The digital proportion control is introduced to improve the performance of the analog adaptive interference cancellation system (ICS). For the high frequency parts of the signals after multiplier are not required, the sampling frequency need not satisfy the sampling theorem for high frequency. Because the sampling, calculation and output expend time in digital control, the ideal condition, delay condition and delay-wait condition are taken into account. Through analyzing the system model with three conditions, we gain the stable conditions of the system, the optimization step factors that can make the system converge fastest and the formulas of the interference cancellation ratios (ICRs). One step convergence can be accomplished under ideal condition, whereas the system can not converge in one step under delay condition and delay-wait condition. The calculation results show the convergence speed of delay-wait condition is slower than that of delay condition. The ICR is improved with the increase of the step factor which is in stable bound, but the convergence speed is decreased if the step factor exceeds the optimization step factor. In order to avoid that confine, the method of amending the steady state weight to improve the ICR is proposed. The analyses are in agreement with the computer simulations

Coordinated multi-point (CoMP) joint transmission is considered in the 3rd generation partnership project (3GPP) long term evolution (LTE)-advanced as a key technique to mitigate inter-cell interference and improve the cell-edge performance. To effectively apply CoMP joint transmission, efficient frequency reuse schemes need to be designed to support resource management cooperation among coordinated cells. However, most of the existing frequency reuse schemes are not suitable for CoMP systems due to not considering multi-point joint transmission scenarios in their frequency reuse rules. In addition, the restrictions of frequency resources in those schemes result in a high blocking probability. To solve the above two problems, a multi-beam cooperative frequency reuse (MBCFR) scheme is proposed in this paper, which reuses all the available frequency resources in each sector and supports multi-beam joint transmission for cell-edge users. Besides, the blocking probability is proved to be efficiently reduced. Moreover, a frequency-segment-sequence based MBCFR scheme is introduced to further reduce the inter-cell interference. System level simulations demonstrate that the proposed scheme results in higher cell-edge average throughput and cell-average throughput with lower blocking probability.

Two-hop relaying systems suffer spectral efficiency loss due to the half-duplex property of relays. This paper proposes an efficient relaying protocol which can recover the spectral efficiency loss but still work with half-duplex relays. However, there exists inter-relay interference which degrades the performance of the protocol. With this consideration, a power control policy is derived to suppress the interference using game theory, and then an algorithm is given to facilitate distributed implementation. Furthermore, impact of deploying more destination antennas on performance of the relaying protocol is investigated. Simulation results show that, with the power control policy, the proposed relaying protocol can achieve high spectral efficiency.

This paper investigates the relay selection and power allocation problem in multi-user based cooperative networks, where intermediate relay nodes help source forward information to destination using decode-and-forward (DF) relaying protocol. Specifically, we propose a novel multi-relay nodes selection strategy taking both instantaneous channel state information (I-CSI) and residual energy into consideration, by which ‘emergence’ diversity gain can be achieved and the imbalance of resource utilization can be overcome. Besides, using Largangian dual-primal decomposition and subgradient projection approach, an optimal power allocation algorithm at source and cooperative relay nodes is presented with the constraints of each user’s individual quality of service (QoS) requirements and system total transmit power. Theoretical analysis and simulation results demonstrate that the proposed scheme can significantly improve energy efficiency, while guaranteeing a good balance between achievable data rate and average network lifetime with relatively low implementation complexity.

For practical considerations, it is essential to accelerate the convergence speed of the decoding algorithm used in an iterative decoding system. In this paper, replica versions of horizontal-shuffled decoding algorithms for low-density parity-check (LDPC) codes are proposed to improve the convergence speed of the original versions. The extrinsic information transfer (EXIT) chart technique is extended to the proposed algorithms to predict their convergence behavior. Both EXIT chart analysis and numerical results show that replica plain horizontal-shuffled (RPHS) decoding converges much faster than both plain horizontal-shuffled (PHS) decoding and the standard belief-propagation (BP) decoding. Furthermore, it is also revealed that replica group horizontal- shuffled (RGHS) decoding can increase the parallelism of RPHS decoding as well as preserve its high convergence speed if an equivalence condition is satisfied, and is thus suitable for hardware implementation.

In cognitive radio (CR), power allocation plays an important role in protecting primary user from disturbance of secondary user. Some existing studies about power allocation in CR utilize ‘interference temperature’ to achieve this protection, which might not be suitable for the OFDM-based CR. Thus in this paper, power allocation problem in multi-user orthogonal frequency division multiplexing (OFDM) and distributed antenna cognitive radio with radio over fiber (RoF) is firstly modeled as an optimization problem, where the limitation on secondary user is not ‘interference temperature’, but that total throughput of primary user in all the resource units (RUs) must be beyond the given threshold. Moreover, based on the theorem about maximizing the total throughput of secondary user, equal power allocation algorithm is introduced. Furthermore, as the optimization problem for power allocation is not convex, it is transformed to be a convex one with geometric programming, where the solution can be obtained using duality and Karush-Kuhn-Tucker (KKT) conditions to form the optimal power allocation algorithm. Finally, extensive simulation results illustrate the significant performance improvement of the optimal algorithm compared to the existing algorithm and equal power allocation algorithm.

