Study on the mesh mode of IEEE 802.16

[[abstract]]The new Wireless-MAN standard, IEEE 802.16, provides broadband, wide coverage, was one of the most worthy technique. there are two established modes for IEEE 802.16, One is PMP another is Mesh, There are two mechanisms to schedule data transmission in the IEEE 802.16 Mesh networks: centralized and distributed scheduling.In the centralized scheduling scheme, the BS works like the cluster head and determines time slot allocation of each SS. In order to transmit data packets, the SS is required to submit the request packet to the BS via the control channel. The BS grants the access request by sending the slot allocation schedule call UP_MAP to all SS nodes. In the distributed scheduling scheme, if the SS have data to send, it need to compete with it neighbors. So that it can start data transmission. In this paper, for clarity purposes we will focus on a two classes system and use an approximation two-dimensional Markov mode to analyze the system performance, though the analysis approach may be easily extended to general class case

CS-CapsFPN: A Context-Augmentation and Self-Attention Capsule Feature Pyramid Network for Road Network Extraction from Remote Sensing Imagery

The information-accurate road network database is greatly significant and provides essential input to many transportation-related activities. Recently, remote sensing images have been an important data source for assisting rapid road network updating tasks. However, due to the diverse challenging scenarios of roads in remote sensing images, such as occlusions, shadows, material diversities, and topology variations, it is still difficult to realize highly accurate extraction of roads. This paper proposes a novel context-augmentation and self-attention capsule feature pyramid network (CS-CapsFPN) to extract roads from remote sensing images. By designing a capsule feature pyramid network architecture, the proposed CS-CapsFPN can extract and fuze different-level and different-scale high-order capsule features to provide a high-resolution and semantically strong feature representation for predicting the road region maps. By integrating the context-augmentation and self-attention modules, the proposed CS-CapsFPN can exploit multi-scale contextual properties at a high-resolution perspective and emphasize channel-wise informative features to further enhance the feature representation robustness. Quantitative evaluations on two test datasets show that the proposed CS-CapsFPN achieves a competitive performance with a precision, recall, intersection-over-union, and Fscore of 0.9470, 0.9407, 0.8957, and 0.9438, respectively. Comparative studies also confirm the feasibility and superiority of the proposed CS-CapsFPN in road extraction tasks