Seasonal ARIMA model is a good traffic model capable of capturing the behavior of a network traffic stream. In this paper, we give a general expression of seasonal ARIMA models with two periodicities and provide procedures to model and to predict traffic using seasonal ARIMA models. The experiments conducted in our feasibility study showed that seasonal ARIMA models can be used to model and predict actual wireless traffic such as GSM traffic in China.

Spectrum sharing effectively improves the spectrum usage by allowing secondary users (SUs) to dynamically and opportunistically share the licensed bands with primary users (PUs). The concept of cooperative spectrum sharing allows SUs to use portions of the PUs' radio resource for their own data transmission, under the condition that SUs help the PUs' transmission. The key issue with designing such a scheme is how to deal with the resource splitting of the network. In this paper we propose a relay-based cooperative spectrum sharing scheme in which the network consists of one PU and multiple SUs. The PU asks the SUs to relay its data in order to improve its energy efficiency, in return it rewards the SUs with a portion of its authorized spectrum. However each SU is only allowed to transmit its data via the rewarded channel at a power level proportional to the contribution it makes to the PU. Since energy cost is considered, the SUs must carefully determine their power level. This scheme forms a non-cooperative Stackelberg resource allocation game where the strategy of PU is the bandwidth it rewards and the strategy of each SU is power level of relay transmission. We first investigate the second stage of the sub-game which is addressed as power allocation game. We prove there exists an equilibrium in the power allocation game and provide a sufficient condition for the uniqueness of the equilibrium. We further prove a unique Stackelberg equilibrium exists in the resource allocation game. Distributed algorithms are proposed to help the users with incomplete information achieve the equilibrium point. Simulation results validate our analysis and show that our proposed scheme introduces significant utility improvement for both PU and SUs.

The development of efficient quality of service (QoS) routing algorithms in a high-speed network environment is a very important and at the same time very difficult task due to the need to provide divergent services with multiple QoS requirements. Recently heuristic algorithms based on Lagrange relaxation techniques have been proposed to resolve the contradiction between the time complexity and the quality of solution. In this paper, we investigate the performance of two heuristic algorithms, LR_DCLC and NR_DCLC, for the delay-constrained least-cost (DCLC) routing problem. Algorithm LR_DCLC is based on linear relaxation, while algorithm NR_DCLC, which is proposed in this paper, is based on nonlinear relaxation. A large number of simulations demonstrate that even though both algorithms have very good performance, NR_DCLC can obtain much better solutions than LR_DCLC by running Dijkstra's algorithm on average a few more times, especially in the case when the optimal solutions are hard to find.

The modern network service of finding the optimal path subject to multiple constraints on performance metrics such as delay, jitter, loss probability, etc. gives rise to the multi-constrained optimal-path (MCOP) QoS routing problem, which is NP-complete. In this paper, this problem is solved through both exact and heuristic algorithms. We propose an exact algorithm E_MCOP, which first constructs an aggregate weight and then uses a K-shortest-path algorithm to find the optimal solution. By means of E_MCOP, the performance of the heuristic algorithm H_MCOP proposed by Korkmaz et al. in a recent work is evaluated. H_MCOP only runs Dijkstra's algorithm (with slight modifications) twice, but it can find feasible paths with a success ratio very close to that of the exact algorithm. However, we notice that in certain cases its feasible solution has an unsatisfactorily high average cost deviation from the corresponding optimal solution. For this reason, we propose some modified algorithms based on H_MCOP that can significantly improve the performance by running Dijkstra's algorithm a few more times. The performance of the exact algorithm and heuristics is investigated through computer simulations on networks of various sizes.

A conical area evolutionary algorithm (CAEA) is presented to further improve computational efficiencies of evolutionary algorithms for bi-objective optimization. CAEA partitions the objective space into a number of conical subregions and then solves a scalar subproblem in each subregion that uses a conical area indicator as its scalar objective. The local Pareto optimality of the solution with the minimal conical area in each subregion is proved. Experimental results on bi-objective problems have shown that CAEA offers a significantly higher computational efficiency than the multi-objective evolutionary algorithm based on decomposition (MOEA/D) while CAEA competes well with MOEA/D in terms of solution quality.

