BD-ZCS: Multi-cell interference coordination via Zadoff-Chu sequence-based block diagonalization

Multi-cell interference coordination via Block Diagonalization (BD), which is widely used in LTE-Advanced interference management, has three main drawbacks: (1) limitation on the number of supported co-channel users; (2) performance degradation in Doppler channels with feedback delay and quantization; and (3) high signaling overhead to exchange Channel State Information (CSI) and to generate precoding matrix. In this paper, a new approach is proposed to overcome these drawbacks and to improve the multi-cell interference coordination. The core idea is to create a null space only to the users within one cell; to achieve this goal, Zadoff-Chu Sequence (ZCS) spreading is used to eliminate the multi-cell interference by leveraging the zero-correlation property of cyclic shifts of the ZCS. This way, the capacity of the cellular network is improved in Doppler channels with delayed and quantized feedback; moreover, signaling overhead and computational complexity are both reduced. The auxiliary sampling capability of the hardware is utilized to realize the higher sampling rate required by the proposed approach. The new interference-management solution is compared against the existing one via computer simulations and is shown to lead to significant capacity gains even in high-mobility scenarios.

[1]  David C. Chu,et al.  Polyphase codes with good periodic correlation properties (Corresp.) , 1972, IEEE Trans. Inf. Theory.

[2]  Jaehoon Jung,et al.  Zadoff-Chu Sequence Based Signature Identification for OFDM , 2013, IEEE Transactions on Wireless Communications.

[3]  Youngchul Sung,et al.  Coordinated Beamforming With Relaxed Zero Forcing: The Sequential Orthogonal Projection Combining Method and Rate Control , 2012, IEEE Transactions on Signal Processing.

[4]  Risto Wichman,et al.  Geodesical codebook design for precoded MIMO systems , 2009, IEEE Communications Letters.

[5]  Gerhard Fettweis,et al.  Static Clustering for Cooperative Multi-Point (CoMP) in Mobile Communications , 2011, 2011 IEEE International Conference on Communications (ICC).

[6]  Shuguang Cui,et al.  Cooperative Interference Management With MISO Beamforming , 2009, IEEE Transactions on Signal Processing.

[7]  Rui Zhang Cooperative Multi-Cell Block Diagonalization with Per-Base-Station Power Constraints , 2010, IEEE J. Sel. Areas Commun..

[8]  Huu Phu Bui,et al.  Performance Evaluation of a Multi-User MIMO System With Prediction of Time-Varying Indoor Channels , 2013, IEEE Transactions on Antennas and Propagation.

[9]  Marco Lops,et al.  Code-aided interference suppression for DS/CDMA overlay systems , 2002, Proc. IEEE.

[10]  Zhi Ding,et al.  Cooperative Multi-Cell MIMO Downlink Precoding With Finite-Alphabet Inputs , 2015, IEEE Trans. Commun..

[11]  Jeffrey G. Andrews,et al.  Block Diagonalization in the MIMO Broadcast Channel with Delayed CSIT , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[12]  Markus Rupp,et al.  Subspace Quantization Based Combining for Limited Feedback Block-Diagonalization , 2013, IEEE Transactions on Wireless Communications.

[13]  Martin Haardt,et al.  Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels , 2004, IEEE Transactions on Signal Processing.

[14]  Shuguang Cui,et al.  Cooperative Interference Management in Multi-Cell Downlink Beamforming , 2010, 2010 IEEE Wireless Communication and Networking Conference.

[15]  Mohammad M. Mansour Optimized Architecture for Computing Zadoff-Chu Sequences with Application to LTE , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[16]  Ana García Armada,et al.  Mean Achievable Rates in Clustered Coordinated Base Station Transmission with Block Diagonalization , 2013, IEEE Transactions on Communications.

[17]  Eitan Altman,et al.  Interference coordination in wireless networks: A flow-level perspective , 2013, 2013 Proceedings IEEE INFOCOM.