Grouping-Based Interference Alignment With IA-Cell Assignment in Multi-Cell MIMO MAC Under Limited Feedback

Pan Cao, Alessio Zappone, Eduard Jorswieck

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Interference alignment (IA) is a promising technique to efficiently mitigate interference and to enhance the capacity of a wireless communication network. This paper proposes a grouping-based interference alignment (GIA) with optimized IA-Cell assignment for the multiple cells interfering multiple-input multiple-output (MIMO) multiple access channel (MAC) network under limited feedback. This work consists of three main parts: 1) an improved version (including some new improvements) of the GIA with respect to the degrees of freedom (DoF) and optimal linear transceiver design is provided, which allows for low-complexity and distributed implementation; 2) based on the GIA, the concept of IA-Cell assignment is introduced. Three IA-Cell assignment algorithms are proposed with different backhaul overhead and their DoF and rate performance is investigated; 3)the performance of the proposed GIA algorithms is studied under limited feedback of IA precoders. To enable efficient feedback, a dynamic feedback bit allocation (DBA) problem is formulated and solved in closed-form. The practical implementation, the backhaul overhead requirements, and the complexity of the proposed algorithms are analyzed. Numerical results show that our proposed algorithms greatly outperform the traditional GIA under both unlimited and limited feedback.
Original languageEnglish
Pages (from-to)1336-1351
Number of pages16
JournalIEEE Transactions on Signal Processing
Issue number5
Early online date30 Oct 2015
Publication statusPublished - 1 Mar 2016


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