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This paper focuses on the problem of maximizing the minimum power gain in a wide main lobe for an array antenna. To obtain the desired power gain pattern, current literatures solve the problem known as shaped beam pattern synthesis (SBPS) by setting the beam pattern expression as the cost function. The power gain in a certain direction of the array antenna is comprehensively decided by the array radiated power density and the total radiated power. Both these two parameters are affected by the selection of array elements’ weights. However, the SBPS-based methods do not take the total radiated power into account. Therefore, the SBPS-based methods cannot obtain the power gain pattern directly, and the optimal power gain pattern, consequently, cannot be guaranteed. To overcome this deficiency, this paper maximizes the minimum power gain in the desired wide main lobe by directly setting the power gain expression as the cost function. Aiming at optimizing the cost function, this paper first sets up a power gain optimization problem which has concave form. Termed this optimization problem as the power gain pattern synthesis (PGPS) problem. The concave PGPS problem is then converted to a series of convex subproblems and an iterative algorithm is proposed to solve it efficiently. Solution to the PGPS problem is proven to be able to achieve higher minimum power gain in a desired wide main lobe than the results from the SBPS-based algorithms. Simulations are carried out with both linear array antenna and planar array antenna. Comparisons with the existed SBPS-based algorithms validate the effectiveness and advantages of the proposed PGPS-based algorithm. In addition, further improvement on suppressing the sidelobe level for the PGPS problem is discussed. [ABSTRACT FROM AUTHOR]

This paper describes a new method of power-grid signal variation measurement, consisting of a demodulation algorithm and a set of measures (indices). The measures that will be presented are the abstract functionals, operating on the complex envelope of a signal. This paper presents a demodulation algorithm, definitions of proposed functionals, results of simulations, as well as laboratory experiments. [ABSTRACT FROM AUTHOR]

Channel order estimation is a critical step in most blind single-input multiple-output (SIMO) channel identification/equalization algorithms. Several methods for estimating either the true channel order or its most significant part (the so-called effective channel order) have been recently proposed, but a solution able to work in practical scenarios (low or moderate signal-to-noise ratios (SNR5) and channels with small leading and/or trailing coefficients) has not been found yet. In this paper, a new criterion for effective channel order detection of SIMO channels is presented. The method is based on the fact that the cost function typically used in blind identification algorithms decreases monotonically with the estimated channel order, whereas for blind equalization algorithms, the cost function increases monotonically. In this paper, it is shown that a straightforward combination of both cost functions attains its minimum at the correct channel order even for moderate SNRS. The proposed method is able to work with small data sets, colored signals, and channels with small head and tail taps, which is a common problem in communication applications. The improvement of the proposed criterion over a number of existing algorithms is demonstrated through simulations. [ABSTRACT FROM AUTHOR]

This paper considers the coherent integration problem for a maneuvering target with complex motions, where the velocity, acceleration, and jerk result in respectively the first-order range migration (FRM), second-order range migration (SRM), and third-order range migration (TRM) within the coherent pulse interval. A new coherent integration algorithm based on generalized keystone transform (KT) and second-order dechirp process is proposed, which employs the third-order KT, six-order KT, second-order KT, and fold factor searching to correct the TRM, SRM, and FRM, respectively. The range migration change during each step and computational complexity are also theoretically analyzed. Compared with the generalized Radon Fourier transform (GRFT) algorithm, the presented method can avoid the blind speed sidelobe (BSSL) and acquire close integration performance but with much lower computational cost. Simulations are provided to demonstrate the effectiveness. Finally, a generalized method, named generalized KT and generalized dechirp process (GKTGDP), is also introduced for the maneuvering target with arbitrary high-order range migration. [ABSTRACT FROM PUBLISHER]

