A critical issue encountered in implementing a bus signal priority system is to ensure a trade-off between the delay of a bus and the surrounding traffic. Traditional BSP methods treat buses uniformly so that an empty bus may have the same priority to the full loaded bus, and lead to an excessive delay for private traffic. However, with RFID technique, a control model can realize delicate control mode by identifying and acquiring more detailed, real-time and dynamic data on the bus. Hence, An intelligent method for determing bus priority based on RFID is proposed, assuming two-objective optimization of both on-bus passengers' total delay and private vehicles total delay. The result is a considerable reduction in private traffic delay with higher traffic volume when using bus signal priority. What's more, the proposed control method could also be applied to the coordination of similar complex cyber-physical systems.

The overwhelming success of the Internet provides scientists new tools to understand our mother nature. Argos is a worldwide satellite system dedicated to Earth observation and environmental research. Argos system has an excellent track record for data collection, processing and dissemination to the scientific and international community and gains popularity steadily. However, when the number of Argos users exceeds certain limit, the transmission success rate is reduced significantly, which results in the severe dead-station problem. To solve the problem, a randomized transmission scheme has been proposed: stations transmit in their designated repetition rate but with a random deviation. We quantify and compare the probabilities of successful reception during a satellite pass and the system capacity for deterministic, independent, and randomized transmissions. How the random level affects the system performance is evaluated and verified by simulation. The results provide important guidelines for future growth and success of the satellite telemetry system and other sensor networks.

Various wireless networks have emerged and constituted a complex radio access network environment. In cognitive environment, network resources are always varying with time and space. When a mobile cognitive user with a multi-mode terminal generates a call request, how to select the best network? Considering that the traffic load varying with time, we introduce back propagation (BP) neural network to predict its changing trend. At the same time, because parameters obtained through measurement or prediction are imprecise and uncertain, we introduce fuzzy logic to deal with such uncertainty. Then synthesizing technology preference, operator preference and user demand, we propose a multi-attribute decision making (MADM) network strategy based on BP neural network and fuzzy logic. Finally, we evaluate the performance of our strategy through simulations. The results show that our strategy can effectively increase users' utility-price and reduce their dropping probability.

In a B3G/4G wireless communication system, the users will connect to the network using one of several available radio access technologies. In this paper, we proposed a Q-learning based algorithm for terminals' independent access network selection with the aim of improving the resource utilization and providing the best quality of service with respect to the wireless environment status, network performance and user' requirement. In particular, for the first time we introduced the concept of low-carbon as one of the evaluation indicators of wireless communication performance, in order to reduce the power consumption and achieve a balance between quality and consumption. The proposed scheme is based on the concept of cognitive network, which has been proposed recently by the motivation of complexity, heterogeneity and reliability requirements of tomorrow's network and the cognitive pilot channel used in it. The performance of the access network selection algorithm is shown in the simulation and it can be seen that this algorithm significantly reduced the blockrate and power consumption as well as increased the throughput compared with random accessing approach. In future work, we will continue to research on the effective access network selection algorithm and try to introduce the low-carbon indicator to other aspects of the wireless communication system.

The orthogonal frequency division multiplexing-based transform domain communication system (OFDM-TDCS) is a promising candidate for signaling transmission in Cognitive Radio (CR) networks. An important issue of OFDM-TDCS system is the effective signal detection scheme design when channel coding is applied. In this paper, a class of new hard-demodulation (HD) and soft-demodulation (SD) algorithms are proposed in a unified signal detection framework. Although HD is based on the maximum likelihood (ML) estimation while SD is to calculate the log-likelihood ratio (LLR) of each coded bit, both of them are derived from an identical conditional probability density function. To further improve the implementation efficiency of SD detector, a code table is established to facilitate the needed searching operation. Finally, simulations under IEEE 802.22 Profile C channel validate the proposed signal detection detectors in terms of bit error rate (BER).