Active Reliable Multicast (ARM) is a novel loss recovery scheme for large-scale reliable multicast that employs active routers to protect the sender and network bandwidth from unnecessary feedback and repair traffic. Active routers perform NACKs suppression, cache multicast data for local loss recovery, and use scoped retransmission to avoid exposure. Limited active resources at routers need to be optimized to achieve low loss recovery latency and/or high network throughput. In this paper, we study the cache placement strategies and caching policies for ARM. Several heuristics, namely uniform allocation, proportional allocation, max-min fair share and weighted allocation for cache allocation methods are proposed. To further improve the loss recovery performance, caching policies can be employed in conjunction with the cache allocation strategies. Several caching policies, namely complete caching, random caching and deterministic caching, are proposed. Extensive simulation experiments are conducted to evaluate and compare the performance of the proposed strategies and policies. Numerical results reveal that significant performance gains can be achieved when a proper cache placement strategy and a caching policy are used for a given available cache resource. Another interesting finding is that the contributions of the cache placement scheme and caching policy to the recovery latency performance are roughly independent. The obtained insights in this study will provide some design guidelines for optimal active resource allocation and caching polices for reliable multicast communications.

Error-propagation is an important issue and should be carefully coped with in the decision-feedback equalizers (DFE). Ignoring the impact of error-propagation often leads to impractical laboratory results. In this paper, we investigate two novel layered space-frequency equalizers (LSFE) for single-carrier multiple-input multiple-output (MIMO) systems, where the recently proposed frequency-domain equalizer with time domain noise-predictor (FDE-NP) is adopted at each stage of the LSFE. We first derive the partially-connected LSFE with noise predictor (PC-LSFE-NP) which has exactly the same mean square error (MSE) as the conventional LSFE under the assumption of perfect feedback. However, if error-propagation is considered, the proposed PC-LSFE-NP can achieve better performance than the conventional LSFE due to the more reliable feedback output by the decoders. To reduce the interference from the not yet detected layers in the feedback section, we then introduce the fully-connected LSFE with noise predictor (FC-LSFE-NP), in which all layers are implicitly equalized within each stage and their decisions fed back internally. The powerful feedback filter of FC-LSFE-NP brings significant performance superiority over the conventional LSFE and PC-LSFE-NP with either perfect or imperfect feedback. Moreover, we propose a simple soft-demapper for the equalizers to avoid information loss during decoding, and thus, further improve the performance. Finally, we compare the performance of (PC/FC)-LSFE-NP with the existing schemes by computer simulations.

In the future asynchronous transfer mode (ATM) networks, an efficient virtual path (VP) control strategy must be applied to guarantee the network has high throughput with tolerable node processing load. The multistage VP control may be the best candidate since the tasks in this method are shared by the central node and local nodes, and it allows us to track the traffic changes while maintain a good state of the VP topology by reconfiguring it at regular or need based intervals. In this paper, we focus on the VP topology optimization problem in the multistage VP control. We first present the problem formulation in which the tradeoff between the network throughput and processing costs is considered, and then employ an algorithm based on a route-neuron Hopfield neural network (HNN) model to solve this problem. The numerical results demonstrate the HNN can converge to optimal solutions with high probability and stability while in other cases to near optimal solutions if the values of the system parameters in the route-neuron model are chosen according to some empirical formulas provided in this paper.

In this paper, we study the power allocation problem in a relay assisted multi-band underlay cognitive radio network. Such a network allows unlicensed users (secondary users) to access the spectrum bands under a transmission power constraint. Due to the concave increasing property of logarithm function, it is not always wise for secondary users to expend all the transmission power in one band if their aim is to maximize achievable data rate. In particular, we study a scenario where two secondary users and a half-duplexing relay exist with two available bands. The two users choose different bands for direct data transmission and use the other band for relay transmission. By properly allocating the power on two bands, each user may be able to increase its total achievable data rate while satisfying the power constraint. We formulate the power allocation problem as a non-cooperative game and investigate its Nash equilibria. We prove the power allocation game is a supermodular game and that Nash equilibria exist. We further find the best response function of users and propose a best response update algorithm to solve the corresponding dynamic game. Numerical results show the overall performance in terms of achievable rates is improved through our proposed transmission scheme and power allocation algorithm. Our proposed algorithm also shows satisfactory performance in terms of convergence speed.

The predictability of network traffic is an important and widely studied topic because it can lead to the solutions to get more efficient dynamic bandwidth allocation, admission control, congestion control and better performance wireless networks. Support vector machine (SVM) is a novel type of learning machine based on statistical learning theory, can solve small-sample learning problems. The work presented in this paper aims to examine the feasibility of applying SVM to predict actual WLAN traffic. We study one-step-ahead prediction and multi-step-ahead prediction without any assumption on the statistical property of actual WLAN traffic. We also evaluate the performance of different prediction models such as ARIMA, FARIMA, artificial neural network, and wavelet-based model using three actual WLAN traffic. The results show that the SVM-based model for predicting WLAN traffic is reasonable and feasible and has the best performance among the above mentioned prediction models.

