Optimal dynamic bandwidth allocation for complex networks

Traffic capacity of one network strongly depends on the link’s bandwidth allocation strategy. In previous bandwidth allocation mechanisms, once one link’s bandwidth is allocated, it will be fixed throughout the overall traffic transmission process. However, the traffic load of every link changes from time to time. In this paper, with finite total bandwidth resource of the network, we propose to dynamically allocate the total bandwidth resource in which each link’s bandwidth is proportional to the queue length of the output buffer of the link per time step. With plenty of data packets in the network, the traffic handling ability of all links of the network achieves full utilization. The theoretical analysis and the extensive simulation results on complex networks are consistent. This work is valuable for network service providers to improve network performance or to do reasonable network design efficiently.     

基于交通流量的病毒爆发动力行为研究

给出基于交通流量的病毒扩散易染-感染模型（SI）修正模型,进而利用平均场近似方法,研究基于交通流量的病毒扩散动力学行为.以最小搜索信息路由为例,重新研究了SI模型下扩散系数τ与平均发包率λ、感染率β以及实际介数之间的关系.理论分析与实验结果均表明,当网络拓扑和路由策略一定时,扩散参数τ反比于平均发包率λ和感染率β,是λ=1时的扩散参数τ_λ=1的1/λ.     

Emergence of a phase transition for the required amount of storage in highly renewable electricity systems

Due to global environmental concerns, our electricity supply will transform from mostly conventional power generation to mostly fluctuating renewable power generation. The transition will require combined backup from conventional sources and storage. A phase transition emerges during the ramp-up of the required amount of storage, with renewable penetration being the control parameter and average relative storage filling level being the order parameter. A singularity appears for the required storage energy capacity at a renewable penetration determined by the parameters of the storage. For an ideal storage with no roundtrip losses the transition occurs at 100% renewable penetration. Moreover, the required storage energy capacity is strongly enhanced by temporal correlations on the synoptic weather time scale. A Markov process is proposed, which reproduces these findings.     

Variable disparity estimation based intermediate view reconstruction in dynamic flow allocation over EPON-based access networks

In this paper, a variable disparity estimation (VDE)-based intermediate view reconstruction (IVR) in dynamic flow allocation (DFA) over an Ethernet passive optical network (EPON)-based access network is proposed. In the proposed system, the stereoscopic images are estimated by a variable block-matching algorithm (VBMA), and they are transmitted to the receiver through DFA over EPON. This scheme improves a priority-based access network by converting it to a flow-based access network with a new access mechanism and scheduling algorithm, and then 16-view images are synthesized by the IVR using VDE. Some experimental results indicate that the proposed system improves the peak-signal-to-noise ratio (PSNR) to as high as 4.86 dB and reduces the processing time to 3.52 s. Additionally, the network service provider can provide upper limits of transmission delays by the flow. The modeling and simulation results, including mathematical analyses, from this scheme are also provided.     

Optimal flux patterns in cellular metabolic networks

The availability of whole-cell-level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate the metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30,000 random cellular environments. The distribution of reaction fluxes is heavy tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations has relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reactions are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central carbon metabolic pathways for the sample of random environments.     

Full-duplex communication for future wireless networks: Dynamic resource block allocation approach

In this paper, we discuss a full-duplex (FD) communication scenario, where multiple FD user equipments (UEs) share same spectrum resources (or resource blocks) simultaneously. The FD eNodeB deploys digital precoding and successive interference cancellation with optimal ordering algorithm, to allow coexistence of multiple UEs in downlink and uplink, respectively. The sharing of same resource blocks, results in co-channel interference (CCI), in downlink of a UE, from uplink signals of other UEs. To mitigate the interference, a smart antenna approach is adopted. The approach includes using multiple antennas at UEs to form directed beams towards eNodeB and nulls towards other UEs. However, the approach fails when the UEs due to their mobility align themselves in the same direction with respect to the eNodeB (eNB). In this paper, we propose a dynamic resource block allocation (DRBA) algorithm for avoiding CCI due to mobility of UEs, sharing the spectrum resource, in a FD communication scenario. The proposed algorithm shows significant improvement of the quality of service (QoS) of the communication links.     

Numerical study of the heat transfer and electro-thermo-convective flow patterns in dielectric liquid layer subjected to unipolar injection

In this article we study the electro-thermal convection in a dielectric liquid layer placed between two electrodes and subjected to the simultaneous action of an electric field and a thermal gradient. The full set of equations describing the electro-thermo-convective phenomena is directly solved using a finite volume method. We first heat the liquid from below at time t = 0, wait for the thermal steady state and then inject the electric charges by applying the electric potential. The development of the electro-convective motion is analysed in detail in two cases: 1) strong injection from the lower electrode, 2) strong injection from the upper one. We also study the heat transfer enhancement due to electro-convection. The evolution in time of the Nusselt number Nu for different combinations of the two usual non-dimensional parameters associated to the electro-thermo-convection phenomena (Rayleigh number Ra and the electrical parameter T) is also given and analysed.     

Pair-correlated patterns in Hopfield model of neural networks

We study the retrieval properties of the Hopfield model of neural networks when the memorized patterns are statistically correlated in pairs. There is a finite correlationk between the memories of each pair, but memories of different pairs are uncorrelated. The analysis is restricted to the case of an arbitrary but finite number of memories in the thermodynamic limit. We find that there are two retrieval regimes: for 0<T<(1–k) the system recognizes the stored patterns and for (1–k)<T<(1+k) the system is able to recognize pairs, but it is not able to distinguish between its two patterns.     

