The multiple-access channel (MAC) becomes very popular in various communication systems, because multi-terminal communication systems have been widely used in practical systems, e.g., mobile phones and P2P, etc. For some MACs, it is known that feedback can enlarge the capacity region, where the capacity region is the set of rate pairs such that the error probability can be made arbitrarily small for sufficiently large block length. The capacity region for general MACs, which are not required to satisfy ergodicity and stationarity with perfect feedback was first shown by Tatikonda and Mitter without the proof, where perfect feedback means that the channel output is perfectly fed back to senders. In this paper, we generalize Tatikonda and Mitter's result to the case of deterministic feedback, where the values of deterministic functions of past channel outputs is fed back to senders. We show that the capacity region for general MACs with deterministic feedback can be represented by the information-spectrum formula introduced by Han and Verdu, and directed information introduced by Massey. We also investigate the compound MAC problem, the ε-coding problem, the strong converse property and the cost constraint problem for general MACs with deterministic feedback.

IEEE 802.11 Wireless LANs (WLANs) support multiple transmission rates. When some stations transmit at low transmission rates, the performance of the high transmission rate stations degrades heavily, and this phenomenon is known as the performance anomaly. As a solution to the performance anomaly, airtime fairness was proposed. However, the distributed coordination function (DCF) of IEEE 802.11 cannot provide airtime fairness to all competing stations because the protocol is designed to ensure fair attempt probability. In this paper, we propose a new medium access control, successful transmission time fair MAC (STF-MAC), which is fair in terms of successful transmission time and also provides the maximum aggregate throughput of a basic service set (BSS) in distributed manner. STF-MAC can be easily applied to solve the uplink/downlink fairness problem in infrastructure mode. Through simulations, we demonstrate that STF-MAC not only remedies the performance anomaly but also maximizes the aggregate throughput under the fairness constraint.

IEEE 802.15.3c has been standardized for wireless personal area networks (WPANs) to realize high-speed wireless communications with 1 Gbps throughput. In this paper we introduce a 802.15.3c WPAN prototype. The introduced 802.15.3c WPAN prototype applies the enhanced MAC functions of data separation on hybrid multiple access, long frame size, aggregation, block acknowledgment, and timing operation, which can realize Gbps throughput in IEEE 802.15.3c. Moreover, the experiment performance studies on the prototype show that around 1.6 Gbps throughput can be successfully achieved and video streaming applications can be accommodated. Also, our studies provide the useful information of MAC capacity for developing the 802.15.3c devices.

In this paper, we evaluate the downlink performance of Transparent mode (T-mode) and Non-Transparent mode (NT-mode) in a two-hop cellular system based on IEEE 802.16j. In particular, we evaluate the performance in terms of the system capacity, optimal resource allocation, and outage probability using Monte Carlo simulation with various system parameters such as different Frequency Reuse Factors (FRFs) and the distance between Base Station (BS) and Relay Station (RS). To analyze the Signal to Interference and Noise Ratio (SINR) of the access and relay links, an SINR model is introduced for cellular multihop systems considering intra- and inter-cell interferences. Then, we present a method of optimal resource allocation for the Access Zone (AZ) and Relay Zone (RZ) to maximize the system capacity. Consequently, the simulation results provide an insight into choosing the appropriate RS position and optimal resource allocation. Through numerical examples, it is found that the FRFs of two and three are good choices to achieve the highest capacity with low outage in T- and NT-modes, respectively.

In this paper, we propose an energy efficient MAC protocol for wireless sensor networks. In sensor networks, reducing energy consumption is one of the critical issues for extending network lifetime. One good solution to resolve this issue is introducing listen-sleep cycles, allowing sensor nodes to turn their transceiver off during sleep periods, which was adopted by S-MAC [1]. However, in S-MAC, due to the synchronized scheduling, transmission collisions will increase in heavy traffic situations, resulting in energy waste and low throughput. Hence, in this paper, we propose probabilistic scheduled MAC (PS-MAC), in which each node determines ‘listen’ or ‘sleep’ pseudo-randomly based on its own pre-wakeup probability and pre-wakeup probabilities of its neighbor nodes in each time slot. This allows the listen-sleep schedule of nodes in each transmitter and receiver pair to be synchronized, while maintaining those of the rest of nodes to be asynchronous. Therefore, collisions can be reduced even under heavy traffic conditions, resulting in reduced energy waste and high throughput. In addition, by dynamically adjusting the pre-wakeup probabilities of sensor nodes based on the change of the network environment, system throughput and latency can be further improved. Simulation results show that PS-MAC provides significant energy savings, low delay, and high network throughput.

In 2010, Guo and Zhang proposed a group key agreement protocol based on the chaotic hash function. This letter points out that Guo-Zhang's protocol is still vulnerable to off-line password guessing attacks, stolen-verifier attacks and reflection attacks.

