In this letter, we investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in device-todevice (D2D) communications underlaying cellular networks with uplink channel reuse. The resource allocation problem is modeled as a noncooperative game, in which each user equipment (UE) is self-interested and wants to maximize its own EE. Given the SE requirement and maximum transmission power constraints, a distributed energy-efficient resource allocation algorithm is proposed by exploiting the properties of the nonlinear fractional programming. The relationships between the EE and SE tradeoff of the proposed algorithm and system parameters are analyzed and verified through computer simulations. Index Terms-EE and SE tradeoff, D2D communication, noncooperative game, nonlinear fractional programming.

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In multi-objective evolutionary algorithms (MOEAs), the diversity of Pareto front (PF) is significant. For good diversity can provide more reasonable choices to decision-makers. The diversity of PF includes the span and the uniformity. In this paper, we proposed a dynamic crowding distance (DCD) based diversity maintenance strategy (DMS) (DCD-DMS), in which individual's DCD are computed based on the difference degree between the crowding distances of different objectives. The proposed strategy computes individuals' DCD dynamically during the process of population maintenance. Through experiments on 9 test problems, the results demonstrate that DCD can improve diversity at a high level compared with two popular MOEAs: NSGA-II and ε-MOEA.

Abstract-The pole-sensitivity approach is employed to investigate the stability issue of the discrete-time control system, where a digital controller, implemented with finite word length (FWL), is used. A new stability related measure is derived, which is more accurate in estimating the closed-loop stability robustness of an FWL implemented controller than some existing measures for the pole-sensitivity analysis. This improved stability measure thus provides a better criterion to find the optimal realizations for a generic controller structure that includes output-feedback and observer-based controllers. A numerical example is used to verify the theoretical analysis and to illustrate the design procedure.Index Terms-Closed-loop stability, digital controller, finite word length, optimization.

Abstract-The introduction of device-to-device (D2D) into cellular networks poses many new challenges in the resource allocation design due to the co-channel interference caused by spectrum reuse and limited battery life of user equipments (UEs). In this paper, we propose a distributed interference-aware energy-efficient resource allocation algorithm to maximize each UE's energy efficiency (EE) subject to its specific quality of service (QoS) and maximum transmission power constraints. We model the resource allocation problem as a noncooperative game, in which each player is self-interested and wants to maximize its own EE. The formulated EE maximization problem is a nonconvex problem and is transformed into a convex optimization problem by exploiting the properties of the nonlinear fractional programming. An iterative optimization algorithm is proposed and verified through computer simulations.

In this work we focus on iterative learning control (ILC) for iteratively varying reference trajectories which are described by a high-order internal models (HOIM) that can be formulated as a polynomials between two consecutive iterations. The classical ILC with iteratively invariant reference trajectories, on the other hand, is a special case of HOIM where the polynomial renders to a first-order internal model with a unity coefficient. By incorporating HOIM into the ILC law, and designing appropriate learning control gains, the learning convergence in the iteration axis can be guaranteed for continuous-time linear time-varying (LTV) systems. The initial resetting condition, P-type and D-type ILC, and possible extension to nonlinear cases are also explored in this work.

In this paper, we base our research by dealing with a real-world problem in an enterprise. A RFM (recency, frequency, and monetary) model and K-means clustering algorithm are utilized to conduct customer segmentation and value analysis by using online sales data. Customers are classified into four groups based on their purchase behaviors. On this basis, different CRM (customer relationship management) strategies are brought forward to gain a high level of customer satisfaction. The effectiveness of our method proposed in this paper is supported by improvement results of some key performance indices such as the growth of active customers, total purchase volume, and the total consumption amount.

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