This work studies the benefits of heterogeneous cellular networks with overlapping picocells in a large macrocell. We consider three different strategies for resource allocation and cell association. The first model employs a spectrum overlapping strategy with an SINR-based cell association. The second model avoids the interference between macrocell and picocell through a spectrum splitting strategy. Furthermore, picocell range expansion is also considered in this strategy to enable a load balancing between the macrocell and picocells. The last model is a hybrid one, called as fractional spectrum splitting strategy, where spectrum splitting strategy is only applied at the picocell-edge, while the picocell-inner reuses the spectrum of the macrocell. We constructs resource allocation optimization problem for these strategies to maximize the system rate. Our results show that in terms of system rate, all the three strategies outperform the performance of macrocell-only case, which shows the benefit of heterogeneous networks. Moreover, fractional spectrum splitting strategy provides highest system rate at the expense of outage user rate degradation due to inter-macro-pico interference. Spectrum overlapping model provides the second highest system rate gain and also improves outage user rate owing to full spectrum reuse and the benefit of macro diversity, while spectrum splitting model achieves a moderate system rate gain.

In Long-Term Evolution (LTE)-Advanced, heterogeneous networks where femtocells and picocells are overlaid onto macrocells are extensively discussed in addition to traditional well-planned macrocell deployment to improve further the system throughput. In heterogeneous network deployment, combined usage of inter-cell interference coordination (ICIC) and cell range expansion (CRE) is very effective in improving the system and cell-edge throughput. In this combined usage, the fraction of the sets of user equipment (UEs) connected to the picocells, which are controlled through CRE, and that connected to macrocells affect the gain from the ICIC. Therefore, this paper evaluates the throughput performance of different offset values for CRE and different amounts of protected resources for ICIC in picocell deployments in LTE-Advanced downlink. Simulation results (2–10 picocells and 30 UEs are located within 1 macrocell) assuming a full buffer traffic model show that when the CRE offset value is set between 8 to 20 dB, almost the same user throughput performance is obtained by allocating the appropriate resources to protect UEs that connect to the picocells. Furthermore, the appropriate resource ratio is derived based on the fraction of UEs connected to the picocells through CRE, the fraction of UEs connected to the macrocell, and the number of picocells under the simulation conditions.

When a cognitive radio system dynamically utilizes a frequency band, channel control information must be communicated over the network in order for the currently available carrier frequencies to be shared. In order to keep efficient spectrum utilization, this control information should also be dynamically transmitted through channels such as cognitive pilot channels based on the channel conditions. If transmitters dynamically select carrier frequencies, receivers must receive the control signal without knowledge of its carrier frequencies. A novel scheme called differential code parallel transmission (DCPT) enables receivers to receive low-rate information without any knowledge of the carrier frequency. The transmitter simultaneously transmits two signals whose carrier frequencies are separated by a predefined value. The absolute values of the carrier frequencies can be varied. When the receiver receives the DCPT signal, it multiplies the signal by a frequency-shifted version of itself; this yields a DC component that represents the data signal, which is then demodulated. However, the multiplication process results in the noise power being squared, necessitating high received signal power. In this paper, to realize a bandpass filter that passes only DCPT signals of unknown frequency and that suppresses noise and interference at other frequencies, a DCPT-adaptive bandpass filter (ABF) that employs an adaptive equalizer is proposed. In the training phase, the received signal is the filter input and the frequency-shifted signal is the training input. Then, the filter is trained to pass the higher-frequency signal of the two DCPT signals. The performance of DCPT-ABF is evaluated through computer simulations. We find that DCPT-ABF operates successfully even under strong interference.

An adaptive rate controller (ARC) based on an adaptive neural fuzzy inference system (ANFIS) is designed to autonomously adjust the data rate of a mobile heterogeneous network to adapt to the changing traffic load and the user speed for multimedia call services. The effect of user speed on the handoff rate is considered. Through simulations, it has been demonstrated that the ANFIS-ARC is able to maintain new call blocking probability and handoff failure probability of the mobile heterogeneous network below a prescribed low level over different user speeds and new call origination rates while optimizing the average throughput. It has also been shown that the mobile cognitive wireless network with the proposed CS-ANFIS-ARC protocol can support more traffic load than neural fuzzy call-admission and rate controller (NFCRC) protocol.

