• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.564-564
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• 2006

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.54-59
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• 2012

Multiple antenna transmission and reception, whose principal merits are significant increase in spectral efficiency and/or reduction in error rate, lose much of their effectiveness in high levels of interference from other cells. Incorporating the other cell interference into advanced signal processing at transmitter and receiver is one of the key challenges for cell boundary users in cellular system. Since receiver can obtain exact knowledge of interference channels more easily than transmitter, an interference-aware multiple antenna receiver that can significantly attenuate interferences is considered. Based on the receiver, codebook-based coordinated precoding schemes are proposed. According to the level of cooperation, centralized and distributed schemes are proposed. We verified by the simulation results that even the distributed schemes, which have same amount of feedback and no cooperation between cells, have performance gain compared to the conventional non-coordinated scheme.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.18-25
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• 2014

In this paper, we propose a codebook-based interference alignment (IA) scheme in the constant multiple-input multiple-output (MIMO) interference channel especially for the uplink scenario. In our proposed scheme, we assume cooperation among base stations (BSs) through reliable backhaul links so that global channel knowledge is available for all BSs, which enables BS to compute he transmit precoder and inform its quantized index to the associated user via limited rate feedback link. We present an upper bound on the rate loss of the proposed scheme and derive the scaling law of the feedback load to maintain a constant rate loss relative to IA with perfect channel knowledge. Considering the impact of overhead due to training, cooperation, and feedback, we address the effective degrees of freedom (DOF) of the proposed scheme and derive the maximization of the effective DOF. From simulation results, we verify our analysis on the scaling law to preserve the multiplexing gain and confirm that the proposed scheme is more effective than the conventional IA scheme in terms of the effective DOF.