TD-MBMS is the abbreviation of time division-synchronous code division multiple access (TD-SCDMA) multimedia broadcast and multicast service (MBMS) , which was introduced in the release 6 of universal mobile telecommunications system (UMTS) in order to deliver multimedia data more efficiently. Since TD-MBMS may work on the same time slot (TS) of the same frequency with TD-SCDMA and time division-high speed downlink packet access (TD-HSDPA) as described in the unified timeslot networking (UTN) strategy, the work of co-existing interference analysis is an important research filed. This article firstly analyzes theoretically the co-existing interference between TD-MBMS and TD-SCDMA/TD-HSDPA. Then, system level simulation is used to quantify the co-existing interference under different scenarios considering the transmit power of TD-MBMS and TD-HSDPA and the load factor of TD-SCDMA network. Simulation results show that TD-SCDMA/TD-HSDPA has little impact on the TD-MBMS while the interference from TD-MBMS to TD-SCDMA/TD-HSDPA is more serious and the corresponding isolation distance is then suggested. The results are useful for radio network planning and optimization of TD-SCDMA.

Uniform channel decomposition (UCD) has been proven to be optimal in bit error rate (BER) performance and strictly capacity lossless when perfect channel state information (CSI) is assumed to be available at both the transmitter and receiver side. In practice, CSI can be obtained by channel estimation at receiver and conveyed to transmitter via a limited-rate feedback channel. In such case, the implementation of traditional UCD by treating the imperfect CSI as perfect CSI cause significant performance degradation due to inevitable channel estimation error and vector quantization error. To overcome this problem, a practical robust UCD scheme was proposed in this paper, which includes two steps, firstly, a matching architecture was proposed to eliminate the mismatch between CSI at receiver (CSIR) and CSI at transmitter (CSIT), secondly, an MMSE based robust UCD scheme considering channel estimation error and vector quantization error as an integral part of the design was derived. Simulation results show that the proposed practical robust UCD scheme is capable of improving the BER performance greatly in the context of channel estimation error and vector quantization error compared with the traditional UCD scheme.

The problem of resource allocation in multiuser orthogonal frequency division multiplexing (OFDM) system is a combinatorial optimization problem, difficult to obtain optimal solutions in polynomial time. For the sake of reducing complexity, it can be solved either by relaxing constraints and making use of linear algorithms or by metaheuristic methods. In this paper, an algorithm based on ant colony optimization (ACO), which is a typical algorithm of metaheuristic methods, is proposed for the problem, utilizing excellent search performance of ACO to obtain good solutions. In addition, a parameter is applied to balance the efficiency and fairness of resource allocation. Performance analysis between algorithms based on ACO and genetic algorithm (GA) is carried out, indicating that the proposed algorithm based on ACO outperforms traditional linear algorithms as well as GA in the system throughput with assurance of fairness simultaneously, being as a promising technology for OFDM resource allocation.

Opportunistic spectrum access (OSA) is considered as a promising approach to mitigate spectrum scarcity by allowing unlicensed users to exploit spectrum opportunities in licensed frequency bands. Derived from the existing channel-hopping multiple access (CHMA) protocol, we introduce a hopping control channel medium access control (MAC) protocol in the context of OSA networks. In our proposed protocol, all nodes in the network follow a common channel-hopping sequence; every frequency channel can be used as control channel and data channel. Considering primary users’ occupancy of the channel, we use a primary user (PU) detection model to calculate the channel availability for unlicensed users’ access. Then, a discrete Markov chain analytical model is applied to describe the channel states and deduce the system throughput. Through simulation, we present numerical results to demonstrate the throughput performance of our protocol and thus validate our work.

Internet application identification is needed by network management in many aspects, such as quality of service (QoS) management, intrusion detection, traffic engineering, accounting, and so on. This article makes an in-depth study of precise identification of Internet applications by using flow characteristics instead of well-known port or application signature match. A novel approach that identifies the application type of an Internet protocol (IP) flow by finding what flow the flow looks the most like based on medium mathematics system (MMS) is proposed. The approach differs from previous ones mainly in two aspects: it has inherent scalability due to its use of the measure of n-dimensional medium truth degree; not only features of a flow, but also the association between the flow and the other flows of the same host as well as the relation among all flows of a host are employed to recognize a flow’s application type. For the present, some popular applications are concentrated on, and up to six application types can be identified with better accuracy. The results of experiments conducted on Internet show that the proposed methodology is effective and deserves attention.

Energy-efficient communication is an important requirement for mobile relay networks due to the limited battery power of user terminals. This paper considers energy-efficient relaying schemes through selection of mobile relays in cooperative cellular systems with asymmetric traffic. The total energy consumption per information bit of the battery-powered terminals, i.e., the mobile station (MS) and the relay, is derived in theory. In the joint uplink and downlink relay selection (JUDRS) scheme we proposed, the relay which minimizes the total energy consumption is selected. Additionally, the energy-efficient cooperation regions are investigated, and the optimal relay location is found for cooperative cellular systems with asymmetric traffic. The results reveal that the MS-relay and the relay-base station (BS) channels have different influence over relay selection decisions for optimal energy-efficiency. Information theoretic analysis of the diversity-multiplexing tradeoff (DMT) demonstrates that the proposed scheme achieves full spatial diversity in the quantity of cooperating terminals in this network. Finally, numerical results further confirm a significant energy efficiency gain of the proposed algorithm comparing to the previous best worse channel selection and best harmonic mean selection algorithms.

Based on strong designated verifiers signatures, a new fair concurrent signature scheme is proposed. Compared with the previous concurrent signature schemes, even if a keystone must be chosen by the initial signer, the matching signer will easily get the keystone through an extraction algorithm. Due to the property of strong designate verifying, the initial signer couldn’t make use of the keystone prepared carefully to deceive the matching signer. Then the matching signer is able to participate actively the signature scheme. Besides, there aren’t bilinear operations in the keystone algorithm to deliver the keystone efficiently. Therefore the efficiency of our signature scheme is also improved.

Hierarchical identity-based signature (HIBS) has wide applications in the large network. However, the existing works cannot solve the trade-off between the security and efficiency. The main challenge at present is to construct a high efficient and strong secure HIBS with a low computation cost. In this paper, a new construction of HIBS scheme is proposed. The new scheme achieves the adaptive security which is a strong security in the identity-based cryptography. But our scheme has short public parameters and the private keys size shrinks as the hierarchy depth increases. The signature size is a constant and the cost of verification only requires four bilinear pairings, which are independent of hierarchy depth. Furthermore, under the q-strong computational diffie-Hellman problem (q-SDH) assumption, the scheme is provably secure against existential forgery for adaptive chosen message and identity attack in the standard model.

Image corner detection plays an important role in image analysis and recognition. This paper presents a novel corner detector based on the growing neural gas (GNG) network and this proposed detector is called GNG-C. With the GNG network, image topology information can be learned and used to implement corner detection. The GNG-C approach can be described as consisting of the following steps. First, a canny edge detector is used to acquire the contour information of the input image. This edge information is used to train a modified GNG network. A special stopping criterion is defined to terminate network learning. Second, vectors formed between network nodes and their neighbors are used to measure curvatures. Third, dynamic regions of support (ROS) are determined based on these curvatures. These ROS are used to suppress curvature noise. The curvature values of the nodes are then analyzed to estimate the candidate corners. Finally, the candidates are distilled by a non-maxima suppression process to obtain the final set of corners. Experiments on both artificial and real images show that the proposed corner detection method is feasible and effective.

To support quality of service (QoS) management on current Internet working with best effort, we bring forth a systematic approach for end-to-end QoS diagnosis and quantitative guarantee. For QoS diagnosis, we take contexts of a service into consideration in a comprehensive way that is realized by exploiting causal relationships between a QoS metric and its contexts with the help of Bayesian network (BN) structure learning. Context discretization algorithm and node ordering algorithm are proposed to facilitate BN structure learning. The QoS metric is diagnosed to be causally related to its causal contexts, and the QoS metric can be quantitatively guaranteed by its causal contexts. For quantitative QoS guarantee, those causal relationships are first modeled quantitatively by BN parameter learning. Then, the QoS metric is guaranteed to certain value with a probability given its causal contexts tuned to suitable values, that is, quantitative QoS guarantee is reached. Simulations with three sequential stages: context discretization, QoS diagnosis and quantitative QoS guarantee, on a peer-to-peer (P2P) network, are discussed and our approach is validated to be effective.

Integrity constraint is a formula that checks whether all necessary information has been explicitly provided. It can be added into ontology to guarantee the data-centric application. In this paper, a set of constraint axioms called IC-mapping axioms are stated. Based on these axioms, a special ontology with integrity constraint, which is adapted to map ontology knowledge to data in relational databases, is defined. It’s generated through our checking and modification. Making use of the traditional mapping approaches, it can be mapped to relational databases as a normal ontology. Unlike what in the past, a novel mapping approach named IC-based mapping is proposed in accordance with such special ontology. The detailed algorithm is put forward and compared with other existing approaches used in Semantic Web applications. The result shows that our method is advanced to the traditional approaches.

The purpose of this paper is to exploit compressive sensing (CS) method in dealing with electrocardiography (ECG) and electroencephalography (EEG) signals at a high compression ratio. In order to get sparse data of ECG and EEG signals before being compressed, a combined scheme was presented by using wavelet transform and iterative threshold method; then, compressive sensing is executed to make the data compressed. After doing compressive sensing, Bayesian compressive sensing (BCS) is used to reconstruct the original signals. The simulation results show that compressive sensing is an effective method to make data compressed for ECG and EEG signals with high compression ratio and good quality of reconstruction. Furthermore, it shows that the proposed scheme has good denoising effects.