2018
DOI: 10.1109/jsac.2018.2874138
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The Non-Coherent Ultra-Dense C-RAN Is Capable of Outperforming Its Coherent Counterpart at a Limited Fronthaul Capacity

Abstract: The weighted sum rate maximization problem of ultra-dense cloud radio access networks (C-RANs) is considered, where realistic fronthaul capacity constraints are incorporated. To reduce the training overhead, pilot reuse is adopted and the transmit-beamforming used is designed to be robust to the channel estimation errors. In contrast to the conventional C-RAN where the remote radio heads (RRHs) coherently transmit their data symbols to the user, we consider their non-coherent transmission, where no strict phas… Show more

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Cited by 37 publications
(61 citation statements)
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“…However, due to the non-convexity of the EH constraint, Problem (15) is still non-convex. To resolve this issue, we observe that it can be viewed as a difference of convex (d.c.) program, which can be efficiently solved by the successive convex approximation (SCA) method [40]. In particular, we can approximate it by its first-order Taylor expansion.…”
Section: B Optimizing the Precoding Matrices Fmentioning
confidence: 99%
“…However, due to the non-convexity of the EH constraint, Problem (15) is still non-convex. To resolve this issue, we observe that it can be viewed as a difference of convex (d.c.) program, which can be efficiently solved by the successive convex approximation (SCA) method [40]. In particular, we can approximate it by its first-order Taylor expansion.…”
Section: B Optimizing the Precoding Matrices Fmentioning
confidence: 99%
“…We note that {γi}i∈N are achieved without phase synchronization between BSs. We also remark that {γi}i∈N are the aggregated instantaneous SINR, i.e., the total information received at Ui is log(1 + γi) [8] (the derivation of γi can also be found in [11]). We aim at designing beamforming vectors {v ik } i,k so that the WSR is maximized under the constraints of transmit power budget at the BSs.…”
Section: System Modelmentioning
confidence: 99%
“…We adopt the IA framework to develop efficient solution to (3), which was inspired by our earlier work in [19]. To do so, we first reveal the hidden convexity in (3) by transforming the problem into the following equivalent form 2 We note that the decoding order has no impact on γ i [11].…”
Section: Efficient Solution For (3) Via Inner Approximationmentioning
confidence: 99%
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