Mobility load balancing (MLB) is a key technology for self-organization networks (SONs). In this paper, we explore the mobility load balancing problem and propose a unified cell specific offset adjusting algorithm (UCSOA) which more accurately adjusts the largely uneven load between neighboring cells and is easily implemented in practice with low computing complexity and signal overhead. Moreover, we evaluate the UCSOA algorithm in two different traffic conditions and prove that the UCSOA algorithm can get the lower call blocking rates and handover failure rates. Furthermore, the interdependency of the proposed UCSOA algorithm's performance and that of the inter-cell interference coordination (ICIC) algorithm is explored. A self-organization soft frequency reuse scheme is proposed. It demonstrates UCSOA algorithm and ICIC algorithm can obtain a positive effect for each other and improve the network performance in LTE system.

With the ever-growing prevalence of web 2.0, users can access information and resources easily and ubiquitously. It becomes increasingly important to understand the characteristics of user's complex behavior for efficient network management and security monitoring. In this paper, we develop a novel method to visualize and measure user's web-communication-behavior character in large-scale networks. First, we employ the active and passive monitoring methods to collect more than 20,000 IP addresses providing web services, which are divided into 12 types according to the content they provide, e.g. News, music, movie and etc, and then the IP address library is established with elements as (servicetype, IPaddress). User's behaviors are complex as they stay in multiple service types during any specific time period, we propose the behavior spectrum to model this kind of behavior characteristics in an easily understandable way. Secondly, two kinds of user's behavior characters are analyzed: the character at particular time instants and the dynamic changing characters among continuous time points. We then employ Renyi cross entropy to classify the users into different groups with the expectation that users in the same groups have similar behavior profiles. Finally, we demonstrated the application of behavior spectrum in profiling network traffic patterns and finding illegal users. The efficiency and correctness of the proposed methods are verified by the experimental results using the actual traffic traces collected from the Northwest Regional Center of China Education and Research Network (CERNET).

Cognitive radio (CR) systems aim for more efficient spectrum utilization by having so called secondary users (SUs) transmit on a frequency band reserved for licensed primary users (PUs). The secondary transmissions are allowed provided that no harmful interference will be caused to the PUs. SU terminals with multiple antennas can employ transmit power control with transmit precoding in order to control the interference levels. In most of the existing works, perfect channel state information (CSI) is assumed to be available for the SUs. However, in practical systems where perfect CSI is not available, the SUs are not able to guarantee that the interference constraints are sufficiently satisfied. In this paper, we investigate the problem of spectrum sharing for multiantenna CR systems using estimated CSI. Due to the random nature of the estimation error, we set a probabilistic interference constraint and, in order to satisfy it, provide a density function for the interference power. In addition, we present a power control framework for the SU to meet the probabilistic interference constraint.

Manifold learning based image clustering models are usually employed at local level to deal with images sampled from nonlinear manifold. Multimode patterns in image data matrices can vary from nominal to significant due to images with different expressions, pose, illumination, or occlusion variations. We show that manifold learning based image clustering models are unable to achieve well separated images at local level for image datasets with significant multimode data patterns. Because gray level image features used in these clustering models are not able to capture the local neighborhood structure effectively for multimode image datasets. In this study, we use nearest neighborhood quality (NNQ) measure based criterion to improve local neighborhood structure in terms of correct nearest neighbors of images locally. We found Gist as the optimal image descriptor among HOG, Gist, SUN, SURF, and TED image descriptors based on an overall maximum NNQ measure on 10 benchmark image datasets. We observed significant performance improvement for recently reported clustering models such as Spectral Embedded Clustering (SEC) and Nonnegative Spectral Clustering with Discriminative Regularization (NSDR) using proposed approach. Experimentally, significant overall performance improvement of 10.5% (clustering accuracy) and 9.2% (normalized mutual information) on 13 benchmark image datasets is observed for SEC and NSDR clustering models. Further, overall computational cost of SEC model is reduced to 19% and clustering performance for challenging outdoor natural image databases is significantly improved by using proposed NNQ measure based optimal image representations.

