The overwhelming data rates in next generation wireless networks impose a burden on the high-capacity network planning. One promising strategy to meet the demand for high-capacity communications is to augment radio frequency (RF) based multi-channel multi-radio (MCMR) wireless mesh network (WMN) by free-space optics (FSO). In this paper, we construct a hybrid RF/FSO MCMR WMN topology and address its resource allocation (RA) problem in terms of interface assignment, channel allocation, routing, FSO link allocation, and topology control. Considering the weather effects on FSO link availability and the fading nature of RF links, the RA problem is formulated as a two-stage optimization problem with the objective of maximizing the network throughput. In our optimization model, we formulate each stage as a mixed integer linear program, and the bottleneck RF links are gradually upgraded by FSO links. To avoid the computational complexity of the second stage optimization, an improved iterated local search algorithm is proposed. Simulation results show that our RA scheme is efficient and the throughput can be enhanced dramatically by proper FSO link augmentation. INDEX TERMSResource allocation, multi-channel multi-radio, hybrid RF/FSO networks, wireless mesh networks, mixed integer linear program.

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Currently, the optical components of a camera embedded in the device constrain its overall thickness. Moreover, if the camera is strongly shaken, the lens and sensor may be misaligned, resulting in a defocusing effect. In this paper, we propose a novel lensless-camera communication model, which removes the lens of camera, therefore decreasing the overall thickness of the device without affecting communications. To decode the images captured by the lensless camera, a decoding algorithm aided by back propagation (BP) neural network was designed, which recognizes the blurred image patterns efficiently. To adapt to time-varying environments, an adaptive training sequence adjustment mechanism was designed. Simulation results show that the proposed image decoding algorithm presents a good bit-error-rate (BER) performance. The proposed system has robust movements and provides resilience to interference, benefiting from the neural network and the designed algorithm.

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