Distributed and collaborative beamforming (DCBF) has been attracting increased interest due to its desirable scalability and enhanced robustness, whereas slow convergence would hinder practical applications of the conventional DCBF algorithms for massive arrays. In this work, we consider the distributed reduced-rank beam coordination over an array network, equipped with a collection of potentially massive arrays interconnected via a certain topology. We develop a diffusion reduced-rank beam coordination algorithm along with an inherently adaptive combination scheme based on the combination matrix for beam coordination, leveraging the joint iterative alternating optimization methodology. We propose two efficient adaptive implementations with parametric matrix combination schemes, which could further enhance the robustness of the distributed reduced-rank beam coordination algorithm against the spatial variations of the signal and noise powers. The proposed distributed reduced-rank beam coordination algorithm could not only remarkably speed the initial convergence in comparison with its full-rank counterparts under the small-sample conditions, but also achieve comparable steady-state performance with sufficiently large sample size. The tracking capability could also be enhanced with the proposed algorithm as a by-product. Illustrative simulations validate the efficiency of the proposed reduced-rank beam coordination algorithm.

中文翻译：

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通过具有参数矩阵组合的阵列网络上的扩散降阶自适应进行光束协调


分布式协作波束形成（DCBF）由于其理想的可扩展性和增强的鲁棒性而引起了越来越多的关注，而缓慢的收敛速度将阻碍传统 DCBF 算法在大规模阵列中的实际应用。在这项工作中，我们考虑了阵列网络上的分布式降阶波束协调，该阵列网络配备了通过某种拓扑互连的潜在大规模阵列的集合。我们开发了一种扩散降阶波束协调算法以及基于波束协调组合矩阵的固有自适应组合方案，利用联合迭代交替优化方法。我们提出了两种具有参数矩阵组合方案的有效自适应实现，这可以进一步增强分布式降秩波束协调算法针对信号和噪声功率的空间变化的鲁棒性。所提出的分布式降阶波束协调算法与小样本条件下的全秩算法相比，不仅可以显着加快初始收敛速度，而且可以在足够大的样本量下实现可比的稳态性能。作为副产品，所提出的算法还可以增强跟踪能力。说明性仿真验证了所提出的降阶波束协调算法的效率。