Beamforming on the MISO interference channel with multi-user decoding capability
This paper considers the multiple-input-single-output interference channel (MISO-IC) with interference decoding capability (IDC), so that the interference signal can be decoded and subtracted from the received signal. On the MISO-IC with single user decoding, transmit beamforming vectors are classic...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
02.07.2011
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Subjects | |
Online Access | Get full text |
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Summary: | This paper considers the multiple-input-single-output interference channel
(MISO-IC) with interference decoding capability (IDC), so that the interference
signal can be decoded and subtracted from the received signal. On the MISO-IC
with single user decoding, transmit beamforming vectors are classically
designed to reach a compromise between mitigating the generated interference
(zero forcing of the interference) or maximizing the energy at the desired
user. The particularly intriguing problem arising in the multi-antenna IC with
IDC is that transmitters may now have the incentive to amplify the interference
generated at the non-intended receivers, in the hope that Rxs have a better
chance of decoding the interference and removing it. This notion completely
changes the previous paradigm of balancing between maximizing the desired
energy and reducing the generated interference, thus opening up a new dimension
for the beamforming design strategy.
Our contributions proceed by proving that the optimal rank of the transmit
precoders, optimal in the sense of Pareto optimality and therefore sum rate
optimality, is rank one. Then, we investigate suitable transmit beamforming
strategies for different decoding structures and characterize the Pareto
boundary. As an application of this characterization, we obtain a candidate set
of the maximum sum rate point} which at least contains the set of sum rate
optimal beamforming vectors. We derive the Maximum-Ratio-Transmission (MRT)
optimality conditions. Inspired by the MRT optimality conditions, we propose a
simple algorithm that achieves maximum sum rate in certain scenarios and
suboptimal, in other scenarios comparing to the maximum sum rate. |
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DOI: | 10.48550/arxiv.1107.0416 |