This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The CBSs use amplify-and-forward (AF) relaying and coordinated beamforming to relay the primary signals and transmit their own signals. The objective is to minimize the overall transmit power of the CBSs given the rate requirements of the PU and the cognitive users (CUs). We show that the relaying matrices have unity rank and perform two functions: Matched filter receive beamforming and transmit beamforming. We then develop two efficient algorithms to find the optimal solution. The first one has a linear convergence rate and is suitable for distributed implementation, while the second one enjoys superlinear convergence but requires centralized processing. Further, we derive the beamforming vectors for the linear conventional zero-forcing (CZF) and prior zero-forcing (PZF) schemes, which provide much simpler solutions. Simulation results demonstrate the improvement in terms of outage performance due to the cooperation between the primary and cognitive systems. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of designing the projection matrix \Phi for a compressive sensing (CS) system in which the dictionary \Psi is assumed to be given. The optimal projection matrix design is formulated in terms of finding those \Phi such that the Frobenius norm of the difference between the Gram matrix of the equivalent dictionary \Phi \Psi and the identity matrix is minimized. A class of the solutions is derived in a closed-form, which is a generalization of the existing results. More interestingly, it is revealed that this solution set is characterized by an arbitrary orthonormal matrix. This freedom is then used to further enhance the performance of the CS system by minimizing the coherence between the atoms of the equivalent dictionary. An alternating minimization-based algorithm is proposed for solving the corresponding minimization problem. Experiments are carried out and simulations show that the projection matrix obtained by the proposed approach significantly improves the signal recovery accuracy of the CS system and outperforms those by existing algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we present a theoretical convergence analysis of the affine projection algorithm (APA) based on the arguments of energy conservation. Although the APA and its convergence analysis have been widely studied, the dependency of weight-error vector on past noise is usually neglected for simplicity. To obtain accurate theoretical results for the APA, we here consider the dependency between the weight-error vector and past noise in the mean-square analysis presented by Shin and Sayed in [“Mean-square performance of a family of affine projection algorithms,” IEEE Transactions on Signal Processing, vol. 52, no. 1, pp. 90–102, January 2004]. Through this work, we can also theoretically analyze the behavior of the periodic APA, which updates its weights periodically. Simulation results show that our theoretical results coincide closely with simulations. [ABSTRACT FROM AUTHOR]

This paper considers the degrees of freedom (DOF) for a K user multiple-input multiple-output (MIMO) M \times N interference channel using interference alignment (IA). A new performance metric for evaluating the efficacy of IA algorithms is proposed, which measures the extent to which the desired signal dimensionality is preserved after zero-forcing the interference at the receiver. Inspired by the metric, two algorithms are proposed for designing the linear precoders and receive filters for IA in the constant MIMO interference channel with a finite number of symbol extensions. The first algorithm uses an eigenbeamforming method to align sub-streams of the interference to reduce the dimensionality of the interference at all the receivers. The second algorithm is iterative, and is based on minimizing the interference leakage power while preserving the dimensionality of the desired signal space at the intended receivers. The improved performance of the algorithms is illustrated by comparing them with existing algorithms for IA using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Source localization especially direction-of-arrival (DOA) estimation using sensor arrays is of considerable interest in both classical array signal processing and radar applications. Most radar systems are designed under the line-of-sight (LOS) assumption with multipath echos treated as undesired clutter noise. Strong multipath, therefore, has a negative impact on the resolution of the radar systems and their ability in accurately localizing the target. Rather than treating multipath as a detrimental effect, the paper introduces time reversal (TR) to exploit spatial/multipath diversity in improving the capability of the existing localization algorithms. In particular, we design TR based range and DOA estimators that adaptively adjust the probing radar waveforms to the multipath characteristics of the environment. The benefits of the spatial/multipath diversity in the proposed DOA and range estimators are quantified by deriving the respective Cramér–Rao bounds (CRB) and comparing them with the analytical expressions for their conventional counterparts. Numerical simulations also confirm the benefit of applying TR to source localization algorithms especially at low signal-to-noise ratios below -5 dB. [ABSTRACT FROM AUTHOR]

Near field radar employing ultrawideband (UWB) signals with its high range resolution has great promise for various sensing applications. It enables non-contact measurement of precision devices with specular surfaces like an aircraft fuselage and wing, or a robotic sensor that can identify a human body in invisible situations. As one of the most promising radar algorithms, the range points migration (RPM) was proposed. This achieves fast and accurate surface extraction, even for complex-shaped objects, by eliminating the difficulty of connecting range points. However, in the case of a more complex shape whose variation scale is less than a pulsewidth, it still suffers from image distortion caused by multiple interference signals with different waveforms. As a substantial solution, this paper proposes a novel range extraction algorithm by extending the Capon method, known as frequency domain interferometry (FDI). This algorithm combines reference signal optimization with the original Capon method to enhance the accuracy and resolution for an observed range into which a deformed waveform model is introduced. The results obtained from numerical simulations and an experiment with bi-static extension of the RPM prove that super-resolution UWB radar imaging is accomplished by the combination between the RPM and the extended Capon methods, even for an extremely complex-surface target including edges. [ABSTRACT FROM AUTHOR]

