Angular interference suppression in cognitive LTE-A femtocells

We consider a heterogeneous Long Term Evolution Advanced (LTE-A) network where a cognitive femtocell operates in the coverage area of a traditional macrocell sharing the same frequency band. A new method is proposed here to avoid the interference generated by the femtocell on the macrocell downlink communications. In particular, the estimation of the direction of arrival of the primary user signals is used to place nulls in the femtocell transmission paths by means of a suitable beamforming algorithm. The novelty of the proposed approach is that it works on a physical resource basis, taking into account the specific LTE-A access scheme. In this way it is able to manage the interference towards several macrocell users characterized by a high angle dispersion of the multipath components. The obtained numerical results show that the system is able to significantly reduce the interference in different operational conditions, especially when the physical resource blocks belonging to the same macrocell user can be clustered, thus increasing the dimension of the snapshot used for the direction of arrival estimation.

[1]  Zaher Dawy,et al.  Enhancing the performance of OFDMA underlay cognitive radio networks via secondary pattern nulling and primary beam steering , 2011, 2011 IEEE Wireless Communications and Networking Conference.

[2]  Jingjing Xie,et al.  Spectrum sensing based on estimation of direction of arrival , 2010, International Conference on Computational Problem-Solving.

[3]  Romano Fantacci,et al.  Enabling technologies for smart building, what's missing? , 2013, AEIT Annual Conference 2013.

[4]  Romano Fantacci,et al.  LTE-A femto-cell interference mitigation with MuSiC DOA estimation and null steering in an actual indoor environment , 2013, 2013 IEEE International Conference on Communications (ICC).

[5]  Klaus Hugl,et al.  Spatial Reciprocity of Uplink and Downlink Radio Channels in FDD Systems , 2002 .

[6]  G. A. Ioannopoulos,et al.  A survey on the effect of small snapshots number and SNR on the efficiency of the MUSIC algorithm , 2012, Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation.

[7]  Romano Fantacci,et al.  Beamforming for small cell deployment in LTE-advanced and beyond , 2014, IEEE Wireless Communications.

[8]  Chin Choy Chai Distributed subcarrier and power allocation for OFDMA-based cognitive femtocell radio uplink , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[9]  Romano Fantacci,et al.  Physical Resource Block clustering method for an OFDMA cognitive femtocell system , 2014, Phys. Commun..

[10]  L. Godara Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations , 1997, Proc. IEEE.

[11]  Xiaojiang Du,et al.  Cognitive femtocell networks: an opportunistic spectrum access for future indoor wireless coverage , 2013, IEEE Wireless Communications.

[12]  Chan-Byoung Chae,et al.  Uncoordinated beamforming for cognitive networks , 2011 .

[13]  Yong-Hwan Lee,et al.  Power Control and Beamforming for Femtocells in the Presence of Channel Uncertainty , 2011, IEEE Transactions on Vehicular Technology.

[14]  Satoshi Nagata,et al.  Trends in small cell enhancements in LTE advanced , 2013, IEEE Communications Magazine.

[15]  Ta-Sung Lee,et al.  Game theoretic distributed dynamic resource allocation with interference avoidance in cognitive femtocell networks , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[16]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .