Soft and hard hybrid balanced clustering with innovative qualitative balancing approach

Anaraki, Seyed Alireza Mousavian; Haeri, Abdorrahman

doi:10.1016/j.ins.2022.09.044

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…So the overall performance degrades. This observation emphasizes the correlation between cardinality balancing and error performance, as discussed in[28]. Moreover, it is always not possible to make a balanced cluster, i.e., assign an equal number of APs and users to each cluster.…”

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confidence: 92%

Optimal Access Point Centric Clustering for Cell-Free Massive MIMO Using Gaussian Mixture Model Clustering

Biswas,

Mallik,

Letaief

2024

Trans. Mach. Learn. Comm. Netw.

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This paper proposes a Gaussian mixture model (GMM) based access point (AP) clustering technique in cell-free massive MIMO (CFMM) communication systems. The APs are first clustered on the basis of large-scale fading coefficients, and the users are assigned to each cluster depending on the channel gain. As the number of clusters increases, there is a degradation in the overall data rate of the system, causing a trade-off between the cluster number and average rate per user. To address this problem, we present an optimization problem that optimizes both the upper bound on the average downlink rate per user and the number of clusters. The optimal number of clusters is intuitively determined by solving the optimization problem, and then grouping the APs and users. As a result, the computation expense is much lower than the current techniques, since the existing methods require evaluations of the network performance in multiple iterations to find the optimal number of clusters. In addition, we analyze the performance of both balanced and unbalanced clustering. Numerical results will indicate that the unbalanced clustering yields a superior rate per user while maintaining a lower level of complexity compared to the balanced one. Furthermore, we investigate the statistical analysis of the spectral efficiency (SE) per user in the clustered CFMM. The findings reveal that the SE per user can be approximated by the logistic distribution.INDEX TERMS AP centric clustering, cell-free massive MIMO (CFMM), Gaussian mixture model (GMM), logistic distribution, spectral efficiency.

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confidence: 92%