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Binary sequences with low aperiodic autocorrelation levels, defined in terms of the peak sidelobe level (PSL) and/or merit factor, have many important engineering applications, such as radars, sonars, spread spectrum communications, system identification, and cryptography. Searching for low autocorrelation binary sequences (LABS) is a notorious combinatorial problem, and has been chosen to form a benchmark test for constraint solvers. Due to its prohibitively high complexity, an exhaustive search solution is impractical, except for relatively short lengths. Many suboptimal algorithms have been introduced to extend the LABS search for lengths of up to a few hundred. In this paper, we address the challenge of discovering even longer LABS by proposing an evolutionary algorithm (EA) with a new combination of several features, borrowed from genetic algorithms, evolutionary strategies (ES), and memetic algorithms. The proposed algorithm can efficiently discover long LABS of lengths up to several thousand. Record-breaking minimum peak sidelobe results of many lengths up to 4096 have been tabulated for benchmarking purposes. In addition, our algorithm design can be easily adapted to tackle various extensions of the LABS problem, say, with a generic sidelobe criterion and/or for possibly nonbinary sequences.

Many information processing problems can be transformed into some form of eigenvalue or singular value problems. Eigenvalue decomposition (EVD) and singular value decomposition (SVD) are usually used for solving these problems. In this paper, we give an introduction to various neural network implementations and algorithms for principal component analysis (PCA) and its various extensions. PCA is a statistical method that is directly related to EVD and SVD. Minor component analysis (MCA) is a variant of PCA, which is useful for solving total least squares (TLSs) problems. The algorithms are typical unsupervised learning methods. Some other neural network models for feature extraction, such as localized methods, complex-domain methods, generalized EVD, and SVD, are also described. Topics associated with PCA, such as independent component analysis (ICA) and linear discriminant analysis (LDA), are mentioned in passing in the conclusion. These methods are useful in adaptive signal processing, blind signal separation (BSS), pattern recognition, and information compression.

The radial basis function (RBF) network has its foundation in the conventional approximation theory. It has the capability of universal approximation. The RBF network is a popular alternative to the well-known multilayer perceptron (MLP), since it has a simpler structure and a much faster training process. In this paper, we give a comprehensive survey on the RBF network and its learning. Many aspects associated with the RBF network, such as network structure, universal approimation capability, radial basis functions, RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described. We also compare the features and capability of the two models.

Based on the minimum mean squared error (MMSE) between the data stream and the linear combiner output, a new multiuser detection (MUD) algorithm that combines space–time (ST) processing and antenna array on direct-sequence CDMA signals is proposed. The proposed ST-MUD algorithm is proved to be equivalent to two existing MMSE-based ST-MUD algorithms, and the theoretical BER performances for all the three algorithms are the same. The most attractive feature of the new ST-MUD algorithm is based on the fact that the new method does not require explicit estimation of channel and signaling information. This avoids any channel estimation error, and the method is thus more robust and more accurate than the other two ST-MUD algorithms in practical implementation. Adaptation of the proposed ST-MUD algorithm is implemented by using training sequences. Performance of this new multiuser detector is compared with that of two existing MMSE multiuser detectors and the conventional single-user space–time rake receiver through simulations. The proposed ST-MUD algorithm provides a performance better than existing algorithms and is especially suitable for practical CDMA systems.

Cognitive radio (CR) technology is a promising way to improve the bandwidth efficiency of underutilized radio spectra. For practical CR systems with limited a priori knowledge of the primary users' signal characteristics, spectrum sensing is mainly based on energy detection and cyclostationary feature detection. Energy detection is simple and practical but becomes ineffective at a low signal-to-interference-and-noise ratio (SINR). Conventional cyclostationary feature detection based on cyclic spectrum estimation can robustly detect weak signals from primary users by only exploiting the cyclostationarity property of communication signals. However, the high implementation complexity it requires limits its widespread usage. In the literature, the use of smart-antenna technology is suggested to track the locations of the primary users and apply transmit beamforming to avoid spatial interference with their signals. The objective of this paper is to establish adaptive cyclostationary (receive) beamforming as an effective spectrum-sensing method with affordable complexity for multiple-antenna cognitive radio. Specifically, we introduce a new spectrum-sensing method that exploits a recently proposed beamforming algorithm, called the adaptive cross-self-coherent-restoral (ACS) algorithm. The complexity of the resultant algorithm is higher than that of the energy detector but is at least an order of magnitude smaller than that of the previous cyclostationary feature detectors, such as the Fourier spectrum cyclic density analysis method and its multitaper-Loe?ve version. Their performances for spectrum sensing are empirically evaluated and compared in detail in an example.

