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Interference Alignment

Future mobile wireless networks are faced with an increasing demand for higher data rates. The recent research show that the mobile data volume will increase 18 times in the next five years. That's why more efficient communication systems must be developed where multiple transmitter and receiver pairs use the same radio resources. As an intrinsic result of this, the users will suffer some interference which refers to the addition of undesired signals to the desired signal. Therefore, the desired signal will be corrupted by the undesired signal and it will reduce the capacity which is the achievable rate of information that can be sent reliably.

Interference Alignment

The interference channel, where users interfere to each other, is a good model for many networks such as cellular networks, wireless local area networks, wireless ad-hoc networks. In these networks, it is well known that the performance of each user is interference limited. The researchers have been working on mitigating the interference for decades. Some of them addressed to cake cutting solutions which is basically dividing the resources into the number of users, just like cutting a cake such as Time Division Multiple Access (TDMA) where users are allocated into different time slots to use the same frequency band and Frequency Division Multiple Access (FDMA) where users communicate through different frequency slots at the same time. For example, in time domain for K users, we divide time into K time slots. Hence each user can communicate 1/K of the time interference free. Similarly FDMA allocates frequency subbands to the users. However these methods are known as bandwidth limited systems. Some other researchers focused on treating interference as noise like in Code Division Multiple Access (CDMA). However these systems are known to be interference limited as internal interference is generated by the system. In other words, these interference management approaches are not sufficient to achieve interference-free communication.

Interference Alignment

Recently, the researchers have proposed a new technique - Interference Alignment (IA) and showed that under certain conditions each user can utilize one half of the network resources interference free, regardless of how many users exist in the network. IA is a very inspiring approach as it provides more capacity than what was thought before IA. The key idea of IA is to fit the undesired signals from various users into a smaller space and separate it from the desired signals. The concept of IA is originated from the linear algebra, pioneered by two research groups led by Prof. Jafar in California and Prof. Khandani in Canada with their papers published in 2008. They showed that interference can be aligned into a smaller dimension while leaving the other dimensions available to the desired signals interference free. IA is simply a linear precoding technique that attempts to align interference in time, frequency or space. Precoding can be defined as signal processing to consolidate signals into dimensions via vectors that are multiplied with signals at transmitters. These precoders are generated in order to align all interference in a space spanned by all undesired signals. That is why each transmitter generates their precoders in a way such that their interference to the other users can be aligned.

In our research at VUW, we focus on IA systems for cellular networks which are widely used in today's communication systems. We analyze systems with imperfect CSI at transmitters and investigate the impact of CSI feedback errors on achievable sum rates of the systems. We optimize the precoders and receivers in order to maximize the system performance. We apply IA into multi-cellular networks using 3GPP channel models to have more realistic results.

Some Key Papers:

[1] V. Cadambe and S. Jafar, "Interference Alignment and the degrees of freedom of the K User Interference Channel," IEEE Transactions on Information Theory, vol. 54, pp. 3425-3441, Aug 2008.

[2] M.A. Maddah, A.S. Motahari and A.K. Khandani, "Communication over MIMO X Channels: Interference Alignment, Decomposition and Performance Analysis" IEEE Transactions on Information Theory, vol. 54, issue 8, pp. 3457-3470, Aug. 2008.

[3] S. Jafar and S. Shamai, "Degrees of Freedom Region for the MIMO X Channel," IEEE Transactions on Information Theory, Vol.53, no. 7, pp.1037-1044, July. 2007

[4]T. Gou, S. Jafar, S-W Jeon and S-Y Chung, "Aligned Interference Neutralization and the Degrees of Freedom of the 2 x 2 x 2 Interference Channel," IEEE Transactions on Information Theory, vol. 58, no. 7, July 2012.

[5] W. Shin and B. Clerckx, "Interference Alignment with Limited Feedback on Two-cell Interfering Two User MIMO-MAC," in the IEEE Proc. of International Symposium on Wireless Communication Systems (ISWCS 2012), pp. 566–570, 2012.

[6] W. Shin, N. Lee, J-B Lim, C. Shin and K. Jang, "Interference Alignment Through User Cooperation for Two-Cell MIMO Interfering Broadcast Channels, " IEEE Proc. of Global Communications Conference (GLOBECOM 2010), pp.120-125 2010.

[7] C. Suh and D. Tse, “Interference Alignment for cellular networks ,” in Proc. of Allerton Conference on Communications, Control and Computing, pp. 1037–1044, 2008.

[8] H. C. Suh and D. Tse, “Downlink Interference Alignment,” IEEE Transactions on Communications, vol. 59, pp. 2616–2626, Sept 2011.


R. Ustok, P. Dmochowski, P. Smith, M. Shafi, "Aligned Interference Neutralisation for 2x2x2 Interference Channel with Imperfect Channel State Information," Proc. of IEEE International Conference on Communications (ICC 2013),pp. 3823-3828, June 2013.

R. Ustok, M. Shafi, P. Dmochowski, P. Smith, "Interference Alignment with Combined Receivers For Heterogenous Networks," Submitted to IEEE International Conference on Communications (ICC 2014)

R.Ustok, P. Dmochowski, M. Shafi, P. Smith “Interference Alignment with Signal-to-Leakage-Noise-Ratio Based Precoders for Multicell Broadcast Networks” in preparation.

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