In this paper, we present a coexistence protocol design for a coexistence information service to provide coexistence solutions among dissimilar or independently operated autonomous decision-making networks in a wireless communication environment over, specifically but not limited to, TV white space (TVWS) frequency bands. The designed coexistence protocol for the coexistence information service has three main functionalities: (1) To collect basic information of subscribed TVWS networks; (2) To support generating neighbor lists for the TVWS networks based on the geography information and/or propagation parameters; (3) To provide necessary information for TVWS networks to make coexistence decisions. Both theoretical analysis and simulation results show that the designed coexistence information service ensures harmonious communications among dissimilar networks and is able to achieve coexistence over an area with the limited number of available channels in white space.

Program comprehension using dynamic information is one of key tasks of software maintenance. Software visualization with sequence diagrams is a promising technique to help developer comprehend the behavior of object-oriented systems effectively. There are many tools that can support automatic generation of a sequence diagram from execution traces. However it is still difficult to understand the behavior because the size of automatically generated sequence diagrams from the massive amounts of execution traces tends to be beyond developer's capacity. In this paper, we propose an execution trace slicing and visualization method. Our proposed method is capable of slice calculation based on a behavior model which can treat dependencies based on static and dynamic analysis and supports for various programs including exceptions and multi-threading. We also introduce our tool that perform our proposed slice calculation on the Eclipse platform. We show the applicability of our proposed method by applying the tool to two Java programs as case studies. As a result, we confirm effectiveness of our proposed method for understanding the behavior of object-oriented systems.

Dynamic Framed Slotted ALOHA (DFSA) is one of the most popular protocols to resolve tag collisions in RFID systems. In DFSA, it is widely known that the optimal performance is achieved when the frame size is equal to the number of tags. So, a reader dynamically adjusts the next frame size according to the current number of tags. Thus it is important to estimate the number of tags exactly. In this paper, we propose a novel tag estimation and identification method using litmus (test) slots for DFSA. We compare the performance of the proposed method with those of existing methods by analysis. We conduct simulations and show that our scheme improves the speed of tag identification.

Reliable detection of the licensed user signal is a pre-requirement for avoiding interference to the licensed user in a CR network. Cooperative spectrum sensing (CSS) is able to offer improved sensing performance compared to individual sensing. In this paper, we propose a robust soft combination rule based on the Kullback-Leibler divergence (KL-divergence) for CSS. The proposed scheme is able to obtain a similar sensing performance compared to that of maximum gain combination (MGC) without requiring signal to noise ratio (SNR) information. In addition, the proposed scheme protects the sensing process against common types of malicious users without a malicious user identification step. The simulation results demonstrate the effectiveness of the proposed scheme.

This paper presents a novel method for improving the readability of automatic speech recognition (ASR) results for classroom lectures. Because speech in a classroom is spontaneous and contains many ill-formed utterances with various disfluencies, the ASR result should be edited to improve the readability before presenting it to users, by applying some operations such as removing disfluencies, determining sentence boundaries, inserting punctuation marks and repairing dropped words. Owing to the presence of many kinds of domain-dependent words and casual styles, even state-of-the-art recognizers can only achieve a 30-50% word error rate for speech in classroom lectures. Therefore, a method for improving the readability of ASR results is needed to make it robust to recognition errors. We can use multiple hypotheses instead of the single-best hypothesis as a method to achieve a robust response to recognition errors. However, if the multiple hypotheses are represented by a lattice (or a confusion network), it is difficult to utilize sentence-level knowledge, such as chunking and dependency parsing, which are imperative for determining the discourse structure and therefore imperative for improving readability. In this paper, we propose a novel algorithm that infers clean, readable transcripts from spontaneous multiple hypotheses represented by a confusion network while integrating sentence-level knowledge. Automatic and manual evaluations showed that using multiple hypotheses and sentence-level knowledge is effective to improve the readability of ASR results, while preserving the understandability.

