This position paper outlines the author's view on architectural directions and key technology enablers for the future mobile Internet. It is pointed out that mobile and wireless services will dominate Internet usage in the near future, and it is therefore important to design next-generation network protocols with features suitable for efficiently serving emerging wireless scenarios and applications. Several key requirements for mobile/wireless scenarios are identified - these include new capabilities such as dynamic spectrum coordination, cross-layer support, disconnection tolerant routing, content addressing, and location awareness. Specific examples of enabling technologies which address some of these requirements are given from ongoing research projects at WINLAB. Topics covered briefly include wireless network virtualization, the cache-and-forward (CNF) protocol, geographic (GEO) protocol stack, cognitive radio protocols, and open networking testbeds.

The National Institute of Information and Communications Technology (NICT) vision and five network targets of research and development (R&D) of the NeW-Generation Network (NWGN) are presented in this letter. The NWGN is based on new design concepts that look beyond the next generation network (NGN). The NWGN will maintain the sustainability of our prosperous civilization and help resolve various social issues and problems by using information and communication technologies (ICTs). NICT's vision for NWGN is also presented in this letter. Based on this vision, 19 items concerning social issues and future social outlook are analyzed, and the functional requirements of the NWGN are extracted. The requirements are refined and categorized into five network targets that must be developed for realizing the vision.

In this letter we propose a novel resource allocation and admission control strategy for OFDMA-based emerging LTE systems. Considering users' reneging and migration between service providers, we first prove that the optimal resource allocation problem, which maximizes the service provider's gross income is, NP-complete. Subsequently, we propose two different heuristics based on dynamic programming and greedy algorithms to get a near-optimal resource allocation and admission control strategy in computationally feasible time. Simulation results point out that the solutions offer increased gross income of the service provider, while offering low latency, adequate throughput and session acceptance.

Charge redistribution based successive approximation (SA) analog-to-digital converter (ADC) has the advantage of power efficiency. Split capacitor digital-to-analog converter (CDAC) technique implements two sets of binary-weighted capacitor arrays connected by a bridge capacitor so as to reduce both input load capacitance and area. However, capacitor mismatches degrade ADC performance in terms of DNL and INL. In this work, a split CDAC mismatch calibration method is proposed. A bridge capacitor larger than conventional design is implemented so that a tunable capacitor can be added in parallel with the lower-weight capacitor array to compensate for mismatches. To guarantee correct CDAC calibration, comparator offset is cancelled using a digital timing control charge compensation technique. To further reduce the input load capacitance, an extra unit capacitor is added to the higher-weight capacitor array. Instead of the lower-weight capacitor array, the extra unit capacitor and the higher-weight capacitor array sample analog input signal. An 8-bit SA ADC with 4-bit + 4-bit split CDAC has been implemented in a 65 nm CMOS process. The ADC has an input capacitance of 180 fF and occupies an active area of 0.03 mm2. Measured results of +0.2/-0.3LSB DNL and +0.3/-0.3LSB INL have been achieved after calibration.

A proposed pseudo fractional-N clock generator with 50% duty cycle output is presented by using the pseudo fractional-N controller for SoC chips and the dynamic frequency scaling applications. The different clock frequencies can be generated with the particular phase combinations of a four-stage voltage-controlled oscillator (VCO). It has been fabricated in a 0.13 µm CMOS technology, and work with a supply voltage of 1.2 V. According to measured results, the frequency range of the proposed pseudo fractional-N clock generator is from 71.4 MHz to 1 GHz and the peak-to-peak jitter is less than 5% of the output period. Duty cycle error rates of the output clock frequencies are from 0.8% to 2% and the measured power dissipation of the pseudo fractional-N controller is 146 µW at 304 MHz.

This paper considers the use of an antenna selection mechanism to reduce the cost of multiple analog transmit/receive chains in multiple-input multiple-output (MIMO) systems. With the optimal antenna selection scheme, radio-frequency chains can optimally connect with the best subset of transmitter and/or receiver antennas. However, the optimal antenna selection algorithm requires an exhaustive search of all possible combinations to find the optimum subset at the transmitter and/or receiver, thus resulting in high complexity. In order to reduce the computational load while still maximizing channel capacity, we introduce the simulated annealing (SA) method, an effective algorithm that solves various combinatorial optimization problems, to search the optimal subset. The simulation results show that the performance of the proposed SA method provides almost the same channel capacity as that of the optimal exhaustive search algorithm while maintaining low complexity.