It is well known that the motion of a target induces range migration, especially for high-resolution synthetic aperture radar (SAR) systems. Ground moving target imaging necessitates the correction of the unknown range migration. To finely refocus a moving target, one must accurately obtain the motion parameters for compensating the target trajectory. However, in practice, these parameters usually cannot be precisely estimated. This paper proposes a new imaging approach for ground moving targets without a priori knowledge of their motion parameters. In the devised method, the azimuth compression function is constructed in range frequency domain, which can eliminate the coupling effect between range and azimuth. Theoretical analysis confirms that the methodology can precisely focus targets without interpolation procedure. The effectiveness of the proposed imaging technique is demonstrated by both simulated and real airborne SAR data. [ABSTRACT FROM AUTHOR]

Multibaseline phase unwrapping is a critical processing procedure of multibaseline synthetic aperture radar interferometry (InSAR). It has an advantage over single-baseline phase unwrapping in discontinuous-terrain-height estimation. In this paper, the pixels' combination information of multiple InSAR interferograms with different baseline lengths is deeply investigated, and the term ambiguity vector is proposed to represent a pixel's ambiguity number of multiple interferograms. It is also revealed that pixels with the same ambiguity vector have an exclusive pattern among them. A fast cluster-analysis (CA)-based method is proposed for multibaseline phase unwrapping. In this method, all pixels are first clustered into different groups according to their patterns, and then, information of the cluster center is used to unwrap the phases of pixels group by group. Simulation results are shown to verify the effectiveness, efficiency, and noise robustness of CA multibaseline phase-unwrapping method. [ABSTRACT FROM AUTHOR]

Recent advances in electro-optical systems make them ideal for undersampling multiband signals with very high carrier frequencies. In this paper, we propose a new scheme for sampling and reconstructing of a multiband sparse signals that occupy a small part of a given broad frequency range under the constraint of a small number of sampling channels. The locations of the signal bands are not known a priori. The scheme, which we call synchronous multirate sampling (SMRS), entails gathering samples synchronously at few different rates whose sum is significantly lower than the Nyquist sampling rate. The signals are reconstructed by finding a solution of an underdetermined system of linear equations by applying a pursuit algorithm and assuming that the solution is composed of a minimum number of bands. The empirical reconstruction success rate is higher than obtained using previously published multicoset scheme when the number of sampling channels is small and the conditions for a perfect reconstruction in the multicoset scheme are not fulfilled. The practical sampling system which is simulated in our work consists of three sampling channels. Our simulation results show that a very high empirical success rate is obtained when the total sampling rate is five times higher than the total signal support of a complex signal with four bands. By comparison, a multicoset sampling scheme obtains a very high empirical success rate with a total sampling rate which is three times higher than the total signal support. However, the multicoset scheme requires 14 channels. [ABSTRACT FROM AUTHOR]

In this paper we propose an edge-directed error concealment (EDEC) algorithm, to recover lost slices in video sequences encoded by flexible macroblock ordering. First, the strong edges in a corrupted frame are estimated based on the edges in the neighboring frames and the received area of the current frame. Next, the lost regions along these estimated edges are recovered using both spatial and temporal neighboring pixels. Finally, the remaining parts of the lost regions are estimated. Simulation results show that compared to the existing boundary matching algorithm [1] and the exemplarbased inpainting approach [2], the proposed EDEC algorithm can reconstruct the corrupted frame with both a better visual quality and a higher decoder peak signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many mechanical systems. However, vibration signals are often corrupted by noise; therefore, the performance of diagnostic and prognostic algorithms is degraded. In this paper, a novel denoising structure is proposed and applied to vibration signals collected from a testbed of the helicopter main gearbox subjected to a seeded fault. The proposed structure integrates a denoising algorithm, feature extraction, failure prognosis, and vibration modeling into a synergistic system. Performance indexes associated with the quality of the extracted features and failure prognosis are addressed, before and after denoising, for validation purposes. [ABSTRACT FROM AUTHOR]

