In this letter, the tradeoff between symbol rate and diversity gain of Space-Time Block Codes (STBCs) with linear receivers is considered. It is known that Group Orthogonal-Toeplitz Codes (GOTCs) can achieve a good tradeoff with linear receivers. However, the symbol rate of GOTCs is limited to that of the base Orthogonal Space-Time Block Codes (OSTBCs). We propose to simply change the GOTC base codes from OSTBCs to Quasi-Orthogonal Space-Time Block Codes (Q-OSTBCs). Q-OSTBCs can improve the symbol rate of GOTCs at the expense of diversity gain. Simulation results show that Q-OSTBC based GOTCs can improve the tradeoff between symbol rate and diversity gain over that of the original GOTCs.

Capacitor charging modeling efficiently and accurately represents power consumption current of CMOS digital circuits and actualizes co-simulation of AC power noise including the interaction with on-chip and on-board integrated power delivery network (PDN). It is clearly demonstrated that the AC power noise is dominantly characterized by the frequency-dependent impedance of PDN and also by the operating frequency of circuits as well. A 65 nm CMOS chip exhibits the AC power noise components in substantial relation with the parallel resonance of the PDN seen from on-chip digital circuits. An on-chip noise monitor measures in-circuit power supply voltage, while a near-field magnetic probing derives on-board power supply current. The proposed co-simulation well matches the power noise measurements. The proposed AC noise co-simulation will be essentially applicable in the design of PDNs toward on-chip power supply integrity (PSI) and off-chip electromagnetic compatibility (EMC).

L-band SiGe HBT frequency-tunable differential amplifiers with dual-bandpass or dual-bandstop responses have been developed for the next generation adaptive and/or reconfigurable wireless radios. Varactor-loaded dual-band resonators comprised of series and parallel LC circuits are employed in the output circuit of differential amplifiers for realizing dual-bandpass responses as well as the series feedback circuit for dual-bandstop responses. The varactor-loaded series and parallel LC resonator can provide a wider frequency separation between dual-band frequencies than the stacked LC resonator. With the use of the varactor-loaded dual-band resonator in the design of the low-noise SiGe HBT differential amplifier with dual-bandpass responses, the lower-band frequency can be varied from 0.58 to 0.77 GHz with a fixed upper-band frequency of 1.54 GHz. Meanwhile, the upper-band frequency can be varied from 1.1 to 1.5 GHz for a fixed lower-band frequency of 0.57 GHz. The dual-band gain was 6.4 to 13.3 dB over the whole frequency band. In addition, with the use of the varactor-loaded dual-band resonator in the design of the low-noise differential amplifier with dual-bandstop responses, the lower bandstop frequency can be varied from 0.38 to 0.68 GHz with an upper bandstop frequency from 1.05 to 1.12 GHz. Meanwhile, the upper bandstop frequency can be varied from 0.69 to 1.02 GHz for a lower bandstop frequency of 0.38 GHz. The maximal dual-band rejection of gain was 14.4 dB. The varactor-loaded dual-band resonator presented in this paper is expected to greatly contribute to realizing the next generation adaptive and/or reconfigurable wireless transceivers.

This paper investigates the dominant impact on the interference rejection combining (IRC) receiver due to the downlink reference signal (RS) based covariance matrix estimation scheme. When the transmission modes using the cell-specific RS (CRS) in LTE/LTE-Advanced are assumed, the property of the non-precoded CRS is different from that of the data signals. This difference poses two problems to the IRC receiver. First, it results in different levels of accuracy for the RS based covariance matrix estimation. Second, assuming the case where the CRS from the interfering cell collides with the desired data signals of the serving cell, the IRC receiver cannot perfectly suppress this CRS interference. The results of simulations assuming two transmitter and receiver antenna branches show that the impact of the CRS-to-CRS collision among cells is greater than that for the CRS interference on the desired data signals especially in closed-loop multiple-input multiple-output (MIMO) systems, from the viewpoint of the output signal-to-interference-plus-noise power ratio (SINR). However, the IRC receiver improves the user throughput by more than 20% compared to the conventional maximal ratio combining (MRC) receiver under the simulation assumptions made in this paper even when the CRS-to-CRS collision is assumed. Furthermore, the results verify the observations made in regard to the impact of inter-cell interference of the CRS for various average received signal-to-noise power ratio (SNR) and signal-to-interference power ratio (SIR) environments.

Finding DC operating points of nonlinear circuits is an important and difficult task. The Newton-Raphson method adopted in the SPICE-like simulators often fails to converge to a solution. To overcome this convergence problem, homotopy methods have been studied from various viewpoints. However, most previous studies are mainly focused on the bipolar transistor circuits and no paper presents the global convergence theorems of homotopy methods for MOS transistor circuits. Moreover, due to the improvements and advantages of MOS transistor technologies, extending the homotopy methods to MOS transistor circuits becomes more and more necessary and important. This paper proposes two nonlinear homotopy methods for MOS transistor circuits and proves the global convergence theorems for the proposed MOS nonlinear homotopy method II. Numerical examples show that both of the two proposed homotopy methods for MOS transistor circuits are more effective for finding DC operating points than the conventional MOS homotopy method and they are also capable of finding DC operating points for large-scale circuits.