In this paper, we propose voice conversion (VC) based on articulatory features (AF) to vocal-tract parameters (VTP) mapping. An artificial neural network (ANN) is applied to map AF to VTP and to convert a speaker's voice to a target-speaker's voice. The proposed system is not only text-independent VC, in which it does not need parallel utterances between source and target-speakers, but can also be used for an arbitrary source-speaker. This means that our approach does not require source-speaker data to build the VC model. We are also focusing on a small number of target-speaker training data. For comparison, a baseline system based on Gaussian mixture model (GMM) approach is conducted. The experimental results for a small number of training data show that the converted voice of our approach is intelligible and has speaker individuality of the target-speaker.

In this paper, we study an objective quality measure that approximates the subjective mean opinion score (MOS) for bandwidth-extended wideband speech with respect to narrowband speech. Bandwidth-extended speech should be widely evaluated by a subjective quality assessment such as MOS. However, such subjective quality assessments are expensive and time-consuming. This paper proposes a new objective quality measure that combines the perceptual evaluation of speech quality (PESQ) and spectral-distortion. We evaluated the correlation between our proposed scheme and MOS using AMR and AMR-WB speech codecs. The coefficient of correlation between the proposed scheme and the MOS value was found to be 0.973. We concluded that the proposed scheme is a valid and effective objective quality measure.

The estimation of the power spectral density (PSD) of noise is crucial for retrieving speech in noisy environments. In this study, we propose a novel method for estimating the non-white noise PSD from noisy speech on the basis of a generalized gamma distribution and the minimum mean square error (MMSE) approach. Because of the highly non-stationary nature of speech, deriving its actual spectral probability density function (PDF) using conventional modeling techniques is difficult. On the other hand, spectral components of noise are more stationary than those of speech and can be represented more accurately by a generalized gamma PDF. The generalized gamma PDF can be adapted to optimally match the actual distribution of the noise spectral amplitudes observed at each frequency bin utilizing two real-time updated parameters, which are calculated in each frame based on the moment matching method. The MMSE noise PSD estimator is derived on the basis of the generalized gamma PDF and Gaussian PDF models for noise and speech spectral amplitudes, respectively. Combined with an improved Weiner filter, the proposed noise PSD estimate method exhibits the best performance compared with the minimum statistics, weighted noise estimation, and MMSE-based noise PSD estimation methods in terms of both subjective and objective measures.

The problem of resource allocation to minimize the outage probability for the secondary user (SU) groups in a cognitive radio (CR) multicast network is investigated. We propose a joint power and rate allocation scheme that provides significant improvement over the conventional scheme in terms of outage probability.

This letter proposes a noise spectrum estimation algorithm for speech enhancement. The algorithm incorporates the speech presence probability, which is calculated from SNR (signal-to-noise ratio) discrepancy. The discrepancy is measured based on the estimation of the a priori and a posteriori SNR. The proposed algorithm is found to be effective in rapidly switched noise environments. This is confirmed by the experimental results which indicate that the proposed algorithm when integrated in a speech enhancement scheme performs better than conventional noise estimation algorithms.

The evolution of wireless communication systems leads to Dynamic Spectrum Allocation for Cognitive Radio, which requires reliable spectrum sensing techniques. Among the spectrum sensing methods proposed in the literature, those that exploit cyclostationary characteristics of radio signals are particularly suitable for communication environments with low signal-to-noise ratios, or with non-stationary noise. However, such methods have high computational complexity that directly raises the power consumption of devices which often have very stringent low-power requirements. We propose a strategy for cyclostationary spectrum sensing with reduced energy consumption. This strategy is based on the principle that p processors working at slower frequencies consume less power than a single processor for the same execution time. We devise a strict relation between the energy savings and common parallel system metrics. The results of simulations show that our strategy promises very significant savings in actual devices.

This letter proposes a mobile application framework named erasable photograph tagging (EPT) for photograph annotation and fast retrieval. The smartphone owner's voice is employed as tags and hidden in the host photograph without an extra feature database aided for retrieval. These digitized tags can be erased anytime with no distortion remaining in the recovered photograph.