The FOCal Underdetermined System Solver (FOCUSS) algorithm has already found many applications in signal processing and data analysis, whereas the regularized M-FOCUSS algorithm has been recently proposed by Cotter et a!. for finding sparse solutions to an underdetermined system of linear equations with multiple measurement vectors. In this paper, we propose three modifications to the M-FOCUSS algorithm to make it more efficient for sparse and locally smooth solutions. First, motivated by the simultaneously autoregressive (SAR) model, we incorporate an additional weighting (smoothing) matrix into the Tikhonov regularization term. Next, the entire set of measurement vectors is divided into blocks, and the solution is updated sequentially, based on the overlapping of data blocks. The last modification is based on an alternating minimization technique to provide data-driven (simultaneous) estimation of the regularization parameter with the generalized cross-validation (GCV) approach. Finally, the simulation results demonstrating the benefits of the proposed modifications support the analysis. [ABSTRACT FROM AUTHOR]

In this paper, we investigate efficient wireless data access algorithms in mobile hotspots. We introduce a proxy cache (PC) and propose PC-based poll-each-read (P-PER) and PC-based callback (P-CB) data access algorithms to reduce the transmission cost over wireless links in mobile hotspots. An analytical model is developed, and extensive simulations are conducted to demonstrate the performance of P-PER and P-CB. It is shown that P-PER and P-CB can improve cache hit performance and significantly reduce transmission cost. A tradeoff between P-PER and P-CB suggests the need to use a hybrid proxy-based approach to attain optimal performance of wireless data access in mobile hotspots. [ABSTRACT FROM AUTHOR]

Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Likewise, weights-adjusted second-order blind identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to, contain both Gaussian AR and non-Gaussian i.i.d. sources, rendering WASOBI and EFICA severely suboptimal. In this paper, we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures. [ABSTRACT FROM AUTHOR]

This paper addresses the estimation of multipath channels with long impulse response at low signal-to-noise ratio (SNR). The channel sparseness impulse response is modeled by means of a Bernoulli-Gaussian process. Then, the optimization of the resulting posterior distribution resorts to Monte Carlo Markov chain (MCMC) simulation techniques. Special attention is devoted to noise correlation involved by matched filtering: Taking this correlation into account in the algorithm derivation leads to significantly improved performance for both amplitude and time-delay estimation. The method is also extended to cope with Doppler frequency offsets. In particular, simultaneous paths with different Doppler offsets can be estimated. Cramér-Rao lower bounds (CRLBs) are derived and presented together with simulation results. [ABSTRACT FROM AUTHOR]