Many learning machines that have hierarchical structure or hidden variables are now being used in information science, artificial intelligence, and bioinformatics. However, several learning machines used in such fields are not regular but singular statistical models, hence their generalization performance is still left unknown. To overcome these problems, in the previous papers, we proved new equations in statistical learning, by which we can estimate the Bayes generalization loss from the Bayes training loss and the functional variance, on the condition that the true distribution is a singularity contained in a learning machine. In this paper, we prove that the same equations hold even if a true distribution is not contained in a parametric model. Also we prove that, the proposed equations in a regular case are asymptotically equivalent to the Takeuchi information criterion. Therefore, the proposed equations are always applicable without any condition on the unknown true distribution.

Performance of band-limited baseband synchronous CDMA using orthogonal Independent Component Analysis (ICA) spreading sequences is investigated. The orthogonal ICA sequences have an orthogonality condition in a synchronous CDMA like the Walsh-Hadamard sequences. Furthermore, these have useful correlation properties like the Gold sequences. These sequences are obtained easily by using the ICA which is one of the brain-style signal processing algorithms. In this study, the ICA is used not as a separator for received signal but as a generator of spreading sequences. The performance of the band-limited synchronous CDMA using the orthogonal ICA sequences is compared with the one using the Walsh-Hadamard sequences. For limiting bandwidth, a Root Raised Cosine filter (RRC) is used. We investigate means and variances of correlation outputs after passing the RRC filter and the Bit Error Rates (BERs) of the system in additive white Gaussian noise channel by numerical simulations. It is found that the BER in the band-limited system using the orthogonal ICA sequences is much lower than the one using the Walsh-Hadamard sequences statistically.

Network virtualization has become a common research topic that many researchers consider a basis for defining a new generation network architectures. In this paper, we attempt to clarify the concept of network virtualization with its brief history, to introduce the benefit of network virtualization for the future network, to posit our strong belief in that the future network should adopt a form of a meta-architecture that accommodates multiple competing multiple architectures, and to identify challenges to achieving this architecture.

Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation that is suitable for multi-dimensional continuous variables. The basic idea of the proposed method is to express the conditional density in terms of the density ratio and the ratio is directly estimated without going through density estimation. Experiments using benchmark and robot transition datasets illustrate the usefulness of the proposed approach.

This paper proposes a novel system called the cyclic prefix reconstructable time domain synchronous orthogonal frequency division multiplexing ( CPR-TDS-OFDM ) system, which uses a new frame structure and restores the cyclicity of the received OFDM block with low complexity. Simulation results show that the CPR-TDS-OFDM system outperforms the conventional TDS-OFDM system in high-speed fading channels.

In this paper, we describe several approaches to address the challenges of the network of the future. Our main hypothesis is that the Future Internet must be designed for the environment of applications and transport media of the 21st century, vastly different from the initial Internet's life space. One major requirement is the inherent support for mobile and wireless usage. A Future Internet should allow for the fast creation of diverse network designs and paradigms and must also support their co-existence at run-time. We detail the technical and business scenarios that lead the development in the EU FP7 4WARD project towards a framework for the Future Internet.

Many applications of wireless sensor networks (WSNs) require secure group communications. The WSNs are normally operated in unattended, harsh, or hostile environment. The adversaries may easily compromise some sensor nodes and abuse their shared keys to inject false sensing reports or modify the reports sent by other nodes. Once a malicious node is detected, the group key should be renewed immediately for the network security. Some strategies have been proposed to develop the group rekeying protocol, but most of existing schemes are not suitable for sensor networks due to their high overhead and poor scalability. In this paper, we propose a new group rekeying protocol for hierarchical WSNs with renewable network devices. Compared with existing schemes, our rekeying method possesses the following features that are particularly beneficial to the resource-constrained large-scale WSNs: (1) robustness to the node capture attack, (2) reactive rekeying capability to malicious nodes, and (3) low communication and storage overhead.

Robot path-planning is one of the important issues in robotic navigation. This paper presents a novel robot path-planning approach based on the associative memory using Self-Organizing Incremental Neural Networks (SOINN). By the proposed method, an environment is first autonomously divided into a set of path-fragments by junctions. Each fragment is represented by a sequence of preliminarily generated common patterns (CPs). In an online manner, a robot regards the current path as the associative path-fragments, each connected by junctions. The reasoning technique is additionally proposed for decision making at each junction to speed up the exploration time. Distinct from other methods, our method does not ignore the important information about the regions between junctions (path-fragments). The resultant number of path-fragments is also less than other method. Evaluation is done via Webots physical 3D-simulated and real robot experiments, where only distance sensors are available. Results show that our method can represent the environment effectively; it enables the robot to solve the goal-oriented navigation problem in only one episode, which is actually less than that necessary for most of the Reinforcement Learning (RL) based methods. The running time is proved finite and scales well with the environment. The resultant number of path-fragments matches well to the environment.

