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Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network (1410.5020v1)

Published 19 Oct 2014 in cs.IT and math.IT

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, while 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 l1-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.

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Authors (2)
  1. Binbin Dai (5 papers)
  2. Wei Yu (209 papers)
Citations (373)

Summary

  • The paper introduces a novel optimization framework that jointly designs group-sparse beamforming, user scheduling, and BS clustering while explicitly addressing finite backhaul constraints.
  • It employs dynamic clustering with reweighted ℓ1-norm approximations and generalized WMMSE methods to reformulate nonconvex constraints into tractable weighted power conditions.
  • Numerical results demonstrate that the dynamic clustering approach outperforms static strategies, significantly enhancing network resource utilization and user throughput in dense 5G scenarios.

Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network

This paper presents a problem formulation and solution in the optimization of Cloud Radio Access Networks (C-RAN) which are envisioned to be crucial for future 5G networks due to their potential in handling inter-cell interference in dense deployments. The authors focus explicitly on a downlink C-RAN system, where multiple base stations (BSs) are connected to a central processing unit through finite-capacity backhaul links.

A key problem the paper addresses is the necessity to optimize user scheduling, BS clustering, and beamforming design, considering the unique constraints posed by finite backhaul capacities. This approach is distinct from other techniques as it implements explicit per-BS backhaul capacity constraints rather than relying on implicit assumptions or ignoring these constraints entirely.

Two main clustering models are explored: dynamic clustering and static clustering. In dynamic clustering, each user’s serving BSs can change per scheduling time slot, which allows for more efficient use of backhaul but requires more dynamic BS-user association management. Static clustering fixes the BS cluster for each user, simplifying management at the cost of some efficiency.

Methodology and Techniques

The problem framework is built around a network utility maximization approach, incorporating weighted sum rate (WSR) as the utility function. The paper leverages advanced techniques such as the reweighted 1\ell_1-norm to approximate nonconvex backhaul constraints. This approximation enables reformulation of these constraints into weighted power constraints which are more tractable for optimization. The authors employ the generalized Weighted Minimum Mean Square Error (WMMSE) method to handle these reformulated problems effectively.

For dynamic clustering, the solution involves designing group-sparse beamforming vectors that imply the most suitable BS cluster for each user. This entails using a joint beamforming and scheduling optimality strategy that adjust dynamically to network demands. For static clustering, attention is on optimizing the scheduling and beamforming vectors while adhering to a pre-defined user-centric BS cluster.

Key Results

Numerical results demonstrate significant performance gains with the proposed dynamic clustering algorithm over existing strategies. Particularly, the studies show that leveraging explicit backhaul constraints leads to more efficient use of network resources and improved user throughput. Even with the static clustering, the proposed heuristic methods achieve notable portions of these performance gains, validating the efficacy of the devised schemes.

Implications and Future Work

Practically, these techniques suggest viable implementations for 5G networks where managing dense user environments efficiently is critical. Theoretically, this work underscores the importance of incorporating realistic constraints like finite backhaul capacities into network optimization problems. Future advancements could explore the inclusion of these methods in more complex network contexts, such as heterogeneous networks and joint uplink-downlink optimization scenarios. The interplay between backhaul capacity and dynamic resource allocation strategies can yield further intriguing insights.

Overall, the paper offers a substantial method for enhancing the efficiency of downlink C-RANs, positioning this research in the field as a technically robust paper requiring deeper integration into the evolving narrative of 5G applications.