Joint Base Station Clustering and Beamformer Design for Partial Coordinated Transmission in Heterogenous Networks (1203.6390v3)

Abstract: We consider the interference management problem in a multicell MIMO heterogenous network. Within each cell there are a large number of distributed micro/pico base stations (BSs) that can be potentially coordinated for joint transmission. To reduce coordination overhead, we consider user-centric BS clustering so that each user is served by only a small number of (potentially overlapping) BSs. Thus, given the channel state information, our objective is to jointly design the BS clustering and the linear beamformers for all BSs in the network. In this paper, we formulate this problem from a {sparse optimization} perspective, and propose an efficient algorithm that is based on iteratively solving a sequence of group LASSO problems. A novel feature of the proposed algorithm is that it performs BS clustering and beamformer design jointly rather than separately as is done in the existing approaches for partial coordinated transmission. Moreover, the cluster size can be controlled by adjusting a single penalty parameter in the nonsmooth regularized utility function. The convergence of the proposed algorithm (to a local optimal solution) is guaranteed, and its effectiveness is demonstrated via extensive simulation.

• The paper introduces a joint optimization framework that integrates BS clustering with beamformer design to manage interference in dense HetNets.
• It leverages sparse optimization and a group LASSO formulation to control user-centric cluster sizes and minimize coordination overhead.
• Simulation results confirm efficient convergence and high throughput performance comparable to full cooperation setups.

Joint Base Station Clustering and Beamformer Design for Partial Coordinated Transmission in Heterogeneous Networks

The paper addresses a fundamental challenge in the design and operation of contemporary wireless heterogeneous networks (HetNets), namely the efficient management of interference in multicell scenarios. The paper is motivated by the increasing demand for wireless data, which necessitates a shift from traditional networks with a few high-power base stations (BSs) to HetNets featuring an abundance of densely packed micro/pico BSs offering extended coverage in hotspot areas. The inter-BS interference in such a dense deployment could, if unmanaged, deteriorate system performance significantly.

The authors propose an innovative framework to tackle the interference management problem through a joint base station clustering and beamforming design. In contrast to existing approaches that often separate base station clustering from beamformer design, this paper advocates for a unified strategy that simultaneously optimizes both aspects. The proposed approach is rooted in sparse optimization and is realized through an iterative algorithm that solves a series of group LASSO problems. An important characteristic of the algorithm is the ability to control cluster sizes through a penalty parameter within a nonsmooth regularized utility function, thereby allowing fine-tuning of the coordination overhead.

Two central themes underscore the practical benefits and mathematical innovations of this work:

1. User-Centric Clustering: The proposed methodology ensures that each user is served by a select group of BSs, which may overlap across users. This user-centric BS clustering reduces the coordination and communication overhead as compared to scenarios where full cooperation by all BSs is enforced.
2. Joint Sparsity-Inducing Framework: The introduction of a group-sparsity encouraging regularization within the optimization problem naturally leads to the desired sparse BS-user associations. This approach allows for maintaining high system throughput while reducing the complexity and resource requirements of full BS coordination.

The effectiveness and applicability of the proposed algorithm are thoroughly demonstrated through simulation studies, which highlight several key strengths:

• The algorithm achieves convergence to a stationary solution.
• It provides a robust mechanism for controlling cluster sizes and, therefore, the level of coordination.
• Simulation results confirm its capability to sustain high throughput levels akin to full cooperation setups but with significantly reduced overhead.

Implications and Future Directions

This paper presents significant implications for the design of practical HetNets. By minimizing the need for full BS coordination, the algorithm not only reduces computational and signaling overhead but also scales effectively within dense network deployments. Furthermore, the sparse optimization framework offers a novel pathway for addressing similar challenges in uplink scenarios or in designing selection algorithms for distributed antenna systems.

Nonetheless, the problem of selecting appropriate penalty parameters for different network settings remains an open area for further research. Future work may consider automated or adaptive methods for tuning these parameters in real-time, thus enhancing the framework's responsiveness to network dynamics. Additionally, the adaptation of this approach to account for more complex user equipment configurations, such as multi-stream transmission, could widen its applicability.

In conclusion, while the paper advances the state of knowledge in interference management in HetNets, it also sets the stage for diverse applications and extensions in related fields of wireless communications technology.