One of the main benefits of the cyclostationary beamforming algorithms is their ability to extract signals from co-channel interference with only a knowledge of the cycle frequency. In this paper, we study the popular cyclostationary beamformers, and propose five new algorithms, namely, the adaptive cyclic adaptive beamforming (ACAB), adaptive cross-SCORE (ACS), constrained least-squares (CLS), adaptive phase-SCORE (APS), and maximal constrained autocorrelation (MCA) algorithms. All these algorithms are adaptive and have a computational complexity of O(n 2) complex multiplications, where n is the number of array elements. A comparative study of these algorithms is made based on numerical simulations. Each of these algorithms has specific application scenarios. The ACS and the APS algorithms are particularly suited for very adverse signal environments. The ACAB, MCA and cyclic adaptive beamforming (CAB, from the work of Wu and Wong) algorithms can provide good performance in the case of medium or weak interference, while the CLS algorithm is especially suitable for weak interference. The CAB algorithm is shown to be a special case of the least-square self-coherent restoral (LS-SCORE) algorithm. Some insights as to how one can assign carrier frequency and symbol rate during digital modulation are also suggested. The proposed adaptive algorithms are easy to implement, and thus are very promising for applications in wireless and mobile communications.

The radiation characteristics of a uniform circular array with linear centrally-fed dipole elements, used for smart antenna systems, are analyzed in this paper. The radiation patterns of the array and the gain of the array over a single element are derived based on the thin-wire model. Numerical simulations using the thin-wire model and the method of moments (MoM) have been conducted and compared for a half-wavelength dipole array. Simulations using the MoM demonstrate that the wire diameter of the elements has no significant effect on normalized azimuthal and elevation patterns for a given pattern design. Normalized azimuthal patterns by the thin-wire model and the MoM are in good agreement with each other, while normalized elevation patterns by the thin-wire model are inaccurate since mutual coupling is not considered in the model. Wire diameter affects the radiation resistance and thus determines the amplitude of the radiation field.

Immature dendritic cells (iDCs) have been shown to be able to induce peripheral T-cell tolerance through distinct pathways. Here, we investigated the tolerogenic property of recipient iDCs whose maturation was arrested by a dominant negative mutant of inhibitor of nuclear factor kappa-B kinase 2 (dnIKK2) gene. We found that dnIKK2-iDCs presented a typical semi-mature morphology and expressed lower levels of CD80 and CD86, slightly higher MHC-II than untransfected iDCs. The expression of these molecules had no significant change even dnIKK2-iDCs were pulsed by donor antigen. In primary mixed leukocyte reaction (MLR), dnIKK2-iDCs exhibited impaired ability to stimulate allogeneic T-cells, but induced CD4(+)CD25(-) T-cell formation. In co-culture MLR, these CD4(+)CD25(-) T-cells suppressed T-cell alloreaction in an antigen-specific manner. Besides, CD4(+)CD25(-) T-cells inhibited IL-2 and IFN-γ release, whereas promoted IL-10 and TGF-β secretion. These data suggested recipient dnIKK2-iDCs could maintain peripheral tolerance through down-regulating costimulatory molecule expressions and inducing CD4(+)CD25(-) T-cell formation.

Matrix factorization or factor analysis is an important task that is helpful in the analysis of high-dimensional real-world data. SVD is a classical method for matrix factorization, which gives the optimal low-rank approximation to a real-valued matrix in terms of the squared error. Many application areas, including information retrieval, pattern recognition, and data mining, require processing of binary rather than real data.

