Optimisation of the CSIT Allocation Size for Interference Alignment Technique in Massive MIMO Networks

Massive multiple-input multiple-output systems use a nearly infinite number of high-quality antennas at the transmitter/receiver are anticipated to play a key role in “5G” systems. However, the large number of antenna elements in massive MIMO also posed interference a major deficiency for successful communication in wireless systems. This work deals with Interference Alignment (IA) technique for mitigating the interference, although massive-antennas ‘based’ transmitters coordinated transmission where each transmission creates interference at the unintended receivers, such that the interference signal lies in a reduced dimensional subspace at each receiver. Recent works deal with the feasibility of IA in terms of the numbers of antennas and assume full channel state information at the transmitter (CSIT) is defined by the perfect knowledge of the global channel matrix. In this paper, we develop an algorithm, which ensures IA with incomplete CSIT, which uses the antennas ensured by Massive MIMO to further minimize the size of the CSIT allocation provided that the feasibility of the interference alignment is conserved and make transmitter cooperation more practical. Where the size of the CIST allocation defined as the total of complex numbers transmitted to the transmitter.

[1]  Tharmalingam Ratnarajah,et al.  Interference Alignment in Two-Tier Randomly Distributed Heterogeneous Wireless Networks Using Stochastic Geometry Approach , 2018, IEEE Systems Journal.

[2]  Z. Luo,et al.  On the Degrees of Freedom Achievable Through Interference Alignment in a MIMO Interference Channel , 2011, IEEE Transactions on Signal Processing.

[3]  Tharmalingam Ratnarajah,et al.  Linear Interference Alignment in Full-Duplex MIMO Networks With Imperfect CSI , 2017, IEEE Transactions on Communications.

[4]  Paul de Kerret,et al.  Interference Alignment with Incomplete CSIT Sharing , 2014, IEEE Transactions on Wireless Communications.

[5]  Robert W. Heath,et al.  The practical challenges of interference alignment , 2012, IEEE Wireless Communications.

[6]  Paul de Kerret,et al.  CSI sharing strategies for transmitter cooperation in wireless networks , 2013, IEEE Wireless Communications.

[7]  Syed Ali Jafar,et al.  Degrees of Freedom of the K User M times N MIMO Interference Channel , 2008, IEEE Trans. Inf. Theory.

[8]  Robert W. Heath,et al.  On the Overhead of Interference Alignment: Training, Feedback, and Cooperation , 2012, IEEE Transactions on Wireless Communications.

[9]  Amir K. Khandani,et al.  Communication Over MIMO X Channels: Interference Alignment, Decomposition, and Performance Analysis , 2008, IEEE Transactions on Information Theory.

[10]  Tharmalingam Ratnarajah,et al.  On the Performance of Cooperative Spectrum Sensing in Random Cognitive Radio Networks , 2018, IEEE Systems Journal.

[11]  Carlos Beltrán,et al.  A general test to check the feasibility of linear interference alignment , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

[12]  Wei Yu,et al.  Multi-Cell MIMO Cooperative Networks: A New Look at Interference , 2010, IEEE Journal on Selected Areas in Communications.

[13]  Tat-Ming Lok,et al.  An Iterative Interference Alignment Algorithm for the General MIMO X Channel , 2019, IEEE Transactions on Wireless Communications.

[14]  Syed Ali Jafar,et al.  Interference Alignment and Degrees of Freedom of the $K$-User Interference Channel , 2008, IEEE Transactions on Information Theory.

[15]  Kate Ching-Ju Lin,et al.  Random access heterogeneous MIMO networks , 2011, SIGCOMM 2011.

[16]  Swarun Kumar,et al.  Interference alignment by motion , 2013, MobiCom.