A fractional-N phase-locked loop (PLL) is designed for the DigRF interface. The digital part of the PLL mainly consists of a dual-mode phase frequency detector (PFD), a digital counter, and a digital delta-sigma modulator (DSM). The PFD can operate on either 52 MHz or 26 MHz reference frequencies, depending on its use of only the rising edge or both the rising and the falling edges of the reference clock. The interface between the counter and the DSM is designed to give enough timing margin in terms of the signal round-trip delay. The circuitry is implemented using a 90-nm CMOS process technology with a 1.2-V supply, draining 1 mA.

This paper proposes a spectrum-overlapped resource management (SORM) technique where each user equipment (UE) can ideally obtain the frequency selection diversity gain under multi-user environments. In the SORM technique for cellular systems, under assumption of adopting a soft canceller with minimum mean square error (SC/MMSE) turbo equalizer, an evolved node B (eNB) accepts overlapped frequency resource allocation. As a result, each UE can use the frequency bins having the highest channel gain. However, the SORM becomes non-orthogonal access when the frequency bins having high channel gain for UEs are partially identical. In this case, the inter-user interference (IUI) caused by overlapping spectra among UEs is eventually canceled out by using the SC/MMSE turbo equalizer. Therefore, SORM can achieve better performance than orthogonal access e.g. FDMA when the IUI is completely canceled. This paper demonstrates that SORM has the potential to improve transmission performance, by extrinsic information transfer (EXIT) analysis. Moreover, this paper evaluates the block error rate (BLER) performance of the SORM and the FDMA. Consequently, this paper shows that the SORM outperforms the FDMA.

In this paper, we propose a method for designing genetically optimized Linguistic Models (LM) with the aid of fuzzy granulation. The fundamental idea of LM introduced by Pedrycz is followed and their design framework based on Genetic Algorithm (GA) is enhanced. A LM is designed by the use of information granulation realized via Context-based Fuzzy C-Means (CFCM) clustering. This clustering technique builds information granules represented as a fuzzy set. However, it is difficult to optimize the number of linguistic contexts, the number of clusters generated by each context, and the weighting exponent. Thus, we perform simultaneous optimization of design parameters linking information granules in the input and output spaces based on GA. Experiments on the coagulant dosing process in a water purification plant reveal that the proposed method shows better performance than the previous works and LM itself.

In content services where people purchase and download large-volume contents, minimizing network traffic is crucial for the service provider and the network operator since they want to lower the cost charged for bandwidth and the cost for network infrastructure, respectively. Traffic localization is an effective way of reducing network traffic. Network traffic is localized when a client can obtain the requested content files from other a near-by altruistic client instead of the source servers. The concept of the peer-assisted content distribution network (CDN) can reduce the overall traffic with this mechanism and enable service providers to minimize traffic without deploying or borrowing distributed storage. To localize traffic effectively, content files that are likely to be requested by many clients should be cached locally. This paper presents a novel traffic engineering scheme for peer-assisted CDN models. Its key idea is to control the behavior of clients by using content-oriented incentive mechanism. This approach enables us to optimize traffic flows by letting altruistic clients download content files that are most likely contributed to localizing traffic among clients. In order to let altruistic clients request the desired files, we combine content files while keeping the price equal to the one for a single content. This paper presents a solution for optimizing the selection of content files to be combined so that cross traffic in a network is minimized. We also give a model for analyzing the upper-bound performance and the numerical results.

In numerical simulations using massively parallel computers like GPGPU (General-Purpose computing on Graphics Processing Units), we often need to transfer computational results from external devices such as GPUs to the main memory or secondary storage of the host machine. Since size of the computation results is sometimes unacceptably large to hold them, it is desired that the data is compressed and stored. In addition, considering overheads for transferring data between the devices and host memories, it is preferable that the data is compressed in a part of parallel computation performed on the devices. Traditional compression methods for floating-point numbers do not always show good parallelism. In this paper, we propose a new compression method for massively-parallel simulations running on GPUs, in which we combine a few successive floating-point numbers and interleave them to improve compression efficiency. We also present numerical examples of compression ratio and throughput obtained from experimental implementations of the proposed method runnig on CPUs and GPUs.

Geographic routing uses the geographical location information provided by nodes to make routing decisions. However, the nodes can not obtain accurate location information due to the effect of measurement error. A new routing strategy using maximum expected distance and angle (MEDA) algorithm is proposed to improve the performance and promote the successive transmission rate. We firstly introduce the expected distance and angle, and then we employ the principal component analysis to construct the object function for selecting the next hop node. We compare the proposed algorithm with maximum expectation within transmission range (MER) and greedy routing scheme (GRS) algorithms. Simulation results show that the proposed MEDA algorithm outperforms the MER and GRS algorithms with higher successive transmission rate.