Due to feedback connections, recurrent neural networks (RNNs) are dynamic models. RNNs can provide more compact structure for approximating dynamic systems compared to feedforward neural networks (FNNs). For some RNN models such as the Hopfield model and the Boltzmann machine, the fixed-point property of the dynamic systems can be used for optimization and associative memory. The Hopfield model is the most important RNN model, and the Boltzmann machine as well as some other stochastic dynamic models are proposed as its generalization. These models are especially useful for dealing with combinatorial optimization problems (COPs), which are notorious NPcomplete problems. In this paper, we provide a state-of-the-art introduction to these RNN models, their learning algorithms as well as their analog implementations. Associative memory, COPs, simulated annealing (SA), chaotic neural networks and multilevel Hopfield models are also important topics treated in this paper.

Introduction OFDM, also known as simultaneous MFSK , has been widely implemented in high-speed digital communications in delay-dispersive environments. It is a multicarrier modulation (MCM) technique. OFDM was first proposed by Chang in 1966. Chang proposed the principle of transmitting messages simultaneously over multiple carriers in a linear band-limited channel without ISI and ICI. The initial version of OFDM employed a large number of oscillators and coherent demodulators. In 1971, DFT was applied to the modulation and demodulation process by Weinstein and Ebert. In 1980, Peled and Ruiz introduced the notion of cyclic prefix to maintain frequency orthogonality over the dispersive channel. The first commercial OFDM-based wireless system is the ETSI DAB standard proposed in 1995. A wide variety of wired and wireless communication standards are based on the OFDM or MCM technology. Examples are digital broadcasting systems such as DAB, DVB-T (Terrestrial DVB), and DVB-H; home networking such as digital subscriber line (xDSL) technologies; wireless LAN standards such as HyperLAN/2 and IEEE 802.11a/g/n; wireless MANs such as IEEE 802.16a/e (WiMAX), ETSI HiperACESS, IEEE 802.20 (mobile-Fi), WiBro, and HiperMAN2; wireless WANs such as 3GPP LTE and 3GPP2 UMB; powerline communications such as HomePlug; wireless PANs such as UWB radios (IEEE 802.15.3a/3c/4a). It is commonly deemed that OFDM is a major modulation technique for beyond-3G wireless multimedia communications. Features of OFDM technology In OFDM technology, the multiple carriers are called subcarriers , and the frequency band occupied by the signal carried by a subcarrier is called a sub-band .

Introduction UWB technology, also known as impulse radio , was first used to transmit Morse codes by Marconi in 1900 through the transatlantic telegraph. Modern UWB technology has been used for radar and communications since the 1960s. Like CDMA systems, early UWB systems were designed for military covert radar and communications. The early applications of UWB technology were primarily related to radar, driven by the fine-ranging resolution that comes with large bandwidth. UWB technology for wireless communications was pioneered by Scholtz. With the intent of operating UWB in an unlicensed mode that overlaps licensed bands, the FCC issued rules under the FCC Rules and Regulations Part 15 for UWB operation in February 2002. The FCC defined a UWB transmitter as “an intentional radiator that, at any point in time, has a fractional bandwidth equal to or greater than 0.20, or has a UWB bandwidth equal to or greater than 500 MHz, regardless of the fractional bandwidth”. “The UWB bandwidth is the frequency band bounded by the points that are 10 dB below the highest radiated emission, as based on the complete transmission system including the antenna.” According to the FCC regulations, the transmitter sends pulses with a bandwidth of at least 500 MHz that is within the band 3.1 to 10.6 GHz, for output power densities below −41.25 dBm/MHz. The FCC Part 15 limit of 500 µV/m at 3 meters is equivalent to an effective isotropic radiated power (EIRP) of −41.25 dBm/MHz.

In this paper, the dynamic spectrum access problem for cognitive radio (CR) networks is formulated as maximizing the sum channel capacity while satisfying the power budgets of individual secondary user radios as well as the SINR constraints on both the secondary and primary users. By applying the Karush-Kuhn-Tucker theorem, we derive a water-filling soluton. An iterative water-filling algorithm is proposed for implementing joint channel and power allocation in a dynamically changing set of available channels. The proposed algorithm has a complexity that increases linearly with both the number of channels and the number of users.