Interference Aware-Coordinated Beamforming in a Multi-Cell System

Chae, Chan-Byoung; Hwang, In-Sun; Heath, Robert W.; Tarokh, Vahid

doi:10.1109/twc.2012.081312.112119

Cited by 68 publications

(70 citation statements)

References 30 publications

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“…Coordinated approaches were proposed for DL TBF in [11]- [13] to mitigate the inter-cell interference and the idea was extended to the coordinated CE in [2] for alleviating the effects of pilot contamination. The channel estimator adopted in the coordinated CE scheme of [2] is the linear MMSE (LMMSE) estimator [5], [10], which yields a better performance than the least squares (LS) estimator, but it requires the knowledge of the second-order statistics of all the UL channels, including those of the interfering MSs roaming in the adjacent cells.…”

mentioning

confidence: 99%

Pilot Contamination Elimination for Large-Scale Multiple-Antenna Aided OFDM Systems

Zhang

Chen

et al. 2014

IEEE J. Sel. Top. Signal Process.

136

124

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mentioning

confidence: 99%

Pilot Contamination Elimination for Large-Scale Multiple-Antenna Aided OFDM Systems

Zhang

Chen

et al. 2014

IEEE J. Sel. Top. Signal Process.

136

124

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“…In a strong interference scenario, we maximize the achievable sum rate R strong in (8), which can be written as…”

Section: Both Users Decode the Interferencementioning

confidence: 99%

“…However, this kind of schemes require sharing large amounts of user data over finite-capacity backhaul links and assume that BSs have full channel state information (CSI) of all the active users in the system. In contrast, the CoMP coordinated beamforming (CB) scheme only requires sharing the CSI of the interfered users among cooperated BSs and is therefore more feasible for practical implementations [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Beamforming design with proactive interference cancelation in MISO interference channels

Tian

Yang

2015

EURASIP J. Adv. Signal Process.

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In this paper, we design coordinated beamforming at base stations (BSs) to facilitate interference cancelation at users in interference networks, where each BS is equipped with multiple antennas and each user is with a single antenna. By assuming that each user can select the best decoding strategy to mitigate the interference, either canceling the interference after decoding when it is strong or treating it as noise when it is weak, we optimize the beamforming vectors that maximize the sum rate for the networks under different interference scenarios and find the solutions of beamforming with closed-form expressions. The inherent design principles are then analyzed, and the performance gain over passive interference cancelation is demonstrated through simulations in heterogeneous cellular networks.

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“…For example, jointly optimized MMSE and zero-forcing MIMO transceiver algorithms for the two-user MIMO interference channel (called interference aware-coordinated beamforming (IA-CBF)) are proposed in [49]. However, the MMSE IA-CBF can only achieve a lower bound on the sum information rate, and the zero-forcing IA-CBF only finds operating points achievable by zero-forcing strategies.…”

Section: Multi-antenna Usersmentioning

confidence: 99%