摘要：
Device-to-Device (D2D) communications and small cell networks, as promising technologies to improve spectral efficiency and system throughput for future cellular networks, have received increasing attentions. In this paper, we model D2D communications in the two-tier heterogeneous macro-small cell networks. We propose not only two novel resource sharing strategies for D2D users, namely dedicated resource sharing strategy and cross-tier resource sharing strategy, but theirs corresponding power control strategies as well. In order to minimize cross-tier interference for the two-tier cellular networks, we formulate the optimization problem of power control and channel allocation, as a convex optimization problem and a 01 assignment problem, respectively. The system-level simulations demonstrate that the proposed schemes significantly increase the throughput compared to cellular-only transmission. Simulation results also reveal the D2D communications can be regarded as an interference mitigation technology for the two-tier heterogeneous networks and present the high potential of D2D communications.
摘要：
Temporal neural networks such as Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM) are popular for their incremental and explicit learning abilities. However, for sub-sequence clustering TKM and RSOM may generate many fragments whose classification membership is hard to decide. Besides they have stability issues in multivariate time series processing because they model the historical neuron activities on each variable independently. To overcome the drawbacks, we propose an adaptive sub-sequence clustering method based on single layered Self-Organizing Incremental Neural Network (SOINN). A recurrent filter is proposed to model the quantizations of neuron activations each as a scalar instead of a vector like in TKM and RSOM. Then it is integrated with the single layered SOINN for adaptive clustering where fragmented clusters in TKM and RSOM is replaced by a smoothed clustering result. Experiments are carried out on two datasets, namely a traffic flow dataset from open Caltrans performance measurement systems and a part of the KDD Cup 99 intrusion detection dataset. Experimental results show that the proposed method outperforms the conventional methods by 21.3% and 9.1% on the two datasets respectively.
会议名称：
27th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
会议时间：
SEP 04-08, 2016
会议地点：
Valencia, SPAIN
会议主办单位：
[Xiao, Zhu;Zhan, Sui;Xiang, Zhiyang;Wang, Dong] Hunan Univ, Colledge Comp Sci & Elect Engn, Changsha, Hunan, Peoples R China.^[Chen, Wenjie] Cent South Univ Forestry & Technol, Business Coll, Changsha, Hunan, Peoples R China.
关键词：
Global Positioning System;Inertial Navigation System;Gauss Process Regression;Particle Swarm Optimization;GPS Outage;Vehicle Localization
摘要：
Land vehicle localization and navigation mainly relies on the Global Positioning System (GPS)/Inertial Navigation System (INS) integration. In this paper, we propose a unified incremental regression framework that enables vehicle localization with high accuracy in urban environment. Within the framework, we propose a nonlinear Gauss Process Regression (GPR) approach to perform vehicle position prediction during GPS outages. By mapping nonlinear data into high-dimensional space by kernel function, the proposed GPR based approach is able to deal with the nonlinearity issue in GPS denied environment. We design a Particle Swarm Optimization (PSO) based algorithm to optimize GPR hyper-parameters, which are tuned with high time efficiency for vehicular position prediction. By real-world road experiments, the proposed method is evaluated against Artificial Neural Network (ANN), Support Vector Machine Regression (SVR) and Partial Least Squares Regression (PLSR). The results reveal that the proposed outperforms the others by 22.8%-65.2% improvement in the positional accuracy.
会议论文集名称：
International Conference on Computer Information and Telecommunication Systems
关键词：
heterogeneous small cell networks;energy efficiency;coverage performance;nodes deployment optimization
摘要：
Various types of small cell base stations (SBSs) are deployed randomly in heterogeneous small cell network to meet the soaring growth in traffic, which leads to more and more complex network topological structure and resource management. In this paper, we analyze the coverage performance and energy efficiency of a two tier heterogeneous network which is composed of micro tier and pico tier. With the aid of stochastic geometry tools and PPP properties, the analytical expression of success probability for each tier is derived to evaluate the tier's coverage performance how to vary with the SBS deployment density. In addition, the energy efficiency optimization question is formulated in terms of the resource allocation fairness proposed in this paper. Base on the analytical results, numerical simulations show that the effects that the SBS deployment density has on the coverage performance and energy efficiency and confirms that there exits an optimal SBS density ratio among the two tier which can be applied to maximize the energy efficiency.
