This letter presents a novel pre-transformed interleaved frequency division multiple access (IFDMA) transmission structure that improves system performance without the desire of channel information at the transmitter. Simulation results show that the proposed structure can provide improved system performance while only moderately increasing the complexity, and keeping the advantage of a low peak-to-average power ratio (PAPR) of the transmitted signal for SISO and MISO channels.

Consider an undirected graph G=(V,E) with n (=|V|) vertices and m (=|E|) edges. It is well-known that the problem of computing the sequence Nn-1,Nn,...,Nm is #P-complete (see e.g.,[3]), where Ni denotes the number of connected spanning subgraphs with i (n-1!im) edges in G. In this paper, by proving new inequalities on the sequence Nn-1,Nn,...,Nm, we show an interesting and stronger property that the sequence γn-1,γn,...,γm, where γi denotes the average number of spanning trees in the connected spanning subgraphs with i edges, is a convex sequence as well as a monotonically increasing sequence, although this property does not hold for the sequence Nn-1,Nn,...,Nm.

In this letter, we investigate the physical layer security in multi-user multi-relay networks, where each relay is not merely a traditional helper, but at the same time, can become a potential eavesdropper. We first propose an efficient low-complexity user and relay selection scheme to significantly reduce the amount of channel estimation as well as the amount of potential links for comparison. For the proposed scheme, we derive the closed-form expression for the lower bound of ergodic secrecy rate (ESR) to evaluate the system secrecy performance. Simulation results are provided to verify the validity of our expressions and demonstrate how the ESR scales with the number of users and relays.

Sharing data might bring the risk of disclosing the sensitive knowledge in it. Usually, the data owner may choose to sanitize data by modifying some items in it to hide sensitive knowledge prior to sharing. This paper focuses on protecting sensitive knowledge in the form of frequent itemsets by data sanitization. The sanitization process may result in side effects, i.e., the data distortion and the damage to the non-sensitive frequent itemsets. How to minimize these side effects is a challenging problem faced by the research community. Actually, there is a trade-off when trying to minimize both side effects simultaneously. In view of this, we propose a data sanitization method based on evolutionary multi-objective optimization (EMO). This method can hide specified sensitive itemsets completely while minimizing the accompanying side effects. Experiments on real datasets show that the proposed approach is very effective in performing the hiding task with fewer damage to the original data and non-sensitive knowledge.

One major challenge to implement orthogonal frequency division multiplexing (OFDM) systems over doubly selective channels is the non-negligible intercarrier interference (ICI), which significantly degrades the system performance. Existing solutions to cope with ICI include zero-forcing (ZF), minimum mean square error (MMSE) and other linear or nonlinear equalization methods. However, these schemes fail to achieve a satisfactory tradeoff between performance and computational complexity. To address this problem, in this paper we propose two novel nonlinear ICI cancellation techniques, which are referred to as parallel interference cancelation (PIC) and hybrid interference cancelation (HIC). Taking advantage of the special structure of basis expansion model (BEM) based channel matrices, our proposed schemes enjoy low computational complexity and are capable of cancelling ICI effectively. Moreover, since the proposed schemes can flexibly select different basis functions and be independent of the channel statistics, they are applicable to practical OFDM based systems such as DVB-T2 over doubly selective channels. Theoretical analysis and simulation results both confirm their performance-complexity advantages in comparison with some existing methods.

We model a road network as a directed graph G(V,E) with a source s and a sink t, where each edge e has a positive length l(e) and each vertex v has a distribution function αv with respect to the traffic entering and leaving v. This paper proposes a polynomial time algorithm for evaluating the importance of each edge e E whicn is defined to be the traffic f(e) passing through e in order to assign the required traffic Fst(0) from s to t along only shortest s-t paths in accordance with the distribution function αv at each vertex v.

This paper discusses how to compute the parametric sensitivity function in continuous-time Markov chains (CTMC). The sensitivity function is the first derivative of the steady-state probability vector regarding a CTMC parameter. Since the sensitivity function is given as a solution of linear equations with a sparse matrix, several linear equation solvers are available to obtain it. In this paper, we consider Jacobi and successive-over relaxation as variants of the Gauss-Seidel algorithm. In addition, we develop an algorithm based on the Takahashi method for the sensitivity function. In numerical experiments, we comprehensively evaluate the performance of these algorithms from the viewpoint of computation time and accuracy.

With the development of Internet technology, the demand for signing electronic contracts has been greatly increased. The electronic contract generated by the participants in an online way enjoys the same legal effect as paper contract. The fairness is the key issue in jointly signing electronic contracts by the involved participants, so that all participants can either get the same copy of the contract or nothing. Most existing solutions only focus on the fairness of electronic contract generation between two participants, where the digital signature can effectively guarantee the fairness of the exchange of electronic contracts and becomes the conventional technology in designing the contract signing protocol. In this paper, an efficient blockchain-based multi-party electronic contract signing (MECS) protocol is presented, which not only offers the fairness of electronic contract generation for multiple participants, but also allows each participant to aggregate validate the signed copy of others. Security analysis shows that the proposed MECS protocol enjoys unforgeability, non-repudiation and fairness of electronic contracts, and performance analysis demonstrates the high efficiency of our construction.

