Density based user grouping for massive MIMO downlink in FDD system

Sun, Yanhua; Lv, Susu; Liu, Shuo; Zhang, Yanhua

doi:10.1109/iccsn.2017.8230153

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The k-means and hierarchical clustering algorithms are two commonly used user clustering algorithms. The traditional k-means algorithm [24] is a classic user clustering algorithm that is simple in principle, low in complexity and easy to implement. However, it depends on the selection of the initial centre points.…”

Section: A User Clusteringmentioning

confidence: 99%

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Performance of Cell-Free Massive MIMO With Joint User Clustering and Access Point Selection

et al. 2021

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We consider an uplink cell-free massive multiple-input multiple-output (MIMO) system, in which the access points are connected to the central processing unit (CPU) through a fronthaul network. This system has the advantages of wide coverage and flexible deployment. However, the performance of this system depends on a capacity-limited fronthaul, and when the fronthaul is saturated, the quality of service will be reduced. To address this issue, we propose a joint user clustering and AP selection scheme, which can reduce the pressure on the fronthaul link while taking into account the system performance and computational complexity. We first derive a closed-form expression for the uplink spectral efficiency over Rician fading channels. Based on the derived expression, we formulate the problem of maximizing the minimum uplink spectral efficiency across all users by jointly optimizing the large-scale fading decoding (LSFD) coefficient and power control coefficient. Then, combined with the optimization results and channel estimation error, a suboptimal access point selection scheme is proposed. In addition, we propose a user clustering scheme to further reduce the complexity of the AP selection scheme. The simulation results show that the joint user clustering and access point selection scheme can reduce the system fronthaul link pressure, while the performance degrades only slightly.INDEX TERMS Access point selection, cell-free massive MIMO, spectral efficiency, User clustering.

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Section: A User Clusteringmentioning

confidence: 99%

“…If problem(26) is feasible and the solution is p * k , replace p k with p * k in(24) to find the optimal LSFD coefficients α * k , and set t min := t.If the problem is not feasible, set tmax := t Stop if tmax − t min < and set α * k = α * k / α * k . Otherwise, go to Step 2 .…”

mentioning

confidence: 99%

Performance of Cell-Free Massive MIMO With Joint User Clustering and Access Point Selection

et al. 2021

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“…Interestingly, this result prompted a series of subsequent studies on the problem of user grouping itself, such as agglomerative clustering method [25], density-based clustering [26], [27] etc.…”

Section: Introductionmentioning

confidence: 99%

A Covariance-Based Hybrid Channel Feedback in FDD Massive MIMO Systems

Qiu

Gesbert

Chen

et al. 2019

IEEE Trans. Commun.

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In this paper, a novel covariance-based channel feedback mechanism is investigated for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The concept capitalizes on the notion of user statistical separability which was hinted in several prior works in the massive antenna regime but has not fully exploited so far. We propose a hybrid statistical-instantaneous feedback mechanism where the users are separated into two classes of feedback design based on their channel covariance. Under the hybrid framework, each user either operates on a statistical feedback mode or quantized instantaneous channel feedback mode. The key challenge lies in the design of a covarianceaware classification algorithm which can handle the complex mutual interactions among all users. The classification is derived from rate bound principles and a precoding method is also devised under the mixed statistical and instantaneous feedback model. Simulations are performed to validate our analytical results and illustrate the sum rate advantages of the proposed feedback scheme under a global feedback overhead constraint.

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Based on Density-Optimized User Grouping in Massive MIMO with Two-Stage Precoding

2019

2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

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Density based user grouping for massive MIMO downlink in FDD system

Cited by 6 publications

References 10 publications

Performance of Cell-Free Massive MIMO With Joint User Clustering and Access Point Selection

Performance of Cell-Free Massive MIMO With Joint User Clustering and Access Point Selection

A Covariance-Based Hybrid Channel Feedback in FDD Massive MIMO Systems

Based on Density-Optimized User Grouping in Massive MIMO with Two-Stage Precoding

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