Concurrently active motor units (MUs) of a given muscle can exhibit a certain degree of synchronous firings, and a certain degree of common variation in their firing rates. The former property is referred to as motor unit synchrony in the literature, which is termed motor unit innervation process (MUIP) correlation in this study. The latter is referred to as motor unit common drive and can be quantified by the common drive coefficient, which is the correlation coefficient between the smoothed firing rates of the two MUs. Both properties have important roles and implications in the generation and resulting characteristics of the myoelectric signal and for the development of signal processing algorithms in myoelectric signal (MES) applications. In order to study these implications and characteristics, in this paper estimation procedures are developed to quantify the degree of MUIP correlation and common drive as functions of physiological parameters. Also, the interaction between MUIP correlation and motor unit common drive is studied in a physiologically realistic simulation model. Neurons modeled by Hodgkin-Huxley systems form the framework of the simulation model in which excitation and synaptic characteristics can be modified. MUIP correlation and common drive degree and interaction are studied through a number of simulations. To support the simulation results, experimental in vivo motor unit trains were collected at low levels of contraction from 11 subjects, and decomposed into the constituent unit trains giving 50 concurrently active motor unit pairs. The simulation demonstrated that the innervation process correlation coefficient is controlled primarily by the postsynaptic conductance, 9syn, and was less than 0.05 mS/cm² for realistic values of 9syn. The common drive was found to be controlled by the exciting neuron input with no statistically significant interaction between it and the MUIP correlation. The experimental data gave results in close agreement with those of the simulation. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop efficient online adaptive algorithms for the generalized eigen-decomposition problem which arises in a variety of modern signal processing applications. First, we reinterpret the generalized eigen-decomposition problem as an unconstrained minimization problem by constructing a novel cost function. Second, by applying projection approximation method and recursive least-square (RLS) technique to the cost function, a parallel adaptive algorithm for a basis for the r-dimensional (r > 0) dominant generalized. eigen-subspace and a sequential algorithm based on deflation technique for the first v-dominant generalized eigenvectors are derived. These algorithms can be viewed as counterparts of the extended projection approximation subspace tracking (PAST) and PASTd algorithms, respectively. Furthermore, we modify the parallel algorithm to explicitly estimate the first v-generalized eigenvectors in parallel, not the generalized eigen-subspace. More important, the modified parallel algorithm can be used to extract multiple generalized eigenvectors of two nonstationary sequences, while the proposed sequential algorithm lacks this ability because of slow convergence of minor generalized eigenvectors due to error propagation of the deflation technique. Third, following convergence analysis methods for PAST and PASTd, we prove the asymptotic convergence properties of the proposed algorithms. Finally, computer simulations are performed to investigate the accuracy and the speed advantages of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A new way to teach advanced undergraduate and graduate students the theory of estimation and detection is described. The paper shows that the global system mobile (GSM) radio interface is well suited to the teaching of estimation and detection theory because it entails the understanding and application of several concepts within estimation and detection, as well as information theory, signal processing, and radio-wave propagation. With GSM now a mature system with specifications available in the public domain, the students created an open-source (OS) GSM simulator, including all four modulation and coding schemes (MCS 1,2,3,4) with puncturing, a channel estimator, a minimum phase prefilter, a Max log maximum a posteriori probability (MAP) equalizer, and a convolutional decoder. Studying and understanding the theory of estimation and detection is now possible by creating algorithms for the simulator and evaluating the impact of the algorithm on the GSM block error rate (BLER). Several experiments that are possible with the simulator are described. [ABSTRACT FROM AUTHOR]

Estimating the number of sources impinging on an array of sensors is a well-known and well-investigated problem. A common approach for solving this problem is to use an information theoretic criterion, such as Minimum Description Length (MDL) or the Akaike Information Criterion (AIC). The MDL estimator is known to be a consistent estimator, robust against deviations from the Gaussian assumption, and nonrobust against deviations from the point source and/or temporally or spatially white additive noise assumptions. Over the years, several alternative estimation algorithms have been proposed and tested. Usually, these algorithms are shown, using computer simulations, to have improved performance over the MDL estimator and to be robust against deviations from the assumed spatial model. Nevertheless, these robust algorithms have high computational complexity, requiring several multidimensional searches. In this paper, which is motivated by real-life problems, a systematic approach toward the problem of robust estimation of the number of sources using information theoretic criteria is taken. An MDL-type estimator that is robust against deviation from assumption of equal noise level across the array is studied. The consistency of this estimator, even when deviations from the equal noise level assumption occur, is proven. A novel low-complexity implementation method avoiding the need for multidimensional searches is presented as well, making this estimator a favorable choice for practical applications. [ABSTRACT FROM AUTHOR]

This paper introduces a fast implementation of the power iteration method for subspace tracking, based on an approximation that is less restrictive than the well-known projection approximation. This algorithm, referred to as the fast approximated power iteration (API) method, guarantees the orthonormality of the subspace weighting matrix at each iteration. Moreover, it out-performs many subspace trackers related to the power iteration method, such as PAST, NIC, NP3, and OPAST, while having the same computational complexity. The API method is designed for both exponential windows and sliding windows. Our numerical simulations show that sliding windows offer a faster tracking response to abrupt signal variations. [ABSTRACT FROM AUTHOR]

In almost all analyzes of the least mean-square (LMS) finite impulse response (FIR) adaptive algorithm, it is assumed that the length of the adaptive filter is equal to that of the unknown system impulse response. However, in many practical situations, a deficient length adaptive filter, whose length is less than that of the unknown system, is employed, and analysis results for the sufficient length LMS algorithm are not necessarily applicable to the deficient length case. Therefore, there is an essential need to accurately quantify the behavior of the LMS algorithm for realistic situations where the length of the adaptive filter is deficient. In this paper, we present a performance analysis for the deficient length LMS adaptive algorithm for correlated Gaussian input data and using the common independence assumption. Exact expressions that completely characterize the transient and steady-state means square performances of the algorithm are developed, which lead to new insights into the statistical behavior of the deficient length LMS algorithm. Simulation experiments illustrate the accuracy of the theoretical results in predicting the convergence behavior of the algorithm. [ABSTRACT FROM AUTHOR]

