To take advantage of the multiuser diversity resulted from the variation in channel conditions among the users, it has become an interesting and challenging problem to efficiently allocate the resources such as subcarriers, bits, and power. Most of current research concentrates on solving the resource-allocation problem for all users together in a centralized way, which brings about high computational complexity and makes it impractical for real system. Therefore, a coalitional game framework for downlink multi-user resource allocation in long term evolution (LTE) system is proposed, based on the divide-and-conquer idea. The goal is to maximize the overall system data rate under the constraints of each user’s minimal rate requirement and maximal transmit power of base station while considering the fairness among users. In this framework, a coalitional formation algorithm is proposed to achieve optimal coalition formation and a two-user bargaining algorithm is designed to bargain channel assignment between two users. The total computational complexity is greatly reduced in comparison with conventional methods. The simulation results show that the proposed algorithms acquire a good tradeoff between the overall system throughout and fairness, compared to maximal rate and max-min schemes.

Technology of cognitive radio networks has emerged as an effective method to enhance the utilization of the radio spectrum where the primary users have priority to use the spectrum, and the secondary users try to exploit the spectrum unoccupied by the primary users. In this paper, considering the non-saturated condition, the performance analysis for the IEEE 802.11-based cognitive radio networks is presented with single-channel and multi-channel, respectively. For the single-channel case, an absorbing Markov chain model describing the system transitions is constructed, and one-step transition probability matrix of the Markov chain is given. By using the method of probability generating function, the non-saturated throughput of the secondary users is obtained. For the multi-channel case, taking into account the negotiation-based sensing policy, the mean number of unused channels perceived by the second users is given, and then the non-saturated aggregate throughput of the secondary users is derived. Finally, numerical examples are provided to show the influences of the non-saturated degree, the number of the secondary users and the channel utilization of the primary users on the performance measures for the non-saturated throughput with single-channel and the non-saturated aggregate throughput with multi-channel.

Energy efficiency is a critical issue in wireless sensor networks (WSNs). In order to minimize energy consumption and balance energy dissipation throughout the whole network, a systematic energy-balanced cooperative transmission scheme in WSNs is proposed in this paper. This scheme studies energy efficiency in systematic view. For three main steps, namely nodes clustering, data aggregation and cooperative transmission, corresponding measures are put forward to save energy. These measures are well designed and tightly coupled to achieve optimal performance. A half-controlled dynamic clustering method is proposed to avoid concentrated distribution of cluster heads caused by selecting cluster heads randomly and to get high spatial correlation between cluster nodes. Based on clusters built, data aggregation, with the adoption of dynamic data compression, is performed by cluster heads to get better use of data correlation. Cooperative multiple input multiple output (CMIMO) with an energy-balanced cooperative cluster heads selection method is proposed to transmit data to sink node. System model of this scheme is also given in this paper. And simulation results show that, compared with other traditional schemes, the proposed scheme can efficiently distribute the energy dissipation evenly throughout the network and achieve higher energy efficiency, which leads to longer network lifetime span. By adopting orthogonal space time block code (STBC), the optimal number of the cooperative transmission nodes varying with the percentage of cluster heads is also concluded, which can help to improve energy efficiency by choosing the optimal number of cooperative nodes and making the most use of CMIMO.

This paper proposes a Tomlinson-Harashima precoding (THP) transceiver for multiple-input multiple-output (MIMO) system, where the spatial correlation information at the transmitter is included in the channel state information (CSI) model. It derives the total mean square error (MSE) and its lower bound as a function of precoding matrix. Then, a precoding matrix and the closed-form expression of minimum MSE lower bound are obtained by use of optimization and matrix theory. By right-multiplying a proper unitary matrix to the above precoding matrix, the paper develops the optimal precoding matrix, thus the optimal transceiver matrices are achieved. Simulation results show that the total MSE performance of the proposed method outperforms the existing linear method and the naive THP method.

In this paper, we present a non-transferable utility coalition graph game (NTU-CGG) based resource allocation scheme with relay selection for a downlink orthogonal frequency division multiplexing (OFDMA) based cognitive radio networks to maximize both system throughput and system fairness. In this algorithm, with the assistance of others SUs, SUs with less available channels to improve their throughput and fairness by forming a directed tree graph according to spectrum availability and traffic demands of SUs. So this scheme can effectively exploit both space and frequency diversity of the system. Performance results show that, NTU-CGG significantly improves system fairness level while not reducing the throughput comparing with other existing algorithms.