Introducing adaptive online data compression at network-internal nodes is considered for alleviating traffic congestion on the network. In this paper, we assume that advanced relay nodes, which possess both a relay function (network resource) and a processing function (computational and storage resources), are placed inside the network, and we propose an adaptive online lossless packet compression scheme utilized at these nodes. This scheme selectively compresses a packet according to its waiting time in the queue during congestion. Through preliminary investigation using actual traffic datasets, we investigate the compression ratio and processing time of packet-by-packet compression in actual network environments. Then, by means of computer simulations, we show that the proposed scheme reduces the packet delay time and discard rate and investigate factors necessary in achieving efficient packet relay.

This letter presents the design and analysis of phase noise optimization of a 4-GHz differential Colpitts voltage-controlled-oscillator (VCO). A low phase noise is achieved by a Colpitts oscillator and a VCO bias optimization using an amplitude control method. The measured phase noise is -134.8 dBc/Hz at 1.25 MHz offset frequency from 4 GHz operating frequency. The VCO is implemented using 0.24 µm SiGe BiCMOS process with integrated copper inductors. The wide VCO frequency range covers both PCS and IMT bands and draws about 15.9 mA from a 2.7 V power supply.

In this paper we propose a file replication scheme inspired by a thermal diffusion phenomenon for storage load balancing in unstructured peer-to-peer (P2P) file sharing networks. The proposed scheme is designed such that the storage utilization ratios of peers will be uniform, in the same way that the temperature in a field becomes uniform in a thermal diffusion phenomenon. The proposed scheme creates replicas of files in peers probabilistically, where the probability is controlled by using parameters that can be used to find the trade-off between storage load balancing and search performance in unstructured P2P file sharing networks. First, we show through theoretical analysis that the statistical behavior of the storage load balancing controlled by the proposed scheme has an analogy with the thermal diffusion phenomenon. We then show through simulation that the proposed scheme not only has superior performance with respect to balancing the storage load among peers (the primary objective of the present proposal) but also allows the performance trade-off to be widely found. Finally, we qualitatively discuss a guideline for setting the parameter values in order to widely find the performance trade-off from the simulation results.

Recently, the marginalized particle filter (MPF) has been applied to blind symbol detection problems over selective fading channels. The MPF can ease the computational burden of the standard particle filter (PF) while offering better estimates compared with the standard PF. In this paper, we investigate the application of the blind MPF detector to more realistic situations where the systems suffer from analog imperfections which are non-linear signal distortion due to the inaccurate analog circuits in wireless devices. By reformulating the system model using the widely linear representation and employing the auxiliary variable resampling (AVR) technique for estimation of the imperfections, the blind MPF detector is successfully modified to cope with the analog imperfections. The effectiveness of the proposed MPF detector is demonstrated via computer simulations.

Traditional wavelet-based speech enhancement algorithms are ineffective in the presence of highly non-stationary noise because of the difficulties in the accurate estimation of the local noise spectrum. In this paper, a simple method of noise estimation employing the use of a voice activity detector is proposed. We can improve the output of a wavelet-based speech enhancement algorithm in the presence of random noise bursts according to the results of VAD decision. The noisy speech is first preprocessed using bark-scale wavelet packet decomposition ( BSWPD ) to convert a noisy signal into wavelet coefficients (WCs). It is found that the VAD using bark-scale spectral entropy, called as BS-Entropy, parameter is superior to other energy-based approach especially in variable noise-level. The wavelet coefficient threshold (WCT) of each subband is then temporally adjusted according to the result of VAD approach. In a speech-dominated frame, the speech is categorized into either a voiced frame or an unvoiced frame. A voiced frame possesses a strong tone-like spectrum in lower subbands, so that the WCs of lower-band must be reserved. On the contrary, the WCT tends to increase in lower-band if the speech is categorized as unvoiced. In a noise-dominated frame, the background noise can be almost completely removed by increasing the WCT. The objective and subjective experimental results are then used to evaluate the proposed system. The experiments show that this algorithm is valid on various noise conditions, especially for color noise and non-stationary noise conditions.

Tree structured data often appear in bioinformatics. For example, glycans, RNA secondary structures and phylogenetic trees usually have tree structures. Comparison of trees is one of fundamental tasks in analysis of these data. Various distance measures have been proposed and utilized for comparison of trees, among which extensive studies have been done on tree edit distance. In this paper, we review key results and our recent results on the tree edit distance problem and related problems. In particular, we review polynomial time exact algorithms and more efficient approximation algorithms for the edit distance problem for ordered trees, and approximation algorithms for the largest common sub-tree problem for unordered trees. We also review applications of tree edit distance and its variants to bioinformatics with focusing on comparison of glycan structures.