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Section 2 describes the system model for distributed beamforming.
In this literature, vector perturbation is a popular and efficient technique for distributed beamforming scheme to mitigate the requirement on CSI feedback [4 7, 9].
Bromberg [24] consider the capacity maximization problem and propose a locally enabled global optimization (LEGO) algorithm for distributed beamforming update under Gaussian other-user interference.
In [23], an intelligent power allocation algorithm for distributed beamforming is proposed to guarantee the required channel capacity and improve the network lifetime as well.
To the best of our knowledge, literature does not evaluate the effects of residual CFO on the SNR gain and diversity order for distributed beamforming schemes where the transmitters share the same time and frequency resources for transmitting a common data toward multiple receivers.
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We consider the problem of distributed beamforming for a two-way relay network which consists of two transceivers and multiple relay nodes.
In their paper "Performance of distributed beamforming for dense relay deployments in the presence of limited feedback information", T. Halinen, A.A. Dowhuszko, and J. Hämäläinen investigate the performance of a DBF algorithm for different amounts of channel phase feedback information.
In MISO-IC beamforming, there are two well-known categories of distributed beamforming strategies, namely competitive (egoistic) and cooperative (altruistic).
In one word, the feedback procedure decreases the efficiency of distributed beamforming.
A hardware experiment is described in Section 5, which demonstrates the validity of distributed beamforming.
Phase feedback and adjustment between wireless nodes greatly reduce the power efficiency of distributed beamforming.
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