This paper presents a novel near-field source localization method based on the time-frequency sparse model. Firstly, the method converts the time domain data of array output into time-frequency domain by time-frequency transform; then constructs sparse localization model by utilizing the specially selected time-frequency points, and finally the greedy algorithms are chosen to solve the sparse problem to localize the source. When the coherent sources exist, we propose an additional iterative selection procedure to improve the estimation performance. The proposed method is suitable for uncorrelated and coherent sources, moreover, the improved estimation accuracy and the robustness to low signal to noise ratio (SNR) are achieved. Simulations results verify the efficiency of the proposed algorithm.

This paper presents a distributed and adaptive fluctuation control scheme for many-to-one routing (FCM) in wireless sensor networks. Unlike well-known topology control schemes, the primary design objective is to reduce the fluctuation which happens due to overload of sensors in a data collection tree. More specifically, an estimation model of a sensor available capacity based on the number of its neighbors is proposed. In addition, this paper proposes a parent selection mechanism by three-way handshake. With such model and the selection mechanism, it is ensured that the load of a sensor does not exceed its available capacity. Finally, an adaptive maintenance mechanism is proposed to adjust the estimation of a sensor available capacity as the network environment changes. Simulation results demonstrate the effectiveness of the scheme.

This paper investigates the impacts of network coding (NC) on user fairness from the network perspective in multiple access channels. Firstly, simultaneous outage is exploited as the metric to evaluate user fairness. To optimize it, a fairness-oriented physical layer NC scheme is proposed, in which users’ signals are superimposed in specific calculated proportion at relay and then decoded by maximal ratio combination (MRC) and serial interference cancellation (SIC) at destination. Furthermore, the influence of power allocation coefficient error on simultaneous outage is also analyzed to reveal its correlation with user fairness. Simulation results indicate that the proposed scheme can supply approximately equal performance promotion for each cooperation-participated user and outperform those without fairness consideration in terms of the employed metric.

Many flows in data centers have deadlines and missing deadlines would hurt application performance such as affecting response quality in Web applications or delaying computing jobs in MapReduce-like systems. However, transmission control protocol (TCP) which is widely used in data centers now cannot provide deadline-aware transmission service. Service differentiation only distinguishes flows with different priority but is unable to guarantee completion time. In this paper, we propose a new protocol named deadline-aware TCP (DATCP) to provide deadline-aware transmission service for the commoditized data centers, which can be used as a flexible method for flow scheduling. DATCP combines flow urgency and importance to calculate precedence. Flow urgency is dynamically adjusted according to the gap between desired rate and actual throughput. Setting importance can avoid starving the important but no-urgent flows. Furthermore, a flow quenching method is presented which allows as many high precedence flows as possible to meet deadlines under heavy network load. By extensive simulations, the performance of DATCP was evaluated. Simulation results show that DATCP can make flows meet deadlines effectively.

Cost-sensitive learning has been applied to resolve the multi-class imbalance problem in Internet traffic classification and it has achieved considerable results. But the classification performance on the minority classes with a few bytes is still unhopeful because the existing research only focuses on the classes with a large amount of bytes. Therefore, the class-dependent misclassification cost is studied. Firstly, the flow rate based cost matrix (FCM) is investigated. Secondly, a new cost matrix named weighted cost matrix (WCM) is proposed, which calculates a reasonable weight for each cost of FCM by regarding the data imbalance degree and classification accuracy of each class. It is able to further improve the classification performance on the difficult minority class (the class with more flows but worse classification accuracy). Experimental results on twelve real traffic datasets show that FCM and WCM obtain more than 92% flow g-mean and 80% byte g-mean on average; on the test set collected one year later, WCM outperforms FCM in terms of stability.

It becomes challenging in order to represent, discover and exchange location information in a ubiquitous environment due to dynamic movement and interaction between mobile nodes inside. In this paper, a new method is presented in order to make location information context-aware so that organizing the format of location information and maintaining the communication between direct connected nodes in a ubiquitous environment is enabled. The structure of a contextual location information repository and a context information communication protocol is manipulated to implement the proposed features. According to the simulation results in network simulator version2 (NS2), the new method has depicted good discovery success and consumed efficient service discovery bandwidth. Other network traffic, i.e. transmission control protocol, (TCP) has been simulated in the scenarios but the new location-aware method has shown its robustness with continuous context discovery process.

This paper proposes a novel graph-based transductive learning algorithm based on manifold regularization. First, the manifold regularization was introduced to probabilistic discriminant model for semi-supervised classification task. And then a variation of the expectation maximization (EM) algorithm was derived to solve the optimization problem, which leads to an iterative algorithm. Although our method is developed in probabilistic framework, there is no need to make assumption about the specific form of data distribution. Besides, the crucial updating formula has closed form. This method was evaluated for text categorization on two standard datasets, 20 news group and Reuters-21578. Experiments show that our approach outperforms the state-of-the-art graph-based transductive learning methods.