In this paper, an automatic retransmission request (ARQ) scheme for IEEE 802.11ac is presented, which can solve the problem of severe packet loss and greatly improve the performance in error-prone environments. The proposed solution only requires to be deployed on the sender and is compatible with the 802.11 protocol. The algorithm utilizes the basic strategy of sliding retransmission and then adds the method of copying frames. The media access control (MAC) protocol data unit (MPDU) lost in the transmission and the newly added data frame brought by the sliding window change are replicated. The scheme retransmits the duplicated aggregated packet and further improves the throughput by increasing the probability of successful transmission of sub-frames. Besides, we also establish a mathematical model to analyze the performance of the proposed scheme. We introduce the concept of average aggregated sub-frames and express the sliding retransmission strategy as the aggregated transmission of average aggregated sub-frames, thereby simplifying the model and effectively analyzing the theoretical throughput of the proposed algorithm. The simulation results of Network simulator 3 (NS-3) simulation results demonstrate that the performance of the proposed algorithm is better than the traditional sliding retransmission ARQ algorithm in error-prone channels with a higher physical layer rate.

In this paper, we propose a polynomial time algorithm for computing the expected maximum number of vertex-disjoint s-t paths in a probabilistic basically series-parallel directed graph and a probabilistic series-parallel undirected graph with distinguished source s and sink t(st), where each edge has a mutually independent failure probability and each vertex is assumed to be failure-free.

Let Ni be the number of connected spanning subgraphs with i(n-1 i m) edges in an n-vertex m-edge undirected graph G=(V,E). Although Nn-1 is computed in polynomial time by the Matrix-tree theorem, whether Nn is efficiently computed for a graph G is an open problem (see e.g., [2]). On the other hand, whether Nn2≥ Nn-1Nn+1 for a graph G is also open as a part of log concave conjecture (see e.g., [6],[12]). In this paper, for a complete graph Kn, we give the formulas for Nn, Nn+1, by which Nn, Nn+1 are respectively computed in polynomial time on n, and, in particular, prove Nn2> Nn-1Nn+1 as well.

Cloud computing is a unlimited computing resource and storing resource, which provides a lot of convenient services, for example, Internet and education, intelligent transportation system. With the rapid development of cloud computing, more and more people pay attention to reducing the cost of data management. Data sharing is a effective model to decrease the cost of individuals or companies in dealing with data. However, the existing data sharing scheme cannot reduce communication cost under ensuring the security of users. In this paper, an anonymous and traceable data sharing scheme is presented. The proposed scheme can protect the privacy of the user. In addition, the proposed scheme also can trace the user uploading irrelevant information. Security and performance analyses show that the data sharing scheme is secure and effective.

Consider an undirected multigraph G=(V,E) with n vertices and m edges, and let Ni denote the number of connected spanning subgraphs with i(min) edges in G. Recently, we showed in [3] the validity of (m-i+1)Ni-1>Ni for a simple graph and each i(min). Note that, from this inequality, 2 is easily derived. In this paper, for a multigraph G and all i(min), we prove (m-i+1)Ni-1(i-n+2)Ni, and give a necessary and sufficient condition by which (m-i+1)Ni-1=(i-n+2)Ni. In particular, this means that (m-i+1)Ni-1>Ni is not valid for all multigraphs, in general. Furthermore, we prove 2, which is not straightforwardly derived from (m-i+1)Ni-1(i-n+2)Ni, and also introduce a necessary and sufficent condition by which =2. Moreover, we show a sufficient condition for a multigraph to have Nn2>Nn-1Nn+1. As special cases of the sufficient condition, we show that if G contains at least +1 multiple edges between some pair of vertices, or if its underlying simple graph has no cycle with length more than 4, then Nn2>Nn-1Nn+1.

Association rule mining is a powerful data mining tool, and it can be used to discover unknown patterns from large volumes of data. However, people often have to face the risk of disclosing sensitive information when data is shared with different organizations. The association rule mining techniques may be improperly used to find sensitive patterns which the owner is unwilling to disclose. One of the great challenges in association rule mining is how to protect the confidentiality of sensitive patterns when data is released. Association rule hiding refers to sanitize a database so that certain sensitive association rules cannot be mined out in the released database. In this study, we proposed a new method which hides sensitive rules by removing some items in a database to reduce the support or confidence levels of sensitive rules below specified thresholds. Based on the information of positive border rules and negative border rules contained in transactions, the proposed method chooses suitable candidates for modification aimed at reducing the side effects and the data distortion degree. Comparative experiments on real datasets and synthetic datasets demonstrate that the proposed method can hide sensitive rules with much fewer side effects and database modifications.

Selected mapping (SLM) is a promising distortionless technique for controlling the high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) systems. In SLM, phase sequence selection plays an important role for efficient PAPR reduction. Although some phase sequence sets have been proposed in past studies, we show that an optimal selection is still desired for the phase sequences from the same set. Therefore, this letter develops a measureable phase sequence selection criterion to optimally select the phase sequences from both the same and different sets, so as to achieve near optimum PAPR reduction performance in SLM-OFDM systems.