Reduced-complexity Algorithm Research Articles

각변위 방식을 이용한 캡슐의 오리엔테이션 측정 방법

본 논문은 각변위 방식을 이용한 소화경로 모델링 및 경로에 대한 캡슐의 오리엔테이션 측정 방법을 제안하였다. 제안하는 방법은 캡슐 내시경의 자세를 3차원 방향벡터와 이의 회전성분으로 표현하였다. 소화경로에 대한 캡슐의 오리엔테이션 정보인 롤, 피치 그리고 요우 값은 각각 <TEX>$1.6^{\circ}$</TEX> 이내의 오차를 보였다. 제안된 방법은 자기장을 이용한 오리엔테이션 측정에서 롤값 측정 문제를 해결하였다. 오일러 각을 이용한 기존의 오리엔테이션 측정 방식과 비교하여 알고리즘의 복잡도를 줄였다. In this paper, we propose an orientation tracking method and a digestion path model based on angular displacement. The proposed method expresses a capsule's orientation as 3-dimension vectors and its rotation angle. Errors in roll, pitch, and yaw representing capsule's orientation information is down to <TEX>$1.6^{\circ}$</TEX>. Using the proposed method we can measure a roll which is not Possible to be measured using the magnetic field method. We reduce algorithm complexity lower than a previous methods based on Euler angle.

MIMO Relaying With Linear Processing for Multiuser Transmission in Fixed Relay Networks

In this paper, a novel relaying strategy that uses multiple-input multiple-output (MIMO) fixed relays with linear processing to support multiuser transmission in cellular networks is proposed. The fixed relay processes the received signal with linear operations and forwards the processed signal to multiple users creating a multiuser MIMO relay. This paper proposes upper and lower bounds on the achievable sum rate for this architecture assuming zero-forcing dirty paper coding at the base station, neglecting the direct links from the base station to the users, and with certain structure in the relay. These bounds are used to motivate an implementable multiuser precoding strategy that combines Tomlinson-Harashima precoding at the base station and linear signal processing at the relay, adaptive stream selection, and QAM modulation. Reduced complexity algorithms based on the sum rate lower bounds are used to select a subset of users. We compare the sum rates achieved by the proposed system architecture and algorithms with the sum rate upper bound and the sum rate achieved by the decode-and-forward relaying.

Reduced-Complexity BCJR Algorithm for Turbo Equalization

We propose novel techniques to reduce the complexity of the well-known Bahl, Cocke, Jelinek, and Raviv (BCJR) algorithm when it is employed as a detection algorithm in turbo equalization schemes. In particular, by also considering an alternative formulation of the BCJR algorithm, which is more suitable than the original one for deriving reduced-complexity techniques, we describe three reduced-complexity algorithms, each of them particularly effective over one of the three different classes of channels affected by intersymbol interference (minimum-phase, maximum-phase, and mixed-phase channels). The proposed algorithms do not explore all paths on the trellis describing the channel memory, but they work only on the most promising ones, chosen according to the maximum a posteriori criterion. Moreover, some optimization techniques improving the effectiveness of the proposed solutions are described. Finally, we report the results of computer simulations showing the impressive performance of the proposed algorithms, and we compare them with other solutions in the literature.

Reduced-Complexity BCJR Algorithm for Turbo Equalization

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We propose novel techniques to reduce the complexity of the well-known Bahl–Cocke–Jelinek–Raviv (BCJR) algorithm when it is employed as a detection algorithm in turbo equalization schemes. In particular, by also considering an alternative formulation of the BCJR algorithm, which is more suitable than the original for deriving reduced-complexity techniques, we describe three reduced-complexity algorithms, each of them being particularly effective over one of the three different classes of channels (minimum-phase, maximum-phase, and mixed-phase channels) affected by intersymbol interference. The proposed algorithms do not explore all paths on the trellis describing the channel memory, but they work only on the most promising ones, which are chosen according to the maximum <emphasis emphasistype="italic">a posteriori</emphasis> criterion. Moreover, some optimization techniques for improving the effectiveness of the proposed solutions are described. Finally, we report the results of computer simulations showing the impressive performance of the proposed algorithms, and compare them with other solutions in the literature. </para>

Capacity Maximization for Zero-Forcing MIMO-OFDMA Downlink Systems with Multiuser Diversity

