Collaborative Covert Communication Design Based on Lattice Reduction Aided Multiple User Detection Method

Spread spectrum communication is a typical scheme for covert communication because of its low detectability and antijam characteristic. However, the associated design concerns multiple factors, such as cochannel multiple access interference (MAI) and spread spectrum gain. In this paper, the lattice reduction theory is applied to MAI cancellation of spread spectrum communication and a novel lattice reduction aided multiple user detection method is proposed. The near maximum likelihood (ML) performance of MAI resistance is verified by simulation and theoretical analysis. The superiority of detection performance in strong MAI scenarios is especially addressed. Based on the algorithm, a collaborative covert communication system design is proposed. Low-power covert signals can be transmitted at a higher bit rate with the same coverage as more high-power cochannel signals. The covert transmission performance can be improved significantly compared to traditional designs.

[1]  Craig K. Rushforth,et al.  A Family of Suboptimum Detectors for Coherent Multiuser Communications , 1990, IEEE J. Sel. Areas Commun..

[2]  Xiaoli Ma,et al.  Performance analysis for MIMO systems with lattice-reduction aided linear equalization , 2008, IEEE Transactions on Communications.

[3]  Baoguo Yu,et al.  A MAI cancellation algorithm with near ML performance , 2015, 2015 IEEE International Conference on Communication Software and Networks (ICCSN).

[4]  Hong Zhang,et al.  Covert Communication by Compressed Videos Exploiting the Uncertainty of Motion Estimation , 2015, IEEE Communications Letters.

[5]  Ming Li,et al.  Cooperative Interference Mitigation for Heterogeneous Multi-Hop Wireless Networks Coexistence , 2016, IEEE Transactions on Wireless Communications.

[6]  Claus-Peter Schnorr,et al.  Lattice Basis Reduction: Improved Practical Algorithms and Solving Subset Sum Problems , 1991, FCT.

[7]  Sérgio M. Dias,et al.  Concept lattices reduction: Definition, analysis and classification , 2015, Expert Syst. Appl..

[8]  Jianbo Li,et al.  Multi-user interference pre-cancellation for downlink signals of multi-beam satellite system , 2013, 2013 3rd International Conference on Consumer Electronics, Communications and Networks.

[9]  László Lovász,et al.  Factoring polynomials with rational coefficients , 1982 .

[10]  A. Tennant,et al.  Covert communication using a directly modulated array transmitter , 2014, The 8th European Conference on Antennas and Propagation (EuCAP 2014).

[11]  Murray R. Bremner,et al.  Lattice Basis Reduction: An Introduction to the LLL Algorithm and Its Applications , 2011 .

[12]  Claus-Peter Schnorr,et al.  Lattice basis reduction: Improved practical algorithms and solving subset sum problems , 1991, FCT.

[13]  Brian A. LaMacchia Basis Reduction Algorithms and Subset Sum Problems , 1991 .

[14]  Roksana Boreli,et al.  Asynchronous Covert Communication Using BitTorrent Trackers , 2014, 2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security, 2014 IEEE 11th Intl Conf on Embedded Software and Syst (HPCC,CSS,ICESS).

[15]  Xiaoli Ma,et al.  Receiver Designs for Differential UWB Systems with Multiple Access Interference , 2014, IEEE Transactions on Communications.

[16]  Elias S. Manolakos,et al.  Hopfield neural network implementation of the optimal CDMA multiuser detector , 1996, IEEE Trans. Neural Networks.

[17]  Zhen Wang,et al.  Blind Multiuser Detection in MC-CDMA: Schmidt-Orthogonalization and Subspace Tracking Kalman Filtering , 2011, 2011 Third International Conference on Communications and Mobile Computing.

[18]  Jacques Stern,et al.  Lattice Reduction in Cryptology: An Update , 2000, ANTS.

[19]  Qu Wen-ke Analysis of the Parasitic Small Signal Coupling with RDSS RF Signal , 2009 .