A method for efficiently estimating the time-varying spectra of nonstationary autoregressive (AR) signals is derived using an indefinite matrix-based sliding window fast linear prediction (ISWFLP). In the linear prediction, the indefinite matrix plays a very important role in sliding an exponentially weighted finite-length window over the prediction error samples. The resulting ISWFLP algorithm successively estimates the time-varying AR parameters of order N at a computational complexity of O(N) per sample. The performance of the AR parameter estimation is superior to the performances of the conventional techniques, including the Yule-Walker, covariance, and Burg methods. Consequently, the ISWFLP-based AR spectral estimation method is able to rapidly track variations in the frequency components with a high resolution and at a low computational cost. The effectiveness of the proposed method is demonstrated by the spectral analysis results of a sinusoidal signal and a speech signal.

This paper presents the design and proof-of-concept validation of a novel network-assisted spectrum coordination (NASCOR) service for improved radio coexistence in future shared spectrum bands. The basic idea is to create an overlay network service for dissemination of spectrum usage information between otherwise independent radio devices and systems, enabling them to implement decentralized spectrum coexistence policies that reduce interference and improve spectrum packing efficiency. The proposed method is applicable to unlicensed band and shared spectrum systems in general (including femtocells), but is particularly relevant to emerging TV white spaces and cognitive radio systems which are still in need of scalable and accurate solutions for both primary-to-secondary and secondary-to-secondary coordination. Key challenges in enabling a network layer spectrum coordination service are discussed along with the description of our system architecture and a detailed case-study for a specific example of spectrum coordination: client-AP association optimization in dense networks. Performance gains are evaluated through large-scale simulations with multiple overlapping networks, each consisting of 15-35 access points and 50-250 clients in a 0.5×0.5 sq.km. urban setting. Results show an average of 150% improvement in random deployments and upto 7× improvements in clustered deployments for the least-performing client throughputs with modest reductions in the mean client throughputs.

Cooperative spectrum sensing is an effective approach that utilizes spatial diversity gain to improve detection performance. Most studies assume that the background noise is exactly known. However, this is not realistic because of noise uncertainty which will significantly degrade the performance. A novel weighted hard combination algorithm with two thresholds is proposed by dividing the whole range of the local test statistic into three regions called the presence, uncertainty and absence regions, instead of the conventional two regions. The final decision is made by weighted combination at the common receiver. The key innovation is the full utilization of the information contained in the uncertainty region. It is worth pointing out that the weight coefficient and the local target false alarm probability, which determines the two thresholds, are also optimized to minimize the total error rate. Numerical results show this algorithm can significantly improve the detection performance, and is more robust to noise uncertainty than the existing algorithms. Furthermore, the performance of this algorithm is not sensitive to the local target false alarm probability at low SNR. Under sufficiently high SNR condition, this algorithm reduces to the improved one-out-of-N rule. As noise uncertainty is unavoidable, this algorithm is highly practical.

Spectrum sensing is one of the methods to identify available white spaces for secondary usage which was specified by the regulators. However, signal quality to be sensed can plunge to a very low signal-to-noise-ratio due to signal propagation and hence readings from individual sensors will be unreliable. Distributed sensing by the cooperation of multiple sensors is one way to cope with this problem because the diversity gain due to the combining effect of data captured at different position will assist in detecting signals that might otherwise not be detected by a single sensor. In effect, the probability of detection can be improved. We have implemented a distributed sensing system to evaluate the performance of different cooperative sensing algorithms. In this paper we describe our implementation and measurement experience which include the system design, specification of the system, measurement method, the issues and solutions. This paper also confirms the performance enhancement offered by distributed sensing algorithms, and describes several ideas for further enhancement of the sensing quality.