This paper considers the optimal resource allocation for a multiuser MIMO-OFDMA downlink system using zero-forcing SDMA, with the objective of maximizing the system capacity. We provide the general mathematical formulation of the capacity maximization problem whose resource allocation includes user selection, power allocation and receive signal subspace selection. While the optimal solution of this problem is difficult to obtain, we consider two special cases: user selection based only and antenna selection based only optimization, and develop the optimal solution by reformulating the combinatorial optimization problem into a convex problem. Our proposed algorithms for these special cases can be extended to develop a suboptimal algorithm for the original general optimization problem which allows subspace selection. Moreover, we develop reduced-complexity algorithms for these problems which incur negligible capacity loss. Numerical results and analysis are provided to evaluate the system capacity and to study the effect of the number of users and the receive antennas per user on the performance. Comparisons on the capacity achieved by user selection only, antenna selection only and suboptimal subspace selection are discussed

Multi-Stage Beamforming for Coded OFDM with Multiple Transmit and Multiple Receive Antennas

Conventionally, subcarrier based beamforming was used in an OFDM (orthogonal frequency division multiplexing) system under MIMO (multiple-input and multiple-output) channels to achieve optimal performance, where many DFT/IDFT (discrete Fourier transform/inverse DFT) blocks, each corresponding to one antenna, are required. To reduce the complexity induced by the DFT/IDFT processing, symbol based beamforming was recently proposed with only one IDFT block at the transmitter and one DFT block at the receiver. However, this approach results in a significant performance loss especially when the number of distinct paths in the channel is large. In this paper, we propose a multi-stage beamforming (MSB) scheme for MIMO-OFDM systems employing both the principles of subcarrier level beamforming and symbol level beamforming to effectively tradeoff system performance and complexity. Using the proposed MSB scheme and with a little complexity increase compared with the symbol based beamforming, system performance can be significantly improved. Under some channel conditions, the proposed MSB scheme can achieve a performance close to the optimal subcarrier based beamforming scheme but with much lower complexity. For the MSB scheme, we propose an iterative algorithm to jointly optimize the symbol level and subcarrier level weighting coefficients. To reduce the complexity of the joint weighting coefficients calculation algorithm, we propose a reduced complexity algorithm to first obtain the symbol level weighting coefficients and then the subcarrier level weighting coefficients. Simulation results show that good performance can be achieved using the proposed weighting coefficients calculation algorithms with reasonable complexity

Reduced-complexity decoding algorithms for unitary space-time codes

Two reduced-complexity decoding algorithms for unitary space-time codes based on tree-structured constellation are presented. In this letter original unitary space-time constellation is divided into several groups. Each one is treated as the leaf nodes set of a subtree. Choosing the unitary signals that represent each group as the roots of these subtrees generates a tree-structured constellation. The proposed tree search decoder decides to which sub tree the receive signal belongs by searching in the set of subtree roots. The final decision is made after a local search in the leaf nodes set of the selected sub tree. The adjacent subtree joint decoder performs joint search in the selected sub tree and its “surrounding” subtrees, which improves the Bit Error Rate (BER) performance of purely tree search method. The exhaustively search in the whole constellation is avoided in our proposed decoding algorithms, a lower complexity is obtained compared to that of Maximum Likelihood (ML) decoding. Simulation results have also been provided to demonstrate the feasibility of these new methods.

Soft Reconstruction of Speech in the Presence of Noise and Packet Loss

Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth efficient way to combat the noisy channel degradations. In this paper, we consider soft reconstruction of speech spectrum, in GSM adaptive multirate and IS-641 vocoders, transmitted over a channel disturbed with noise and/or packet loss. Several schemes are presented which exploit different levels of intraframe and interframe residual redundancy for improved source decoding at the receiver. A packetization strategy is proposed which is matched to the presented error concealment units. For decoders that exploit the residual redundancy, extensive complexity has been a serious concern, especially as the quantizer bitrate increases . In this paper, a novel method is presented to construct reduced complexity algorithms. The proposed methodology is based on the classification of the signal domain and efficient approximation of the residual redundancy or the a priori transition probabilities. The presented schemes provide high quality error concealment solutions for code excited linear prediction (CELP) coders