This paper proposes an efficient list extension algorithm for soft-output multiple-input-multiple-output (soft-MIMO) detection. This algorithm extends the list of candidate vectors based on the vector selected by initial detection, in order to solve the empty-set problem, while reducing the number of additional vectors. The additional vectors are obtained from multiple detection orders, from which high-quality soft-output can be generated. Furthermore, a method to reduce the complexity of the determination of the multiple detection orders is described. From simulation results for a 44 system with 16- and 64-quadrature amplitude modulations (QAM) and rate 1/2 and 5/6 duo-binary convolutional turbo code (CTC), the soft-MIMO detection to which the proposed list extension was applied showed a performance degradation of less than 0.5 dB at bit error rate (BER) of 10-5, compared to that of the soft-output maximum-likelihood detection (soft-MLD) for all code rate and modulation pairs, while the complexity of the proposed list extension was approximately 38% and 17% of that of an existing algorithm with similar performance in a 44 system using 16- and 64-QAM, respectively.

Optical code-division multiple-access (OCDMA) is a promising technique for multimedia transmission in fiber-optic local-area networks (LANs). Variable-weight optical orthogonal codes (OOCs) can be used for OCDMA networks supporting multiple quality of services (QoS). Most constructions for optimal variable-weight OOCs have focused on the case where the number of distinct Hamming weights of all codewords is equal to two, and the codewords of weight 3 are normally included. In this letter, four explicit constructions of optimal (υ,{4,5,6},1,Q)-OOCs are presented, and more new optimal (υ,{4,5,6},1,Q)-OOCs are obtained via recursive constructions. These improve the existing results on optimal variable-weight OOCs with at least three distinct Hamming weights and minimum Hamming weight 4.

In the present paper, we address the problem of extrapolating group proximities from member relations, which we refer to as the group proximity problem. We assume that a relational dataset consists of several groups and that pairwise relations of all members can be measured. Under these assumptions, the goal is to estimate group proximities from pairwise relations. In order to solve the group proximity problem, we present a method based on embedding and distribution mapping, in which all relational data, which consist of pairwise dissimilarities or dissimilarities between members, are transformed into vectorial data by embedding methods. After this process, the distributions of the groups are obtained. Group proximities are estimated as distances between distributions by distribution mapping methods, which generate a map of distributions. As an example, we apply the proposed method to document and bacterial flora datasets. Finally, we confirm the feasibility of using the proposed method to solve the group proximity problem.

We have proposed a novel call admission control (CAC) for maximizing total user satisfaction in a heterogeneous traffic network and showed the effectiveness of our CAC by using an optimal threshold from numerical analysis [1]. In our previous CAC, when a new broadband flow arrives and the total accommodated bandwidth is more than or equal to the threshold, the arriving new broadband flow is rejected. In actual networks, however, users may agree to wait for a certain period until the broadband flow, such as video, begins to play. In this paper, when total accommodated bandwidth is more than or equal to the threshold, arriving broadband flows wait instead of being rejected. As a result, we can greatly improve total user satisfaction.

In this letter, we propose and analyze a cooperative transmission scheme (CTS) that uses transmission timing control for LTE enterprise femtocells. In our scheme, the user equipment (UE) can receive the desired signal from an adjacent fBS as well as its serving femtocell BS (fBS). Thus, UE achieves an improved signal to interference ratio (SIR) due to the synchronization of the two signals. Analysis and simulation results show that the proposed scheme can reduce the outage probability for enterprise femtocells compared to the conventional system. In particular, a significant performance improvement can be achieved for UEs located at cell edges.

To develop the advanced and rich life, and the also economy and social activity continuously, various types of energy are necessary. At the same time, to protect the global environment and to prevent the depletion of natural resources, the effective and moreover efficient use of energy is becoming important. Electric power is one of the most important forms of energy for our life and society. This paper describes topics and survey results of technical trends regarding the electric power supply systems which are playing a core role as the important infrastructure to support the emergence of information-oriented society. Specifically, the power supply systems that enhance high power quality and reliability (PQR) are important for the steady growth of information and communication services. The direct current (DC) power, which has been used for telecommunications power systems and information and communications technologies (ICT), enables existing utilities' grid and distributed energy resources to keep a balance between supply and demand of small-scaled power systems or microgirds. These techniques are expected to be part of smartgrid technologies and facilitate the installation of distributed generators in mission critical facilities.

