMO) systems have attracted considerable attention in wireless communications because they offer a significant increase in data throughput and link coverage without additional bandwidth requirement or increased transmit power. The price that has to be paid is the increased complexity of hardware components and algorithms. The sphere detector (SD) algorithm solves the problem of maximum likelihood (ML) detection for MIMO channels by significantly reducing the search space of possible solutions. The main drawback of the SD algorithm is in its sequential nature, consequently, running it on massively parallel architectures (MPAs) is very inefficient. In order to overcome the drawbacks of the SD algorithm, a new parallel sphere detector (PSD) algorithm is proposed. It implements a novel hybrid tree search method, where the algorithm parallelism is assured by the efficient combination of depth‐first search and breadth‐first search algorithms. A path metric‐based parallel sorting is employed at each intermediate stage. The PSD algorithm is able to adjust its memory requirements and extent of parallelism to fit a wide range of parallel architectures. Mapping details for MPAs are proposed by giving the details of thread dependent, highly parallel building blocks of the algorithm. Based on the building blocks proposed, a mapping to general‐purpose graphics processing unit is provided, and its performance is evaluated. In order to achieve high‐throughput, several levels of parallelism are introduced, and different scheduling strategies are considered. Copyright © 2015 John Wiley & Sons, Ltd.

Comparison of digital breast tomosynthesis and 2D digital mammography using a hybrid performance test

This paper introduces a hybrid method for performing detection studies in projection image based modalities, based on image acquisitions of target objects and patients. The method was used to compare 2D mammography and digital breast tomosynthesis (DBT) in terms of the detection performance of spherical densities and microcalcifications. The method starts with the acquisition of spheres of different glandular equivalent densities and microcalcifications of different sizes immersed in a homogeneous breast tissue simulating medium. These target objects are then segmented and the subsequent templates are fused in projection images of patients and processed or reconstructed. This results in hybrid images with true mammographic anatomy and clinically relevant target objects, ready for use in observer studies. The detection study of spherical densities used 108 normal and 178 hybrid 2D and DBT images; 156 normal and 321 hybrid images were used for the microcalcifications. Seven observers scored the presence/absence of the spheres/microcalcifications in a square region via a 5-point confidence rating scale. Detection performance in 2D and DBT was compared via ROC analysis with sub-analyses for the density of the spheres, microcalcification size, breast thickness and z-position. The study was performed on a Siemens Inspiration tomosynthesis system using patient acquisitions with an average age of 58 years and an average breast thickness of 53 mm providing mean glandular doses of 1.06 mGy (2D) and 2.39 mGy (DBT). Study results showed that breast tomosynthesis (AUC = 0.973) outperformed 2D (AUC = 0.831) for the detection of spheres (p < 0.0001) and this applied for all spherical densities and breast thicknesses. By way of contrast, DBT was worse than 2D for microcalcification detection (AUC2D = 0.974, AUCDBT = 0.838, p < 0.0001), with significant differences found for all sizes (150–354 µm), for breast thicknesses above 40 mm and for heights above the detector of 20 mm and above. In conclusion, the hybrid method was successfully used to produce images for a detection study; results showed breast tomosynthesis outperformed 2D for spherical densities while further optimization of DBT for microcalcifications is suggested.

A hierarchical model for automated standard sagittal-view detection from 3D ultrasound data in 11–14 weeks

The nuchal translucency thickness is an important parameter for the diagnosis of fetal abnormalities during 11–14 weeks. Currently in clinical practice, it first requires manual scanning operations to determine the fetal standard sagittal-view plane and then the measurements can be performed in the corresponding plane images. Besides the difficulty of such standard plane detection, this also leads to the time-consuming and detection-variability problems. In the paper, a hierarchical model is proposed to automatically detect the standard sagittal-view plane based on 3D ultrasound data. In the model, Hessian-matrix based filtering is first applied for obtaining the plate-structure distribution in the data. Then the sphere distribution is calculated by convolving sphere detectors with the ultrasound data. Based on the two prior distributions, the sampling-based Hough transform is further performed for the plane detection. The performance of the proposed model is verified by the experimental results on 3D synthetic data and 241 clinical 3D ultrasound data in 11–14 weeks.

Sparse K ‐best detector for generalised space shift keying in large‐scale multiple‐input–multiple‐output systems

In this study, the authors propose a low complexity detector for the generalised space shift keying (GSSK) in large-scale multiple-input–multiple-output systems. To be concrete, they propose a sparse K-best (SK) detector based on the breadth-first category of sphere detector (referred to K-best sphere decoding). The author's detector is inspired by the fact that the GSSK signal is naturally a sparse zero-one vector since only a few antennas are activated at the transmitter. Different with the conventional K-best detector searching all the transmit antennas, their proposed SK detector investigates only a few promising candidates which are activated antennas at the transmitter. Overall, their proposed SK detector exploits not only the sparsity of the GSSK signal but also the constraint on its non-zero values. Therefore the restricted isometry property-based performance analysis shows that is effective in detecting the GSSK signal. Moreover, the empirical results show that their detector performs much better than the sparse algorithms-based normalised compressive sensing (NCS) detectors while exhibits only slightly higher complexity than the latter (the low-complexity orthogonal matching pursuit-based NCS detector).

