Cheng, Q, Shi, Z, Nguyen, D & Dutkiewicz, E 2019, 'Sensing OFDM Signal: A Deep Learning Approach', IEEE Transactions on Communications (TCOM), vol. 67, no. 11, pp. 7785-7798.View/Download from: Publisher's site
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: 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: 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: 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: Publisher's site
Cheng, Nguyen, D, Dutkiewicz & Mueck 2017, 'Preserving Operational Information in Spectrum Access System with Dishonest Users', 2017 17th International Symposium on Communications and Information Technologies (ISCIT), International Symposium on Communications and Information Technologies, IEEE, Cairns, QLD, Australia.View/Download from: Publisher's site
Privacy has been regarded as one of the most critical issues in light of promoting the development of centralized spectrum management, hence attracting wide attention. However, researchers in the current literature are usually protecting users' privacy based on the assumption that all users are honest, which is reasonable but not practical. In this paper, we investigate the privacy issue among different tiers of users in the centralized spectrum access system (SAS), mainly focusing on honest Priority Access Licenses (PALs) and dishonest General Authorized Accesses (GAAs). To that end, we propose an authentication scheme to prevent GAAs from using other users' information by the aid of a public key cryptosystem. Moreover, we propose a “punishment and forgiveness” scheme, which draws support from GAAs' reputation stores (RSs) and reputation histories (RHs), to encourage GAAs to engage in positive and true activities. Besides, we propose a privacy preservation scheme cooperating with the “punishment and forgiveness” scheme (PS-PFS) in order to effectively mitigate the impact of dishonest users while fully preserving all users' privacy as well as successfully realizing spectrum sharing.
Cheng, Q, Dutkiewicz, Fang, G, Shi, Z, Nguyen, D & Wang, H 2017, 'A Novel Full-Duplex Spectrum Sensing Algorithm for OFDM Signals in Cognitive Radio Networks', GLOBECOM 2017 - 2017 IEEE Global Communications Conference, IEEE Global Communications Conference, IEEE, Singapore.View/Download from: Publisher's site
Full duplex (FD) capability enables a "listen and talk" protocol for spectrum sensing that has been used as a new paradigm to increase the spectrum utilization in cognitive radio networks (CRNs). However, the spectrum sensing performance suffers from the imperfect self-interference suppression (SIS). This could significantly degrade the performance of FD systems in CRNs. In this paper, we investigate the issue of spectrum sensing with imperfect SIS in FD systems. By drawing support from a cyclic prefix (CP) of Orthogonal Frequency Division Modulation (OFDM) signals, we propose a novel spectrum sensing mechanism that is robust to self- interference. Comparing with other conventional sensing approaches in FD systems, the proposed method is independent of timing delay. That significantly improves the sensing performance, even without requiring a complex process for timing delay estimation. As a result, it also reduces the overhead of spectrum sensing. Extensive simulation results indicate that even with serious self-interference and timing delay, the presented approach is still able to achieve much higher performance than the conventional energy detection and waveform-based detection approaches.
Cheng, Q, Fang, G, Nguyen, D & Dutkiewicz, E 2016, 'Novel Pilot Decontamination Methods for Massive MIMO Systems Under Practical Scenarios', 16th International Symposium on Communications and Information Technologies (ISCIT), International Symposium on Communications and Information Technologies, IEEE, Qingdao, PEOPLES R CHINA.View/Download from: Publisher's site
Accurate and efficient channel estimation methods have the ability to realize the theoretical gain in multi-input multi-output (Massive MIMO) systems which have a massive number of antennas. However, the pilot contamination in Massive MIMO channel estimation process, rooted from the pilot reuse, is a critical problem that severely degrades the performance of the system. This work aims to address the problem of pilot contamination in covariance-aided channel estimation methods while considering practical scenarios where the channel covariance matrices change due to a new user arrival and users mobility. To that end, we first design a method to track the channel covariance matrices and then use these estimated values in Bayesian estimation. Simulation results indicate that the normalized mean square error (NMSE) for both channel covariance matrices and the CSI itself of our proposed methods are much lower than those of classical methods based on least square (LS) and Bayesian estimation. Additionally, for the case that users move slowly (e.g., at walking speed), our proposed method can provide satisfactory performance for more than three times as much as classical Bayesian estimation before system re-train channel covariance matrices. In other words, compared with classical Bayesian methods, our proposed methods are able to get good system performance with less overhead and complexity by a lower frequency of re-training process.