Optimal Power Allocation for Two-Way Decode-and-Forward OFDM Relay Networks

This paper presents a novel two-way decode-and-forward (DF) relay strategy for Orthogonal Frequency Division Multiplexing (OFDM) relay networks. This DF relay strategy employs multi-subcarrier joint channel coding to leverage frequency selective fading, and thus can achieve a higher data rate than the conventional per-subcarrier DF relay strategies. We further propose a low-complexity, optimal power allocation strategy to maximize the data rate of the proposed relay strategy. Simulation results suggest that our strategy obtains a substantial gain over the per-subcarrier DF relay strategies, and also outperforms the amplify-and-forward (AF) relay strategy in a wide signal-to-noise-ratio (SNR) region.Comment: 5 pages, 2 figures, accepted by IEEE ICC 201

A High-Accuracy Nonintrusive Networking Testbed for Wireless Sensor Networks

It becomes increasingly important to obtain the accurate and spontaneous runtime network behavior for further studies onwireless sensor networks. However, the existing testbeds cannot appropriately match such requirements. A High-accuracyNonintrusive Networking Testbed (HINT) is proposed. In HINT, the interconnected chip-level signals are passively captured withauxiliary test boards and the captured data are transferred in additional networks to test server. The test server of HINT collects allthe test data and depicts the full network behavior. HINT supports networking test, protocol verification, performance evaluationand so on. The experiments show that HINT transparently gathers accurate runtime data and does not disturb the spontaneousbehavior of sensor networks. HINT is also extendible to different hardware platforms of sensor nodes. Consequently, HINT isan upstanding testbed solution for the future fine-grained and experimental studies on the resource-constrained wireless sensornetworks

Ambient Data Collection with Wireless Sensor Networks

One of the most important applications for wireless sensor networks (WSNs) is Data Collection, where sensing data arecollected at sensor nodes and forwarded to a central base station for further processing. Since using battery powers and wirelesscommunications, sensor nodes can be very small and easily attached at specified locations without disturbing surroundingenvironments. This makes WSN a competitive approach for data collection comparing with its wired counterpart. In this paper,we review recent advances in this research area. We first highlight the special features of data collection WSNs, by comparingwith wired data collection network and other WSN applications. With these features in mind, we then discuss issues and priorsolutions on the data gathering protocol design. Our discussion also covers different approaches for message dissemination, whichis a critical component for network control and management and greatly affects the overall performance of a data collectionWSNsystem

An Improved K-means Algorithm and Its Application for Assessment of Culture Industry Listed Companies

Owing to K-means algorithm has the shortcoming that it always neglects the influence of cluster size when the Euclidean distances between samples and cluster center is calculated. In order to overcome the lack, the influence of cluster size is introduced into K-means algorithm in this paper. Therefore an improved K-means algorithm based on gravity is proposed, namely GK-means algorithm. The experimental simulation results show that GK-means algorithm has better performance compared with K-means algorithm. So the GK-means algorithm is adopted for assessing the performance of culture industry listed companies in this paper. Furthermore some satisfactory results are also obtained

Event-triggered output consensus for linear multi-agent systems via adaptive distributed observer

summary:This paper investigates the distributed event-triggered cooperative output regulation problem for heterogeneous linear continuous-time multi-agent systems (MASs). To eliminate the requirement of continuous communication among interacting following agents, an event-triggered adaptive distributed observer is skillfully devised. Furthermore, a class of closed-loop estimators is constructed and implemented on each agent such that the triggering times on each agent can be significantly reduced while at the same time the desired control performance can be preserved. Compared with the existing open-loop estimators, the proposed estimators can provide more accurate state estimates during each triggering period. It is further shown that the concerned cooperative output regulation problem can be effectively resolved under the proposed control scheme and the undesirable Zeno behavior can be excluded. Finally, the effectiveness of the proposed results is verified by numerical simulations