Interference Alignment Techniques for MIMO Multi-Cell Interfering Broadcast Channels

The interference alignment (IA) is a promising technique to efficiently mitigate interference and to enhance capacity of a wireless communication network. This paper proposes an interference alignment scheme for a network with multiple cells and multiple multiple-input and multiple-output (MIMO) use...

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Bibliographic Details
Published in:IEEE transactions on communications 2013-01, Vol.61 (1), p.164-175
Main Authors: Jie Tang, Lambotharan, S.
Format: Article
Language:English
Subjects:
Access methods and protocols, osi model
Applied sciences
Array signal processing
beamforming
Broadcasting. Videocommunications. Audiovisual
cellular network
Complexity theory
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information, signal and communications theory
Interference
interference alignment (IA)
MIMO
Miscellaneous
Receivers
Signal and communications theory
Signal, noise
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Teleprocessing networks. Isdn
Transmission and modulation (techniques and equipments)
Transmitters
Vectors
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Summary:The interference alignment (IA) is a promising technique to efficiently mitigate interference and to enhance capacity of a wireless communication network. This paper proposes an interference alignment scheme for a network with multiple cells and multiple multiple-input and multiple-output (MIMO) users under a Gaussian interference broadcast channel (IFBC) scenario. We first extend a grouping method already known in the literature to a multiple-cells scenario and jointly design transmit and receiver beamforming vectors using a closed-form expression without iterative computation. Then we propose a new approach using the principle of multiple access channel (MAC) - broadcast channel (BC) duality to perform interference alignment while maximizing capacity of users in each cell. The algorithm in its dual form is solved using interior point methods. We show that the proposed approach outperforms the extension of the grouping method in terms of capacity and basestation complexity. Finally, a rate balancing technique is introduced to maintain fairness among users.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2012.100912.110644