The universal combination operation model is a comprehensive decision model of continuous-valued logic. It overcomes limitations of the scope of operations of reasoning operators in the current comprehensive decision-making system. This article discusses relationship of mutual information and general correlation coefficient and gives the corresponding rules of them, the optimal matching operator is selected to complete fuzzy decision according to mutual information between candidate attributes. The relationship of mutual information between attributes and generalized correlative coefficient provides the principle to select the matching operator. According to the results of experiment, it is more reasonable to enhance classified precision effectively. There is a certain application value of the article’s method.

Recently, Susilo et al.’s perfect concurrent signature scheme (PCS1) and Wang et al.’s improved perfect concurrent signature scheme (iPCS1) are proposed, which are considered as good improvements on concurrent signatures, and they adopt the same algorithms. In this paper, we develop generic perfect concurrent signature algorithms of which Susilo et al. and Wang et al.’s algorithms turn out to be a special instance. We also obtain numerous new, efficient variants from the generic algorithms which have not been proposed before. To display the advantage of these variants, a modified privacy-preserving PCS protocol is given. It shows that the new variants adapt to the protocol well and can form concrete privacy-preserving PCS schemes, while the original algorithms do not. Security proofs and efficiency analysis are also given.

The granularity of the flexible bandwidth optical network is the spectral slots, which is much smaller than that of the wavelength switch optical network. For the dynamic clients’ connections setup and tear down processes, it will give rise to fragmentation of spectral resources. It is the decline in the probability of ?nding suf?cient contiguous spectrum for new connections that result in the fragmentation of spectral resource. To be more specific, these spectra may be unavailable and waste. In this case, the severe waste of the spectrum will lead to low efficiency in spectral utilization and will not adapt to large capacity requirements of transmission in the future. Because path computation element (PCE) framework has the characteristics of the central disposal and deployment of the spectrum resource, we construct the spectral resource allocation scenario based on PCE framework in the flexible bandwidth optical network to use spectrum resource effectively. Based on the principle of the generation of the fragmentation, we put forward a spectrum resource defragmentation algorithm to consolidate the available spectrum for clients’ connections. The simulation results indicate that this algorithm is able to reduce fragmentation of network, improve the continuity of spectral resource, reduce the blocking rate of services in the network and improve the spectral efficiency significantly.

A novel 10 GHz eight-phase voltage-controlled oscillator (VCO) architecture applied in clock and data recovery (CDR) circuit for 40 Gbit/s optical communications system is proposed. Compared with the traditional eight-phase oscillator, a new ring CL ladder filter structure with four inductors is proposed. The VCO is designed and fabricated in IBM 90 nm complementary metal-oxide-semiconductor transistor (CMOS) technology. Measurement results show the tuning range is 9.2 GHz~11.0 GHz and the phase noise of 108.85 dBc/Hz at 1 MHz offset from the carrier frequency of 10 GHz. The chip area of VCO is 500 × 685 and the power dissipation is 17.4 mW with the 1.2 V supply voltage.

This paper presents the design and implementation of a fully integrated low noise multi-band LC-tank voltage-controlled-oscillator (VCO). Multi-band operation is achieved by using switched-capacitor resonator. Additional three-bit binary weighted capacitor array is also used to extend frequency tuning range in each band. To lower phase noise, two noise filters are added and a linear varactor is adopted. Implemented in a 0.18 μm complementary-metal- oxide-semiconductor (CMOS) process, the VCO achieves a frequency tuning range covering 2.26~2.48 GHz, 2.48~ 2.78 GHz, 2.94~3.38 GHz, and 3.45~4.23 GHz while occupies a chip area of 0.52 mm2. With a 1.8 V power supply, it draws a current of 10.9 mA, 10.6 mA, 8.8 mA, and 6.2 mA from the lowest band to the highest band respectively. The measured phase noise is 109~ 120 dBc/Hz and 121~ 131 dBc/Hz at a 1 MHz and 2.5 MHz offset from the carrier, respectively.

Distributed compressed sensing (DCS) is an emerging research field which exploits both intra-signal and inter-signal correlations. This paper focuses on the recovery of the sparse signals which can be modeled as joint sparsity model (JSM) 2 with different nonzero coefficients in the same location set. Smoothed L0 norm algorithm is utilized to convert a non-convex and intractable mixed L2,0 norm optimization problem into a solvable one. Compared with a series of single-measurement-vector problems, the proposed approach can obtain a better reconstruction performance by exploiting the inter-signal correlations. Simulation results show that our algorithm outperforms L1,1 norm optimization for both noiseless and noisy cases and is more robust against thermal noise compared with L1,2 recovery. Besides, with the help of the core concept of modified compressed sensing (CS) that utilizes partial known support as side information, we also extend this algorithm to decode correlated row sparse signals generated following JSM 1.