Subverting MIMO wireless systems by jamming the channel estimation procedure

Multiple-input multiple-output (MIMO) technologies are popular in emerging wireless systems. Optimal MIMO communication is achieved by applying a waterfilling solution over the parallel subchannels associated with the channel matrix between a transmitter and receiver. The viability of such a method relies on the transmitter and receiver performing the singular value decomposition (SVD) of the estimated channel matrix. The estimation and use of the estimated channel matrix, however, is a point of weakness that can be used to subvert MIMO communication. In this paper, we investigate strategies for disrupting MIMO communications by developing attacks that target the channel estimation procedure of a MIMO system. Specifically, we study the impact that jamming the channel estimation procedure can have on SVD-based MIMO systems. Further, most new standards that employ MIMO incorporate space-time block coding (STBC) to bolster throughput and reliability. Although the choice of STBC varies across the standards, the Alamouti [5] scheme is a common basis for many protocols, including 802.11n, WiMAX, and 3GPP. We present jamming attacks that target the Alamouti scheme, and support their validity and effectiveness in the real world using the USRP/GNU Radio platform.

[1]  Eduard Axel Jorswieck,et al.  Optimal transmitter and jamming strategies in Gaussian MIMO channels , 2005, 2005 IEEE 61st Vehicular Technology Conference.

[2]  Georgios B. Giannakis,et al.  Anti-jam distributed MIMO decoding using wireless sensor networks , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  L. Mirsky.  SYMMETRIC GAUGE FUNCTIONS AND UNITARILY INVARIANT NORMS , 1960 .

[4]  Reinaldo A. Valenzuela,et al.  Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture , 1999 .

[5]  Mehul Motani,et al.  STBC-VBLAST for MIMO wireless communication systems , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[6]  Claude Oestges,et al.  MIMO Wireless Communications , 2007 .

[7]  G. Stewart.  Perturbation theory for the singular value decomposition , 1990 .

[8]  Muriel Médard,et al.  On Jamming in the Wideband Regime , 2006, 2006 IEEE International Symposium on Information Theory.

[9]  J.M. Cioffi,et al.  Spatially-Correlated Jamming in Gaussian Multiple Access and Broadcast Channels , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[10]  M. J. Gans,et al.  On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas , 1998, Wirel. Pers. Commun..

[11]  Andrea J. Goldsmith,et al.  Capacity and power allocation for fading MIMO channels with channel estimation error , 2006, IEEE Trans. Inf. Theory.

[12]  Claude Oestges,et al.  MIMO Wireless Communications: From Real-World Propagation to Space-Time Code Design , 2007 .

[13]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[14]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[15]  O. H. Lowry.  Academic press. , 1972, Analytical chemistry.

[16]  Siavash M. Alamouti,et al.  A simple transmit diversity technique for wireless communications , 1998, IEEE J. Sel. Areas Commun..

[17]  David Gesbert,et al.  From theory to practice: an overview of MIMO space-time coded wireless systems , 2003, IEEE J. Sel. Areas Commun..

[18]  Gerard J. Foschini,et al.  Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas , 1996, Bell Labs Technical Journal.

[19]  Tan F. Wong,et al.  Optimal training sequence in MIMO systems with multiple interference sources , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[20]  Joseph Mitola.  An Integrated Agent Architecture for Software Defined Radio , 2000 .

[21]  Reinaldo A. Valenzuela,et al.  V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel , 1998, 1998 URSI International Symposium on Signals, Systems, and Electronics. Conference Proceedings (Cat. No.98EX167).

[22]  Shilpa Achaliya,et al.  Cognitive radio , 2010 .

[23]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[24]  Moe Z. Win,et al.  On the capacity of spatially correlated MIMO Rayleigh-fading channels , 2003, IEEE Trans. Inf. Theory.

[25]  H. Weyl.  Das asymptotische Verteilungsgesetz der Eigenwerte linearer partieller Differentialgleichungen (mit einer Anwendung auf die Theorie der Hohlraumstrahlung) , 1912 .

[26]  P. Bello.  Characterization of Randomly Time-Variant Linear Channels , 1963 .