We focus on the cooperative mobile positioning based on the received signal strength for heterogeneous environments. We use the least squares method to estimate the channel attenuation coefficients, and hyperbolic method to determine the position. For increasing accuracy, we use different weighting to the adjacent users' data in the attenuation coefficients estimation.

Predicting network throughput is important for network-aware applications. Network throughput depends on a number of factors, and many throughput prediction methods have been proposed. However, many of these methods are suffering from the fact that a distribution of traffic fluctuation is unclear and the scale and the bandwidth of networks are rapidly increasing. Furthermore, virtual machines are used as platforms in many network research and services fields, and they can affect network measurement. A prediction method that uses pairs of differently sized connections has been proposed. This method, which we call connection pair, features a small probe transfer using the TCP that can be used to predict the throughput of a large data transfer. We focus on measurements, analyses, and modeling for precise prediction results. We first clarified that the actual throughput for the connection pair is non-linearly and monotonically changed with noise. Second, we built a previously proposed predictor using the same training data sets as for our proposed method, and it was unsuitable for considering the above characteristics. We propose a throughput prediction method based on the connection pair that uses ν-support vector regression and the polynomial kernel to deal with prediction models represented as a non-linear and continuous monotonic function. The prediction results of our method compared to those of the previous predictor are more accurate. Moreover, under an unstable network state, the drop in accuracy is also smaller than that of the previous predictor.

A novel design is proposed to implement highly parallel stream join operators on a field-programmable gate array (FPGA), by examining handshake join algorithm for hardware implementation. The proposed design is evaluated in terms of the hardware resource usage, the maximum clock frequency, and the performance. Experimental results indicate that the proposed implementation can handle considerably high input rates, especially at low match rates. Results of simulation conducted to optimize size of buffers included in join and merge units give a new intuition regarding static and adaptive buffer tuning in handshake join.

In this paper, using the Linear Time Variant (LTV) phase noise model and considering higher order harmonics generated by the oscillator output signal, a more general formula for transformation of the excess phase to the output signal is presented. Despite the basic LTV model which assumes that the total carrier power is within the fundamental harmonic, in the proposed model, the total carrier power is assumed to be distributed among all output harmonics. For the first harmonic, the developed expressions reduce to the basic LTV formulas. Simulation and experimental results are used to ensure the validity of the model.

In this paper, we propose a novel incremental method for discovering latent variables from multivariate data with high efficiency. It integrates non-Gaussianity and an adaptive incremental model in an unsupervised way to extract informative features. Our proposed method discovers a small number of compact features from a very large number of features and can still achieve good predictive performance in EEG signals. The promising EEG signal classification results from our experiments prove that this approach can successfully extract important features. Our proposed method also has low memory requirements and computational costs.

Many learning machines such as normal mixtures and layered neural networks are not regular but singular statistical models, because the map from a parameter to a probability distribution is not one-to-one. The conventional statistical asymptotic theory can not be applied to such learning machines because the likelihood function can not be approximated by any normal distribution. Recently, new statistical theory has been established based on algebraic geometry and it was clarified that the generalization and training errors are determined by two birational invariants, the real log canonical threshold and the singular fluctuation. However, their concrete values are left unknown. In the present paper, we propose a new concept, a quasi-regular case in statistical learning theory. A quasi-regular case is not a regular case but a singular case, however, it has the same property as a regular case. In fact, we prove that, in a quasi-regular case, two birational invariants are equal to each other, resulting that the symmetry of the generalization and training errors holds. Moreover, the concrete values of two birational invariants are explicitly obtained, hence the quasi-regular case is useful to study statistical learning theory.

This study examines the robustness of image quality factors in various types of environment illumination using a parameter design in the field of quality engineering. Experimental results revealed that image quality factors are influenced by environment illuminations in the following order: minimum luminance, maximum luminance and gamma.

Source coding with a helper is one of the most fundamental fixed-length source coding problem for correlated sources. For this source coding, Wyner and Ahlswede-Korner showed the achievable rate region which is the set of rate pairs of encoders such that the probability of error can be made arbitrarily small for sufficiently large block length. However, their expression of the achievable rate region consists of the sum of indefinitely many sets. Thus, their expression is not useful for computing the achievable rate region. This paper deals with correlated sources whose conditional distribution is related by a binary-input output-symmetric channel, and gives a parametric form of the achievable rate region in order to compute the region easily.

A parameterization of perfect sequences over composition algebras over the real number field is presented. According to the proposed parameterization theorem, a perfect sequence can be represented as a sum of trigonometric functions and points on a unit sphere of the algebra. Because of the non-commutativity of the multiplication, there are two definitions of perfect sequences, but the equivalence of the definitions is easily shown using the theorem. A composition sequence of sequences is introduced. Despite the non-associativity, the proposed theorem reveals that the composition sequence from perfect sequences is perfect.