This letter proposes a method for choosing the best quantized beamforming vector that represents a subcarrier group, for coordinated beamforming in the downlink of multiuser multiple input multiple output-orthogonal frequency division multiplexing systems. The correlation between subcarriers is exploited for reducing the feedforward overhead, while maximizing the sum rate.

Kernel biased discriminant analysis (KBDA), as a subspace learning algorithm, has been an attractive approach for the relevance feedback in content-based image retrieval. Its performance, however, still suffers from the “small sample learning” problem and “kernel learning” problem. Aiming to solve these problems, in this paper, we present a new semi-supervised scheme of KBDA (S-KBDA), in which the projection learning and the “kernel learning” are interweaved into a constrained optimization framework. Specifically, S-KBDA learns a subspace that preserves both the biased discriminant structure among the labeled samples, and the geometric structure among all training samples. In kernel optimization, we directly optimize the kernel matrix, rather than a kernel function, which makes the kernel learning more flexible and appropriate for the retrieval task. To solve the constrained optimization problem, a fast algorithm based on gradient ascent is developed. The image retrieval experiments are given to show the effectiveness of the S-KBDA scheme in comparison with the original KBDA, and the other two state-of-the-art algorithms.

A circularly-polarized planar array antenna using hexagonal aperture elements is proposed. A 22-element subarray as the basic unit is excited by a corporate-feed circuit located in the lower layer of the double-layered antenna. The hexagonal aperture is designed to achieve a good axial ratio in the boresight. A 1616-element array antenna with uniform element spacing smaller than the free-space wavelength was fabricated by diffusion bonding of laminated thin metal plates for the 60 GHz-band. The high gain of 33.4 dBic is measured with 91.6% antenna efficiency, including losses.

In cellular networks, maximizing the energy efficiency (EE) while satisfying certain QoS requirements is challenging. In this article, we utilize effective capacity (EC) theory as an effective means of meeting these challenges. Based on EC and taking a realistic base station (BS) power consumption model into account, we develop a novel energy efficiency (EE) metric: effective energy efficiency (EEE), to represent the delivered service bit per energy consumption at the upper layer with QoS constraints. Maximizing the EEE problem with EC constraints is addressed and then an optimal power control scheme is proposed to solve it. After that, the EEE and EC tradeoff is discussed and the effects of diverse QoS parameters on EEE are investigated through simulations, which provides insights into the quality of service (QoS) provision, and helps the system power consumption optimization.

The measure of the goodness, or inversely the cost, of concatenating synthesis units plays an important role in concatenative speech synthesis. In this paper, we present a probabilistic approach to concatenation modeling in which the goodness of concatenation is measured by the conditional probability of observing the spectral shape of the current candidate unit given the previous unit and the current phonetic context. This conditional probability is modeled by a conditional Gaussian density whose mean vector has a form of linear transform of the past spectral shape. Decision tree-based parameter tying is performed to achieve robust training that balances between model complexity and the amount of training data available. The concatenation models are implemented for a corpus-based speech synthesizer, and the effectiveness of the proposed method was confirmed by an objective evaluation as well as a subjective listening test. We also demonstrate that the proposed method generalizes some popular conventional methods in that those methods can be derived as the special cases of the proposed method.

We propose a committee-based method of active learning for large vocabulary continuous speech recognition. Multiple recognizers are trained in this approach, and the recognition results obtained from these are used for selecting utterances. Those utterances whose recognition results differ the most among recognizers are selected and transcribed. Progressive alignment and voting entropy are used to measure the degree of disagreement among recognizers on the recognition result. Our method was evaluated by using 191-hour speech data in the Corpus of Spontaneous Japanese. It proved to be significantly better than random selection. It only required 63 h of data to achieve a word accuracy of 74%, while standard training (i.e., random selection) required 103 h of data. It also proved to be significantly better than conventional uncertainty sampling using word posterior probabilities.

Recently, video streaming services using P2P (Peer-to-Peer) have attracted attention to solve the problem of load concentration on servers and to reduce large latency. Many P2P streaming systems, like Coolstreaming, however, take a complicated approach to control playback timing severely. This leads to less churn resiliency and less adaptability to fluctuation of network traffic. Therefore, we focus on a simple and robust approach to realize “pseudo” streaming with high quality, which is based on BitTorrent. In the existing methods with the simple approach, peers download pieces just closer to playback timing to decrease the playback discontinuity. However, these methods do not consider the constitution of video structure in sophisticated manner. P2P streaming system must consider several important metrics for high-quality and fair distribution. Therefore, in this paper, we propose a new P2P video streaming system considering the cooperation of three important metrics; video structure, playback timing, and piece dispersion on network. In this system, users vary three piece selections to suit the delivery status. Specifically, users preferentially download pieces which affect the video quality, which are closer to playback timing, and which improve the delivery efficiency. Moreover, we show the effectiveness of the proposed method by computer simulation.