Designing a medium access control (MAC) protocol is a key for implementing any practical wireless network. In general, a MAC protocol is responsible for coordinating users in accessing spectrum resources. Given that a user in cognitive radio(CR) networks do not have priority in accessing spectrum resources, MAC protocols have to perform dynamic spectrum access (DSA) functions, including spectrum sensing, spectrum access, spectrum allocation, spectrum sharing and spectrum mobility, beside conventional control procedure. As a result, designing MAC protocols for CR networks requires more complicated consideration than that needed for conventional/primary wireless network. In this paper, we focus on two major perspectives related to the design of a CR-MAC protocol: dynamic spectrum access functions and network infrastructure. Five DSA functions are reviewed from the point of view of MAC protocol design. In addition, some important factors related to the infrastructure of a CR network including network architecture, control channel management, the number of radios in the CR device and the number of transmission data channels are also discussed. The remaining challenges and open research issues are addressed for future research to aim at obtaining practical CR-MAC protocols.

This paper presents a novel acoustic modeling technique of large vocabulary automatic speech recognition for under-resourced languages by leveraging well-trained acoustic models of other languages (called source languages). The idea is to use source language acoustic model to score the acoustic features of the target language, and then map these scores to the posteriors of the target phones using a classifier. The target phone posteriors are then used for decoding in the usual way of hybrid acoustic modeling. The motivation of such a strategy is that human languages usually share similar phone sets and hence it may be easier to predict the target phone posteriors from the scores generated by source language acoustic models than to train from scratch an under-resourced language acoustic model. The proposed method is evaluated using on the Aurora-4 task with less than 1 hour of training data. Two types of source language acoustic models are considered, i.e. hybrid HMM/MLP and conventional HMM/GMM models. In addition, we also use triphone tied states in the mapping. Our experimental results show that by leveraging well trained Malay and Hungarian acoustic models, we achieved 9.0% word error rate (WER) given 55 minutes of English training data. This is close to the WER of 7.9% obtained by using the full 15 hours of training data and much better than the WER of 14.4% obtained by conventional acoustic modeling techniques with the same 55 minutes of training data.

Cognitive Radio (CR) is aimed at increasing the efficiency of spectrum utilization by allowing unlicensed users to access, in an opportunistic and non-interfering manner, some licensed bands temporarily and/or spatially unoccupied by the licensed users. The analysis of CR systems usually requires the spectral activity of the licensed system to be represented and characterized in a simple and tractable, yet accurate manner, which is accomplished by means of spectrum models. In order to guarantee the realism and accuracy of such models, the use of empirical spectrum occupancy data is essential. In this context, this paper explains the complete process of spectrum modeling, from the realization of field measurements to the obtainment of the final validated model, and highlights the main relevant aspects to be taken into account when developing spectrum usage models for their application in the context of the CR technology.

In this paper, we propose a robust speech/music classification algorithm to improve the performance of speech/music classification in the selectable mode vocoder (SMV) of 3GPP2 using deep belief networks (DBNs), which is a powerful hierarchical generative model for feature extraction and can determine the underlying discriminative characteristic of the extracted features. The six feature vectors selected from the relevant parameters of the SMV are applied to the visible layer in the proposed DBN-based method. The performance of the proposed algorithm is evaluated using the detection accuracy and error probability of speech and music for various music genres. The proposed algorithm yields better results when compared with the original SMV method and support vector machine (SVM) based method.

Ultra-wideband pulse radar exhibits high range resolution, and excellent capability in penetrating dielectric media. With that, it has great potential as an innovative non-destructive inspection technique for objects such as human body or concrete walls. For suitability in such applications, we have already proposed an accurate permittivity estimation method for a 2-dimensional dielectric object of arbitrarily shape and clear boundary. In this method, the propagation path estimation inside the dielectric object is calculated, based on the geometrical optics (GO) approximation, where the dielectric boundary points and its normal vectors are directly reproduced by the range point migration (RPM) method. In addition, to compensate for the estimation error incurred using the GO approximation, a waveform compensation scheme employing the finite-difference time domain (FDTD) method was incorporated, where an initial guess of the relative permittivity and dielectric boundary are employed for data regeneration. This study introduces the 3-dimensional extension of the above permittivity estimation method, aimed at practical uses, where only the transmissive data are effectively extracted, based on quantitative criteria that considers the spatial relationship between antenna locations and the dielectric object position. Results from a numerical simulation verify that our proposed method accomplishes accurate permittivity estimations even for 3-dimensional dielectric medium of wavelength size.