Unified Systolic-Like Architecture for DCT and DST Using Distributed Arithmetic

A common computing-core representation of the discrete cosine transform and discrete sine transform is derived and a reduced-complexity algorithm is developed for computation of the proposed computing-core. A parallel architecture based on the principle of distributed arithmetic is designed further for the computation of these transforms using the common-core algorithm. The proposed scheme not only leads to a systolic-like regular and modular hardware for computing these transforms, but also offers significant improvement in area-time efficiency over the existing structures. The structure proposed here is devoid of complicated input/output mapping and does not involve any complex control. Unlike the convolution-based structures, it does not restrict the transform length to be a prime or multiple of prime and can be utilized as a reusable core for cost-effective, memory-efficient, high-throughput implementation of either of these transforms

Efficient Systolic Implementation of DFT Using a Low-Complexity Convolution-Like Formulation

A reduced-complexity algorithm is presented for computation of the discrete Fourier transform, where N-point transform is computed from eight number of nearly (N/8)-point circular-convolution-like operations. A systolic architecture is also derived for very large-scale integration circuit implementation of the proposed algorithm. The proposed architecture is fully pipelined and contains regular and simple locally connected processing elements. It is devoid of complex control structure and is scalable for higher transform lengths. It is observed that the proposed systolic structure involves either less or nearly the same hardware-complexity compared with the corresponding existing systolic structures. In addition, it offers eight times more throughput and significantly low latency compared with the others

Reduced complexity data-aided and code-aided frequency offset estimation for flat-fading MIMO channels

This contribution deals with carrier frequency offset estimation for flat-fading multiple-input multiple-output (MIMO) channels. Both data-aided (using training symbols) and iterative code-aided (using the unknown coded symbols) estimation is considered. In both scenarios, maximum-likelihood (ML) frequency offset estimation involves solving a maximization problem with no closed-form solution. Since numerical calculation of the ML estimates is computationally hard, we derive a simple closed-form approximation. Simulation results indicate that the ML algorithm and the proposed reduced-complexity algorithm operate closely to the Cramer-Rao bound (CRB)

Trustworthy systems through quantitative software engineering

Trustworthy systems through quantitative software engineering

Cross-Layer optimal connection admission control for variable bit rate multimedia traffic in packet wireless CDMA networks

Next generation wireless code division multiple access (CDMA) networks are required to support packet multimedia traffic. This paper addresses the connection admission control problem for multiservice packet traffic modeled as Markov modulated Poisson process (MMPP) with the quality of service (QoS) requirements on both physical layer signal-to-interference ratio (SIR) and network layer blocking probability. Optimal linear-programming-based algorithms are presented that take into account of SIR outage probability constraints. By exploiting the MMPP traffic models and introducing a small SIR outage probability, the proposed algorithms can dramatically improve the network utilization. In addition, we propose two reduced complexity algorithms that require less computation and can have satisfactory approximation to the optimal solutions. Numerical examples illustrating the performance of the proposed schemes are presented.

Reduced-Complexity Fusion of Multiscale Topography and Bathymetry Data Over the Florida Coast

The multiscale Kalman smoother (MKS) is a globally optimal estimator for fusing remotely sensed data. The MKS algorithm can be readily parallelized because it operates on a Markov tree data structure. However, such an implementation requires a large amount of memory to store the parameters and estimates at each scale in the tree. This becomes particularly problematic in applications where the observations have very different resolutions and the finest scale data are sparse or aggregated. Such cases commonly arise when fusing data to capture both regional and local structure. In this work, we develop a reduced-complexity MKS algorithm and apply it to the fusion of topographic and bathymetric elevations on the Florida coast.

Reduced complexity joint iterative equalization and multiuser detection in dispersive DS-CDMA channels

Communication through dispersive direct-sequence code-division multiple-access channels suffers from intersymbol and multiple-access interference, which can significantly impair performance. Joint maximum a posteriori probability equalization and multiuser detection with error control decoding can be used to mitigate this interference and to achieve the optimal bit-error rate. Unfortunately, such optimal detection typically requires prohibitive computational complexity. This problem is addressed in this paper through the development of a reduced state trellis search detection algorithm, based on decision feedback from channel decoders. The performance of this algorithm is analyzed in the large-system limit. This analysis and simulations show that this reduced complexity algorithm can exhibit near-optimal performance under moderate signal-to-noise ratio and attains larger system load capacity than parallel interference cancellation.

