TY - GEN
T1 - Interference subspace tracking for network interference alignment in cellular systems
AU - Niu, Bo
AU - Haimovich, Alexander M.
PY - 2009
Y1 - 2009
N2 - In this paper, a practical solution to implement the distributed interference alignment (IA) algorithm of [1] in a cellular communication system is proposed. In the training period of the proposed strategy, the User Equipment (UE) and the Base Station (BS), without knowing a priori the interference covariance matrix, update directly the precoding and interference suppression matrices based on the received signals by a minor subspace tracking algorithm. At the end of the training phase, the precoding and interference suppression matrices are then used at the UE and BS, respectively, for the transmission period. A special spatial reuse method is also proposed for the training period to lower the system overhead. Numerical system performance results are provided, showing that the algorithm, referred to as Interference Subspace Tracking IA (IST-IA), yields a good trade-off between throughput gains, on one side, and training overhead and computational complexity, on the other. It is also argued that IST-IA is a promising solution not only for training but also for the tracking phase, if applicable.
AB - In this paper, a practical solution to implement the distributed interference alignment (IA) algorithm of [1] in a cellular communication system is proposed. In the training period of the proposed strategy, the User Equipment (UE) and the Base Station (BS), without knowing a priori the interference covariance matrix, update directly the precoding and interference suppression matrices based on the received signals by a minor subspace tracking algorithm. At the end of the training phase, the precoding and interference suppression matrices are then used at the UE and BS, respectively, for the transmission period. A special spatial reuse method is also proposed for the training period to lower the system overhead. Numerical system performance results are provided, showing that the algorithm, referred to as Interference Subspace Tracking IA (IST-IA), yields a good trade-off between throughput gains, on one side, and training overhead and computational complexity, on the other. It is also argued that IST-IA is a promising solution not only for training but also for the tracking phase, if applicable.
UR - http://www.scopus.com/inward/record.url?scp=77951542375&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951542375&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2009.5426236
DO - 10.1109/GLOCOM.2009.5426236
M3 - Conference contribution
AN - SCOPUS:77951542375
SN - 9781424441488
T3 - GLOBECOM - IEEE Global Telecommunications Conference
BT - GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference
T2 - 2009 IEEE Global Telecommunications Conference, GLOBECOM 2009
Y2 - 30 November 2009 through 4 December 2009
ER -