Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network
Binbin Dai,Wei Yu +1 more
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TLDR
The proposed dynamic clustering algorithm can achieve significant performance gain over existing naive clustering schemes and is shown to solve the weighted sum rate maximization problem through a generalized weighted minimum mean square error approach.Abstract:
This paper considers a downlink cloud radio access network (C-RAN) in which all the base-stations (BSs) are connected to a central computing cloud via digital backhaul links with finite capacities. Each user is associated with a user-centric cluster of BSs; the central processor shares the user's data with the BSs in the cluster, which then cooperatively serve the user through joint beamforming. Under this setup, this paper investigates the user scheduling, BS clustering, and beamforming design problem from a network utility maximization perspective. Differing from previous works, this paper explicitly considers the per-BS backhaul capacity constraints. We formulate the network utility maximization problem for the downlink C-RAN under two different models depending on whether the BS clustering for each user is dynamic or static over different user scheduling time slots. In the former case, the user-centric BS cluster is dynamically optimized for each scheduled user along with the beamforming vector in each time-frequency slot, whereas in the latter case, the user-centric BS cluster is fixed for each user and we jointly optimize the user scheduling and the beamforming vector to account for the backhaul constraints. In both cases, the nonconvex per-BS backhaul constraints are approximated using the reweighted l 1 -norm technique. This approximation allows us to reformulate the per-BS backhaul constraints into weighted per-BS power constraints and solve the weighted sum rate maximization problem through a generalized weighted minimum mean square error approach. This paper shows that the proposed dynamic clustering algorithm can achieve significant performance gain over existing naive clustering schemes. This paper also proposes two heuristic static clustering schemes that can already achieve a substantial portion of the gain.read more
Citations
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Proceedings ArticleDOI
Transmit beamforming for QoE improvement in C-RAN with mobile virtual network operators
TL;DR: A beamforming scheme that coordinates multiple remote radio heads in C-RAN to improve the quality of experience (QoE) of users by maximizing their aggregate weighted quality of service (QoS).
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Optimizing RRH Placement Under a Noise-Limited Point-to-Point Wireless Backhaul
TL;DR: In this paper, the deployment decisions and location optimization for the remote radio heads (RRHs) in coordinated distributed networks in the presence of a wireless backhaul were investigated. And the authors showed that even for noise-limited backhaul links, a large bandwidth must be allocated to the backhaul to allow freely distributing the RRHs in the network.
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Robust Beamforming With Pilot Reuse Scheduling in a Heterogeneous Cloud Radio Access Network
TL;DR: In this paper, the authors considered a downlink ultradense heterogeneous cloud radio access network, which guarantees seamless coverage and can provide high date rates, and proposed a pilot scheduling algorithm to minimize the sum mean square error of all channel estimates.
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System Energy-Efficient Hybrid Beamforming for mmWave Multi-user Systems
TL;DR: This paper develops energy-efficient hybrid beamforming designs for mmWave multi-user systems where analog precoding is realized by switches and phase shifters such that radio frequency chain to transmit antenna connections can be switched off for energy saving.
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Optimizing the MIMO Cellular Downlink: Multiplexing, Diversity, or Interference Nulling?
TL;DR: To maximize the per-BS ergodic sum rate, with an optimal allocation of spatial resources, interference nulling does not provide a tangible benefit, and the strategy of avoiding inter-cell interferencenulling is already close-to-optimal in terms of the sum-rate.
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