In this paper, we investigate the degrees of freedom (DoF) of a MIMO cellular interfering network (CIN) with L (L≥3) cells and K users per cell. Previous works established the DoF upper bound of LK(M+N)/(LK+1) for the MIMO CIN by analyzing the interference alignment (IA) feasibility, where M and N denote the number of antennas at each base station (BS) and each user, respectively. However, there is still a gap between the DoF upper bound and the achievable DoF in existing designs. To address this problem, we propose two linear IA schemes without symbol extensions to jointly design transmit and receive beamforming matrices to align and eliminate interference. In the two schemes, the transmit beamforming vectors are allocated to different cluster structures so that the inter-cell interference (ICI) data streams from different ICI channels are aligned. The first scheme, named fixed cluster structure (FCS-IA) scheme, allocates ICI beamforming vectors to the cluster structures of fixed dimension and can achieve the DoF upper bound under some system configurations. The second scheme, named dynamic cluster structure IA (DCS-IA) scheme, allocates ICI beamforming vectors to the cluster structures of dynamic dimension and can get a tradeoff between the number of antennas at BSs and users so that ICI alignment can be applied under various system configurations. Through theoretical analysis and numerical simulations, we verify that the DoF upper bound can be achieved by using the FCS-IA scheme. Furthermore, we show that the proposed schemes can provide significant performance gain over the time division multiple access (TDMA) scheme in terms of DoF. From the perspective of DoF, it is shown that the proposed schemes are more effective than the conventional IA schemes for the MIMO CIN.

In this paper, a discrete-time convergence theorem for continuous-state Hopfield networks with self-interaction neurons is proposed. This theorem differs from the previous work by Wang in that the original updating rule is maintained while the network is still guaranteed to monotonically decrease to a stable state. The relationship between the parameters in a typical class of energy functions is also investigated, and consequently a "guided trial-and-error" technique is proposed to determine the parameter values. The third problem discussed in this paper is the post-processing of outputs, which turns out to be rather important even though it never attracts enough attention. The effectiveness of all the theorems and post-processing methods proposed in this paper is demonstrated by a large number of computer simulations on the assignment problem and the N-queen problem of different sizes.

A single-carrier multiple-input multiple-output (MIMO) system with frequency-selective channels suffers from the inter-symbol interference (ISI) and the co-channel interference (CCI). To eliminate both type of interference, we propose in this letter a hybrid two-stage decision-feedback equalizer (HTS-DFE), which performs the frequency-domain equalization (FDE) in the first stage and the layered serial interference-cancellation (SIC) in the second stage. Since the decision-feedback (DF) or noise-prediction (NP) architecture can be employed in FDE or SIC, the proposed equalizer actually can have four variations that achieve the same mean square error (MSE) under the assumption of perfect feedback. Further, we combine HTS-DFE with the decoded decision-feedback (DDF) scheme to mitigate the error-propagation encountered in the practice. Simulation results confirm that the proposed HTS-DFE can outperform the existing equalizers significantly.

In this letter, we propose a novel frequency-domain equalizer (FDE) for single-carrier systems characterized by severe inter-symbol interference (ISI) channels; it consists of a linear FDE and an iterative block noise-predictor (IBNP). Unlike the FDE with time-domain noise predictor (FDE-NP), the proposed scheme allows the feedback equalizer being an uncausal filter, and performs the noise prediction in an iterative manner. For this reason, FDE-IBNP can remove both precursor and postcursor ISI, and alleviate the impact of error-propagation. Besides, our scheme has lower computational complexity than the present iterative block equalizers.

In this paper, we present a new network design problem that is applicable for designing virtual paths (VP's) in an asynchronous transfer mode (ATM) network to efficiently support multicast applications, especially real-time multimedia applications. We first address several alternatives for the solution and compare their properties. Then we focus on a new solution which sets up a semi-permanent VP layout (VPL) and constructs VC trees for different multicast traffic demand patterns based on the constructed VPL. A three-phase heuristic solution is proposed for designing a good virtual-path layout for a given set of multicast traffic demand patterns. By varying the design parameters, we can obtain different VPLs which possess different tradeoffs among some important criteria, namely, the network overhead for a connection setup, routing table resources and control and management cost. Simulations are performed on randomly generated networks to demonstrate the performance and scalability of our solution. To the best of our knowledge, there is no prior known work which takes the multicast connection traffic into account for the VP layout design.