A MANEMO node is an IP-based mobile node that has interface attachments to both a mobile network, using Network Mobility (NEMO), and a Mobile Ad Hoc Network (MANET). While communicating with a correspondent node in the Internet, the MANEMO node should use the best possible path. Therefore, as conditions change, a handover between NEMO and MANET is desirable. This paper describes the operation of a MANEMO handover when IEEE 802.11 is used. An analytical model illustrates that packet loss during a MANEMO handover may severely affect data and real-time applications. We therefore propose using buffering during the handover, by making use of the Power Save Mode in IEEE 802.11. In the proposed algorithm, a MANEMO node may rapidly switch between the two interfaces, eventually receiving packets delivered via the old network interface while initiating the Mobile IP/NEMO handover on the new interface. Performance results show that packet loss can be significantly reduced, with small and acceptable increases in signalling overhead and end-to-end delay.

We present an improvement to the existing steganography-based bandwidth extension scheme. Enhanced WB (wideband) speech quality is achieved by embedding multiple highband spectral gains into a G.711 bitstream. The number of spectral gains is selected by optimizing the quantity of the embedding data with respect to the quality of the extended WB speech. Compared to the existing method, the proposed scheme improves the WB PESQ (Perceptual Evaluation of Speech Quality) score by 0.334 with negligible degradation of the embedded narrowband speech.

This study focused on three simplified models, namely (1) one set of single-phase DC-DC converter, (2) two sets of parallel connection single-phase DC-DC converter, and (3) two sets of series connection single-phase DC-DC converter. The purposes are: (1) to propose the simplification conditions and procedures for the three-phase AC-DC converter; (2) propose a set of new simplification steps for modeling, and present the examples of different three-phase AC-DC circuit topologies, detailed discussion on the simplification steps for modeling of a three-phase AC-DC converter is offered, to help people simplify and analyze the simplified model easily; (3) according to three types of simplified modeling in the three-phase AC-DC converter, this study established a useful reference for the design and analysis of the control systems of the three-phase AC-DC converter simply; (4) to acquire PWM control strategy beforehand based on PFC-Controlled property; (5) to reduce the switching loss for the PWM control strategy of the simplified model; (6) to maintain the original circuit topology and verify that the theory can extensively apply the knowledge of single-phase DC-DC converter to the simplified modeling of three-phase AC-DC converter.

This paper presents the design and implementation of a quadrature voltage-controlled ring oscillator with the improved figure of merit (FOM) using the four single-ended inverter topology. Furthermore, a new architecture to prevent the latch-up in even number of stages composed of single-ended ring inverters is proposed. The design is implemented in 0.18 µm CMOS technology and the measurement results show a FOM of -163.8 dBc/Hz with the phase noise of -125.8 dBc/Hz at 4 MHz offset from the carrier frequency of 3.4 GHz. It exhibits a frequency tuning range from 1.23 GHz to 4.17 GHz with coarse and fine frequency tuning sensitivity of 1.08 MHz/mV and 120 kHz/mV, respectively.

This study develops a fuzzy logic control mechanism in eigenspace-based MLLR speaker adaptation. Specifically, this mechanism can determine hidden Markov model parameters to enhance overall recognition performance despite ordinary or adverse conditions in both training and operating stages. The proposed mechanism regulates the influence of eigenspace-based MLLR adaptation given insufficient training data from a new speaker. This mechanism accounts for the amount of adaptation data available in transformation matrix parameter smoothing, and thus ensures the robustness of eigenspace-based MLLR adaptation against data scarcity. The proposed adaptive learning mechanism is computationally inexpensive. Experimental results show that eigenspace-based MLLR adaptation with fuzzy control outperforms conventional eigenspace-based MLLR, and especially when the adaptation data acquired from a new speaker is insufficient.

One of the efficient methods to build the index of continuous window queries over moving objects is by means of region quadtree index. In this paper, we present an optimal algorithm to search for the optimal position translation of query windows, where the total number of decomposed quadtree blocks for those windows in quadtree representation is minimal. We exploit the branch-and-bound concept to prune the particular paths of recursions in the search space. Evaluation proves that our optimal algorithm reduces search time greatly and the quadtree index based on optimal position translation works efficiently for continuous window queries. To the best of our knowledge, the algorithms and experiments reported in this paper are novel.

To reduce the error of channel estimation caused by noise, a novel noise suppression method based on the degree of confidence is proposed in this paper. The false alarm and false dismissal probabilities, corresponding to noise being taken as part of channel impulse response (CIR) and part of the CIR being mis-detected as noise, respectively, are also investigated. A false alarm reduction method is therefore presented to reduce the false alarms in the estimated CIR while the mis-detection ratio still remains low. Simulation results show the effectiveness of the proposed method.