Delay-induced cluster patterns in coupled Cayley tree networks

We study effects of delay in diffusively coupled logistic maps on the Cayley tree networks. We find that smaller coupling values exhibit sensitiveness to value of delay, and lead to different cluster patterns of self-organized and driven types. Whereas larger coupling strengths exhibit robustness against change in delay values, and lead to stable driven clusters comprising nodes from last generation of the Cayley tree. Furthermore, introduction of delay exhibits suppression as well as enhancement of synchronization depending upon coupling strength values. To the end we discuss the importance of results to understand conflicts and cooperations observed in family business.     

Multi-channel sensing and resource allocation in energy constrained cognitive radio networks

We consider a cognitive radio network in a multi-channel licensed environment. Secondary user transmits in a channel if the channel is sensed to be vacant. This results in a tradeoff between sensing time and transmission time. When secondary users are energy constrained, energy available for transmission is less if more energy is used in sensing. This gives rise to an energy tradeoff. For multiple primary channels, secondary users must decide appropriate sensing time and transmission power in each channel to maximize average aggregate-bit throughput in each frame duration while ensuring quality-of-service of primary users. Considering time and energy as limited resources, we formulate this problem as a resource allocation problem. Initially a single secondary user scenario is considered and solution is obtained using decomposition and alternating optimization techniques. Later we extend the analysis for the case of multiple secondary users. Simulation results are presented to study effect of channel occupancy, fading and energy availability on performance of proposed method.     

Topological patterns in street networks of self-organized urban settlements

Many urban settlements result from a spatially distributed, decentralized building process. Here we analyze the topological patterns of organization of a large collection of such settlements using the approach of complex networks. The global efficiency (based on the inverse of shortest-path lengths), robustness to disconnections and cost (in terms of length) of these graphs is studied and their possible origins analyzed. A wide range of patterns is found, from tree-like settlements (highly vulnerable to random failures) to meshed urban patterns. The latter are shown to be more robust and efficient.     

Forecasting the daily electricity consumption in the Moscow region using artificial neural networks

In [1] we demonstrated the possibility in principle for short-term forecasting of daily volumes of passenger traffic in the Moscow metro with the help of artificial neural networks. During training and predicting, a set of the factors that affect the daily passenger traffic in the subway is passed to the input of the neural network. One of these factors is the daily power consumption in the Moscow region. Therefore, to predict the volume of the passenger traffic in the subway, we must first to solve the problem of forecasting the daily energy consumption in the Moscow region.     

Phase-synchronisation in continuous flow models of production networks

To improve their position at the market, many companies concentrate on their core competences and hence cooperate with suppliers and distributors. Thus, between many independent companies strong linkages develop and production and logistics networks emerge. These networks are characterised by permanently increasing complexity, and are nowadays forced to adapt to dynamically changing markets. This factor complicates an enterprise-spreading production planning and control enormously. Therefore, a continuous flow model for production networks will be derived regarding these special logistic problems. Furthermore, phase-synchronisation effects will be presented and their dependencies to the set of network parameters will be investigated.     

Binary power allocation for cognitive radio networks with centralized and distributed user selection strategies

Motivated by the desire for efficient spectral utilization, we present a novel algorithm based on binary power allocation for sum rate maximization in Cognitive Radio Networks (CRN). At the core lies the idea of combining multi-user diversity gains with spectral sharing techniques and consequently maximizing the secondary user sum rate while maintaining a guaranteed quality of service (QoS) to the primary system. We consider a cognitive radio network consisting of multiple secondary transmitters and receivers communicating simultaneously in the presence of the primary system. Our analysis treats both uplink and downlink scenarios. We first present a distributed power allocation algorithm that attempts to maximize the throughput of the CRN. The algorithm is simple to implement, since a secondary user can decide to either transmit data or stay silent over the channel coherence time depending on a specified threshold, without affecting the primary users’ QoS. We then address the problem of user selection strategy in the context of CRN. Both centralized and distributed solutions are presented. Simulation results carried out based on a realistic network setting show promising results.     

An optical MEMS-based dynamic capacity allocation scheme for handoff using moving extended cells in radio-over-fiber networks

We demonstrate a Dynamic Capacity Allocation scheme for the realization of Moving Extended Cells in Radio-over-Fiber networks. Our scheme exploits a reconfigurable MEMS-based Central Office architecture for routing of and switching between six data carrying wavelengths, forming in this way cell clusters that transmit the same information content and can move with the mobile user. Error free transmission through 25 km SMF is presented for a 5-cell MEC structure, with each optical channel carrying 500 Mb/s data subcarrier-modulated at 8.5 GHz.     

Improving robustness of complex networks by a new capacity allocation strategy

The robustness of infrastructure networks has attracted great attention in recent years. Scholars have studied the robustness of complex networks against cascading failures from different aspects. In this paper, a new capacity allocation strategy is proposed to reduce cascading failures and improve network robustness without changing the network structure.Compared with the typical strategy proposed in Motter–Lai(ML) model, the new strategy can reduce the scale of cascading failure. The new strategy applied in scale-free network is more efficient. In addition, to reasonably evaluate the two strategies, we introduce contribution rate of unit capacity to network robustness as evaluation index. Results show that our new strategy works well, and it is more advantageous in the rational utilization of capacity in scale-free networks.Furthermore, we were surprised to find that the efficient utilization of capacity costs declined as costs rose above a certain threshold, which indicates that it is not wise to restrain cascading failures by increasing capacity costs indefinitely.     

Separable State Decompositions for a Class of Mixed States

We study certain quantum states for which the PPT criterion is both sufficient and necessary for separability. A class of n×n bipartite mixed states is presented and the conditions of PPT for these states are derived. The separable pure state decompositions of these states are explicitly constructed when they are PPT.