Functional and formal verification are important methodologies for complex mixed-signal design validation. However the industry is still verifying such systems by pure simulation. This process lacks on error localization and formal verifications methods. This is the existing verification gap between the analog and digital blocks within a mixed-signal system. Our approach improves the verification process by creating temporal properties named mixed-signal assertions which are described by a combination of digital assertions and analog properties. The proposed method is a new assertion-based verification flow for designing mixed-signal circuits. The effectiveness of the approach is demonstrated on a Σ/Δ-converter.

The problem of finding a path with two additive constraints, in particular finding a path that satisfies both the cost and the delay constraints, is called multi-constrained path (MCP) problem in the literature. In this paper, we explore the MCP problem based on the idea of single mixed weight --a mixed weight for each link is first obtained by combining its delay and cost, and then Dijkstra's algorithm is used to find the corresponding shortest path. Given two infeasible paths, it can be theoretically proved that a better path can possibly be found if we choose an appropriate parameter to construct the mixed weight. An approximate algorithm is thus proposed to solve the MCP problem. Theoretical analysis demonstrates that this algorithm can make a correct judgment whether there is a feasible path or not with a very high probability even in the strict case where the delay bound is between the delays of the least delay path and the least cost path, while the cost bound is between the costs of the two paths. On the other hand, the time complexity of this algorithm is very small since it only needs to execute Dijkstra's algorithm a limited number of times. The excellent performance of the proposed algorithm is verified by a large number of experiments on networks of different sizes.

In this paper, we study the degrees of freedom (DoF) of a multiple-input multiple-output (MIMO) multiway relay channel (mRC) with two relays, two clusters and K (K≥3) users per cluster. We consider a clustered full data exchange model, i.e., each user in a cluster sends a multicast (common) message to all other users in the same cluster and desires to acquire all messages from them. The DoF results of the mRC with the single relay have been reported. However, the DoF achievability of the mRC with multiple relays is still an open problem. Furthermore, we consider a more practical scenario where no channel state information at the transmitter (CSIT) is available to each user. We first give a DoF cut-set upper bound of the considered mRC. Then, we propose a distributed interference neutralization and retransmission scheme (DINR) to approach the DoF cut-set upper bound. In the absence of user cooperation, this method focuses on the beamforming matrix design at each relay. By investigating channel state information (CSI) acquisition, we show that the DINR scheme can be performed by distributed processing. Theoretical analyses and numerical simulations show that the DoF cut-set upper bound can be attained by the DINR scheme. It is shown that the DINR scheme can provide significant DoF gain over the conventional time division multiple access (TDMA) scheme. In addition, we show that the DINR scheme is superior to the existing single relay schemes for the considered mRC.

Field Programmable Gate Array (FPGA) implementation of Elliptic Curve Cryptography (ECC) over GF(p) is commonly not fast enough to meet the request of high-performance applications. There are three critical factors to determine the performance of ECC processor over GF(p): multiplication structure, modular multiplication algorithm, and scalar point multiplication scheduling. This work proposes a novel multiplication structure which is a two-stage pipeline on the basis of Karatsuba-Ofman algorithm. With the proposed multiplication structure, we design a 256-bit modular multiplier based on Improved Barret Modular Multiplication algorithm. Upon the modular multiplier, we finish the scalar point multiplication scheduling and implement a high-performance ECC processor on FPGA. Compared with the previous modular multipliers, our modular multiplier reduces the 256-bit modular multiplication time by 28% at least. Synthesis result on Altera Stratix II shows that our ECC processor can complete a 256-bit ECC scalar point multiplication in 0.51ms, which is at least 1.3 times faster than the currently reported FPGA ECC processors over GF(p).

In maritime networks, the communication links are characterized as high dynamics due to ship mobility and fluctuation of the sea surface. The performance of traditional transmission protocols is poor in maritime networks. Thus, some researchers have considered using Delay Tolerant Network (DTN) to improve the performance of data transmission in maritime environment. Most existing work on maritime DTNs uses simulation to evaluate the transmission performance in maritime DTNs. In this paper, we develop a theoretical model to analyze the performance of data transmission in maritime DTNs. We first construct a model to describe the ship encounter probability. Then, we use this model to analyze the data delivery ratio from ships in the seaway to the base station (BS) on the coast. Based on the data of tracing the ships navigating in a realistic seaway, we develop a simulator and validate the theoretical models. In addition, by comparing the performance of DTN transmission protocol and traditional end-to-end transmission protocol, we confirm that DTN protocol can effectively improve the performance of data transmission in maritime networks.