Shi, Z, Wu, Z, Yin, Z, Yang, Z & Cheng, Q 2018, 'Novel Markov channel predictors for interference alignment in cognitive radio network', Wireless Networks, vol. 24, no. 6, pp. 1915-1925.View/Download from: UTS OPUS or Publisher's site
© 2017, Springer Science+Business Media New York. In cognitive radio (CR) network, how to mitigate interference between different users is a key task. Interference alignment (IA) is a promising technique to tackle the multi-user interference in communication system. Compared with other interference management methods (such as zero-forcing), IA can not only effectively eliminate the interference, but also greatly increase the system capacity. However, the perfect channel state information (CSI) is required for both transmitters and receivers to apply the IA algorithm, which is hard to achieve in practical applications. In this paper, the effect of imperfect CSI on IA in CR system is analyzed in terms of signal to interference plus noise ratio and achievable sum rate. A linear finite state Markov chain (LFSMC) predictor, which incorporates the finite state Markov chain into the AR predictor, is proposed to reduce the impact of imperfect CSI on system performance of CR network. Moreover, for the sake of simplifying the initialization of LFSMC predictor, a simplified LFSMC (S-LFSMC) predictor is provided. Simulation results indicate that both of the LFSMC and S-LFSMC predictor can greatly improve the performance of IA system with the inaccurate CSI. Specifically, the LSFMC predictor can achieve satisfied performance compared with other predictors mentioned in this paper. And the LSFMC predictor which is simpler and its performance is still much better than traditional predictors. Therefore, we can choose a suitable predictor (LAFMC or S-LSFMC) based on the different requirements.
Shi, Z, Wu, Z, Yin, Z & Cheng, Q 2015, 'Novel spectrum sensing algorithms for OFDM cognitive radio networks', Sensors (Switzerland), vol. 15, no. 6, pp. 13966-13993.View/Download from: UTS OPUS or Publisher's site
© 2015 by the authors; licensee MDPI, Basel, Switzerland. Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm 'Differential Characteristics-Based OFDM (DC-OFDM)' for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain _ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a 'Differential Characteristics-Based Cyclic Prefix (DC-CP)' detector and a 'Differential Characteristics-Based Pilot Tones (DC-PT)' detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors.
Cheng, Q, Nguyen, D, Dutkiewicz, E & Mueck, MD 2018, 'Protecting Operational Information of Incumbent and Secondary Users in FCC Spectrum Access System', International Conference on Communications, IEEE, Kansas City, MO, USA.View/Download from: UTS OPUS or Publisher's site
Both Federal Communications Commission (FCC) and European Telecommunications Standards Institute (ETSI) support dynamic spectrum access (DSA) as an enabling technology for spectrum sharing. To effectively realize DSA in practice, users (from both defense and civil sectors) are required to share their (radio) operational information. That risks exposing their security, privacy, and business plan to unintended agents. In this paper, taking FCC's spectrum access system (SAS) as a study case, we propose a privacy-preserving scheme for DSA by leveraging encryption and obfuscation methods (PSEO). To implement PSEO, we propose an interference calculation scheme that allows users to calculate interference budget without revealing their operation information (e.g., antenna height, transmit power, location...), referred to as blind interference calculation method (BICM). BICM also reduces the computing overhead of PSEO, compared with FCC's SAS by moving interference budgeting tasks to local users and calculating it in an offline manner. Extensive detailed analysis and simulations show that our proposed PSEO is able to better protect all users' operational privacy, guaranteeing efficient spectrum utilization with less online overhead, compared with state of the art approaches.
Wang, H, Nguyen, D, Dinh, H, Dutkiewicz, E & Cheng, Q 2018, 'Real-Time Crowdsourcing Incentive for Radio Environment Maps: A Dynamic Pricing Approach', 2018 IEEE Global Communications Conference (GLOBECOM) Proceedings, IEEE Global Communications Conference, IEEE, UAE.View/Download from: UTS OPUS or Publisher's site