Task-driven image acquisition and reconstruction in cone-beam CT

This work introduces a task-driven imaging framework that incorporates a mathematical definition of the imaging task, a model of the imaging system, and a patient-specific anatomical model to prospectively design image acquisition and reconstruction techniques to optimize task performance. The framework is applied to joint optimization of tube current modulation, view-dependent reconstruction kernel, and orbital tilt in cone-beam CT. The system model considers a cone-beam CT system incorporating a flat-panel detector and 3D filtered backprojection and accurately describes the spatially varying noise and resolution over a wide range of imaging parameters in the presence of a realistic anatomical model. Task-based detectability index (d′) is incorporated as the objective function in a task-driven optimization of image acquisition and reconstruction techniques. The orbital tilt was optimized through an exhaustive search across tilt angles ranging ±30°. For each tilt angle, the view-dependent tube current and reconstruction kernel (i.e. the modulation profiles) that maximized detectability were identified via an alternating optimization. The task-driven approach was compared with conventional unmodulated and automatic exposure control (AEC) strategies for a variety of imaging tasks and anthropomorphic phantoms. The task-driven strategy outperformed the unmodulated and AEC cases for all tasks. For example, d′ for a sphere detection task in a head phantom was improved by 30% compared to the unmodulated case by using smoother kernels for noisy views and distributing mAs across less noisy views (at fixed total mAs) in a manner that was beneficial to task performance. Similarly for detection of a line-pair pattern, the task-driven approach increased d′ by 80% compared to no modulation by means of view-dependent mA and kernel selection that yields modulation transfer function and noise-power spectrum optimal to the task. Optimization of orbital tilt identified the tilt angle that reduced quantum noise in the region of the stimulus by avoiding highly attenuating anatomical structures. The task-driven imaging framework offers a potentially valuable paradigm for prospective definition of acquisition and reconstruction protocols that improve task performance without increase in dose.

Guided Search MIMO Detectors aided by Lattice Reduction under Correlated Channels

The conventional detector (matched filter) becomes inefficient in MIMO systems demanding high data throughput and/or subject to interference and/or under correlated channels. The bit error rate performance or system capacity under conventional detection will be substantially degraded when the spatial diversity provided by multiple antennas cannot be fully exploited and the detection process is unable to efficiently separate the signal from each antenna. The solution discussed in this paper seeks to establish more efficient detectors for MIMO systems with the aid of the Lattice Reduction technic. These detectors use information from the interfering signals in a way to improve the signal detection in the antenna of interest, thus providing advantages over the conventional system, at the expense of increasing complexity. The focus of this paper consists in comparing the characteristics of three sub-optimal detectors, previously analyzed in [1], based on the maximum-likelihood function and the guided search principle. Especially, the complexity vs performance trade-off for the sphere detector (SD), the QR decomposition-based detector (QRD) the greedy search detector (GSD) and its variants aided by the Lattice Reduction are compared. We have considered the performance-complexity tradeoff as a main figure-of-merit of sub-optimal MIMO detectors under Rayleigh correlated fading channels.

New 2-D Adaptive K-Best Sphere Detection for Relay Nodes

Relay nodes are the main players of cooperative networks that used to improve the system performance and to offer virtual multiple antennas for limited antenna devices in a multi-user environment. However, employing relaying strategies requires considerable resources at the relay side and places a large burden on the relay helping node especially when considering the power consumption. Partial detection at the relay is one strategy that reduces the computational load and power consumption. In this paper, we propose a new 2-D Adaptive K-Best Sphere Decoder (2-D AKBSD) for partial detection to be used MISO relays in cooperative networks. Simulation results show that 2-D AKBSD is capable of improving the system performance and also reduces its complexity.

Holmium–lipiodol–alginate microspheres for fluoroscopy-guided embolotherapy and multimodality imaging

Embolotherapy is a minimally invasive transcatheter technique aiming at reduction or complete obstruction of the blood flow by infusion of micro-sized particles in order to induce tumor regression. A major drawback of the current commercially available and clinically used microspheres is that they cannot be detected in vivo with medical imaging techniques, impeding intra- and post-procedural feedback. It can be expected that real-time monitoring of microsphere infusion and post-procedural imaging will result in better predictability and higher efficacy of the treatment. In this study, a novel microsphere formulation has been developed that can be visualized with fluoroscopy, X-ray computed tomography (CT) and magnetic resonance imaging (MRI). The microspheres were prepared with the JetCutter technique and consist of alginate (matrix-forming polymer), holmium (cross-linking and MRI contrast agent), lipiodol (radiopaque contrast agent) and Pluronic F-68 (surfactant). The mean size (±SEM) of the hydrated holmium–lipiodol–alginate microspheres (Ho–lip–ams) was 570±12μm with a holmium content of 0.38±0.01% (w/w). Stability studies showed that the microspheres remained intact during incubation for two weeks in fetal calf serum (FCS) at 37°C. The inclusion of lipiodol in the microspheres rendered excellent visualization capabilities for fluoroscopy and CT, whereas the holmium ions, which keep the alginate network together, also allow MR imaging. In this study it was shown that single sphere detection was possible by fluoroscopy, CT and MRI. The Ho–lip–ams were visualized in real-time, during infusion in a porcine kidney using fluoroscopy, and post-procedural, the deposition of the microspheres was examined with fluoroscopy, (cone beam rotational) CT and MRI. The different imaging modalities showed similar deposition patterns of the microspheres within the organ. The combination of intra-procedural visualization, multimodality imaging for patient follow-up and the possibility of quantification offers a new and promising method for more safe, efficient and successful embolization treatment.