Ultra wideband (UWB) radar is considered a promising technology to complement existing camera-based surveillance systems because, unlike cameras, it provides excellent range resolution. Many of the UWB radar imaging algorithms are based on large-scale antenna arrays that are not necessarily practical because of their complexity and high cost. To resolve this issue, we previously developed a two-dimensional radar imaging algorithm that estimates unknown target shapes and motion using only three antennas. In this paper, we extend this method to obtain three-dimensional images by estimating three-dimensional motions from the outputs of five antennas. Numerical simulations confirm that the proposed method can estimate accurately the target shape under various conditions.

Joint transmission (JT) in time-division-duplex code-division multiple-access (TDD-CDMA) systems can provide a low peak-to-average power ratio (PAPR) for single paths, but causing a high PAPR in multipath environments. To avoid the high PAPR, we propose a new approach to JT technique by selecting certain paths instead of all paths used in JT processing so that PAPR becomes lower. The path selection proposal involves two methods; path selection by taking certain paths from all paths and by taking paths having path gains above a certain threshold value. To enhance the effectiveness of the proposed techniques, we evaluate a combination of the proposed technique with the clipping technique. We evaluate both PAPR and bit error rate (BER) performance for the proposed techniques and its combination with the clipping technique. We compare the results of the proposed techniques with conventional JT technique and the combination techniques with clipping technique. From the results of computer simulation, we show that the proposed path selection techniques perform low PAPR and good BER performance compared to the conventional JT processing. We also show that the combination of proposed path selection technique and clipping performs low PAPR performance without severe BER degradation compared to the conventional clipping technique.

We propose a new method, called Subclass-oriented Dimensionality Reduction with Pairwise Constraints (SODRPaC), for dimensionality reduction. In a high dimensional space, it is common that a group of data points with one class may scatter in several different groups. Current linear semi-supervised dimensionality reduction methods would fail to achieve fair performances, as they assume two data points linked by a must-link constraint are close each other, while they are likely to be located in different groups. Inspired by the above observation, we classify the must-link constraint into two categories, which are the inter-subclass must-link constraint and the intra-subclass must-link constraint, respectively. We carefully generate cannot-link constraints by using must-link constraints, and then propose a new discriminant criterion by employing the cannot-link constraints and the compactness of shared nearest neighbors. The manifold regularization is also incorporated in our dimensionality reduction framework. Extensive experiments on both synthetic and practical data sets illustrate the effectiveness of our method.

In this paper, a novel microstrip hairpin-line bandpass filter which employs a modified Minkowski fractal shape is proposed. Although conventional hairpin-line filters are popular for RF front ends, they suffer from undesired spurious responses located at the second harmonic, which causes asymmetry in the upper skirt band. By proper design, the second harmonic of fractal filters can be significantly suppressed through the use of fractal shape. To validate this novel geometry, the proposed filters are fabricated and measured. Simulated results are in good agreement with measured results.

This paper shows a design method for a regular expression matching circuit based on a decomposed automaton. To implement a regular expression matching circuit, first, we convert a regular expression into a non-deterministic finite automaton (NFA). Then, to reduce the number of states, we convert the NFA into a merged-states non-deterministic finite automaton with unbounded string transition (MNFAU) using a greedy algorithm. Next, to realize it by a feasible amount of hardware, we decompose the MNFAU into a deterministic finite automaton (DFA) and an NFA. The DFA part is implemented by an off-chip memory and a simple sequencer, while the NFA part is implemented by a cascade of logic cells. Also, in this paper, we show that the MNFAU based implementation has lower area complexity than the DFA and the NFA based ones. Experiments using regular expressions form SNORT shows that, as for the embedded memory size per a character, the MNFAU is 17.17-148.70 times smaller than DFA methods. Also, as for the number of LCs (Logic Cells) per a character, the MNFAU is 1.56-5.12 times smaller than NFA methods. This paper describes detail of the MEMOCODE2010 HW/SW co-design contest for which we won the first place award.