Complexity-Reduced Iterative MAP Receiver for Interference Suppression in OFDM-Based Spatial Multiplexing Systems

In this paper, we propose a reduced-complexity iterative algorithm for joint maximum a posteriori (MAP) detection and the cochannel interferences (CCIs) suppression in orthogonal frequency-division multiplex (OFDM)-based spatial multiplexing systems, also known as multiple-input-multiple-output systems. The receiver employs an iterative architecture in which each iteration stage consists of channel/covariance estimation for CCIs and MAP detection, which suppresses interference while detecting the data. The interference suppression is performed in the MAP detection by whitening the interferences through a combination of the estimated covariance with the conventional MAP detection metric. Moreover, a complexity-reduction scheme is proposed for implementing an iterative MAP receiver without incurring performance degradation. Extensive simulations have demonstrated that the proposed scheme dramatically improves the performance compared to that of an iterative MAP scheme without interference suppression. The receiver complexity is significantly reduced with negligible performance degradation. Furthermore, it also can effectively suppress not only synchronous CCIs, but also all kinds of asynchronous CCIs without any a priori information on the CCIs.

Training-Based Time-Delay Estimation for CPM Signals Over Time-Selective Fading Channels

In this paper, we consider training-based symbol timing synchronization for continuous phase modulation over channels subject to flat, Rayleigh fading. A high signal-to-noise-ratio maximum-likelihood estimator based on a simplified channel correlation model is derived. The main objective is to reduce algorithm complexity to a single-dimensional search on the delay parameter, similar to that of the static-channel (slow fading) estimator. The asymptotic behavior of the algorithm is evaluated, and comparisons are made with the Cramer-Rao lower bound for the problem. Simulation results demonstrate highly improved performance over the conventional, static-channel delay estimator.

Blind and Semiblind Detections of OFDM Signals in Fading Channels

This paper considers the problem of blind joint channel estimation and data detection for orthogonal frequency-division multiplexing (OFDM) systems in a fading environment. Employing a regression model for a time-varying channel, we convert the problem into one that finds the data sequence x whose associated least-squares (LS) channel estimate z(x) is closest to the space of some regression curves (surfaces). We apply the branch-and-bound principle to solve the nonlinear integer programming problem associated with finding the curve that fits a subchannel in the LS sense. A recursive formula for fast metric update is obtained by exploiting the intrinsic characteristic of our objective function. The impacts of reordering the data sequence and selective detection are addressed. By employing a preferred order along with a selective detection method, we greatly reduce the detector complexity while giving up little performance loss. Both the complete and the reduced-complexity algorithms can be used for blind and semiblind detections of OFDM signals in a subchannel-by-subchannel manner. To further reduce the complexity and exploit the frequency-domain channel correlation, we suggest a two-stage approach that detects a few selected positions in some subchannels first, and then, treating the detected symbols as pilots, determines the remaining symbols within a properly chosen time-frequency block by a two-dimensional model-based pilot-assisted algorithm. The proposed methods do not require the information of the channel statistics like signal-to-noise ratio or channel correlation function. Performance of differential modulations like differential quaternary phase-shift keying and STAR 16-ary quadrature amplitude modulation are provided. Both blind and semiblind schemes yield satisfactory performance.

Reduced spatio-temporal complexity mmpp and image-based tracking filters for maneuvering targets

We present reduced-complexity nonlinear filtering algorithms for image-based tracking of maneuvering targets. In image-based target tracking, the mode of the target is observed as a Markov modulated Poisson process (MMPP) and the aim is to compute optimal estimates of the target's state. We present a reduced complexity algorithm in two steps. First, a gauge transformation is used to reexpress the filtering equations in a form that is computationally more efficient for time discretization than naive discretization of the filtering equations. Second, a spatial aggregation algorithm with guaranteed performance bounds is presented for the time-discretized filters. A numerical example illustrating the performance of the resulting reduced-complexity filtering algorithms for a switching turn-rate model is presented.

A reduced-complexity multistage detection algorithm for DS-CDMA systems in AWGN channels

In this paper a new, reduced-complexity algorithm for multistage detection of direct-sequence code division multiple access (DS-CDMA) signals in additive white Gaussian noise (AWGN) channels is presented. Bit error rate performance of the new algorithm is determined via Monte Carlo simulation, and it is shown that the new algorithm, although designed to reduce computational complexity, performs better than conventional multistage detection.