This paper describes a solution for automated analysis of circuit breaker operation. The analysis is based on a record of waveforms taken from the circuit breaker control circuit by using a portable recorder and manually forcing an operation of the breaker. This solution was driven by a need to perform the analysis in a more timely and consistent manner than what is available with existing technology. The solution is implemented using advanced wavelet transforms for waveform feature extraction and an expert system for decision making. A web-based database solution for storing and retrieving both the field-recorded and processed data is also implemented. The software is developed in two versions: for field (substation) as well as off-line (office) applications. [ABSTRACT FROM AUTHOR]

Recently, novel vector quantization techniques in multiple nonorthogonal domains for both waveform and model-based signal characterization that give an improved qualitative and quantitative signal coding performance as compared to vector quantization in single domain have been reported. In these techniques, vectors are formed either directly from the signal waveform or from the model parameters extracted from the signal. Then, the vectors are represented in multiple nonorthogonal domains. The encoder chooses the domain that best represents the vector according to a predetermined criterion. An iterative codebook enhancement algorithm, applicable to both waveform and model-based vector quantization in nonorthogonal domains is developed and presented in this paper. In this algorithm, each set of codebooks in a given domain is retrained by the vectors that were best represented by that particular set of codebooks in the most recent iteration. The algorithm is applied successfully and extensive simulation results yield considerable performance enhancement of the vector quantization in nonorthogonal domains, for a given bit rate. Sample results are provided which demonstrate the improved performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel receiver structure for cancelling self-mixed interferences in direct conversion receivers. The proposed receiver structure is based on the adaptive interference cancellation (IC) using an adaptive algorithm such as a least-mean-square (LMS). The reference signal for the adaptive IC is obtained from the received signal itself. Thus, the proposed method can mitigate the self-mixed interference without any additional information on the self-mixed interference. Moreover, the signal-tointerference ratio (SIR) performance of the proposed cancellation method is theoretically analyzed. The simulation results indicate that the numerical SIR performance is well matched with the theoretical SIR performance. Also, the proposed cancellation method cancel the self-mixed interference almost perfectly even when the power of the received interference signal is 50dB larger than that of the received desired signal. [ABSTRACT FROM PUBLISHER]

A novel number theoretic transform called Inverse Gray Robust Symmetrical Number System (IGRSNS) is proposed for error control coding in this paper. IGRSNS is obtained by modifying Robust Symmetrical Number System (RSNS) that was proposed earlier, using Inverse Gray code property. Due to ambiguities present in each residue, RSNS has a short dynamic range (DR) compared to that in other number systems. The short DR of RSNS enables it to be effectively used for error detection without the addition of any redundant modulus as in Redundant Residue Number System. Although RSNS has a large redundant range, its detection ability is not optimal due to the Gray code property associated with it. In an attempt to overcome this limitation, we have proposed Inverse Gray coding to be combined with RSNS in increasing its effectiveness in error detection. The resulting IGRSNS thus inherits properties of both RSNS and Gray code. Analysis and simulations show that IGRSNS has a near-optimal error detection ability. The potential of IGRSNS for error correction is also investigated. Further, a novel error correction algorithm using one redundant modulus is proposed. Simulations show that the proposed algorithm performs well under all cases of single bit errors. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of resource allocation in the DFT-Spread-OFDMA (DFT-S-OFDMA) uplink. We show that the resource allocation problem can be formulated as a set packing problem, which in general is NP-hard. We propose polynomial-time message-passing based algorithms, one of which is guaranteed to yield a solution that is within a constant fraction of the optimal solution and is also asymptotically optimal in the limit as the number of subcarriers in the system goes to infinity. The message-passing based algorithm is also extended to solve the resource allocation problem over a multi-cell uplink in a distributed fashion. Our algorithms account for finite input alphabets and non-ideal practical outer codes. Extensive simulations are performed to assess the performance of the proposed algorithms and it is shown that they yield near-optimal solutions at a low complexity and with a low memory requirement. [ABSTRACT FROM PUBLISHER]