A Multi-Dimensional Features-based Clustering Algorithm for Massive MIMO System

Huang, Lianfen; Lin, Hongyue; Zhang, Huanhuan; Zhao, Yifeng

doi:10.1109/iccse.2019.8845063

Cited by 1 publication

(4 citation statements)

References 13 publications

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“…We utilize the Euclidean distance to measure the similarity between users' relative locations, which is a vector, because the transmission channel is also a vector. The statement of the relevance between users 𝒊 and 𝒋, on the other hand, is defined as [18].…”

Section: User Groupingmentioning

confidence: 99%

“…𝑺 𝟐 = 𝑷 𝒊𝒋 = 𝒅 𝒊𝒋 (18) 𝑺 𝒊,𝒋 = −√𝒘 𝟏 𝑺 𝟏 𝟐 + 𝒘 𝟐 𝑺 𝟐 𝟐 (19) where ∑ 𝒘 𝒊 = 𝟏 𝟐 𝒊=𝟏 ∀ 𝒘 𝒊 ∈ [0,1] is the weight factor associated with the characteristics that meet the criteria. The higher the similarity between two users, the closer the distance between them.…”

Section: User Groupingmentioning

confidence: 99%

“…The affinity propagation (AP) clustering algorithm [18] is a semi-supervised clustering algorithm that does not require the user to specify the initial cluster center or the number of clusters in advance. It has good clustering stability and a low error rate and is widely used.…”

Section: User Groupingmentioning

confidence: 99%

“…It has good clustering stability and a low error rate and is widely used. We utilize the idea of information transmission of the AP method [18] based on multidimensional similarity for grouping users, as described in detail below.…”

Section: User Groupingmentioning

confidence: 99%

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Decoupling Energy Efficient Approach for Hybrid Precoding-Based mmWave Massive MIMO-NOMA With SWIPT

2022

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In this paper, we address the problem of energy consumption associated with mixed signal components such as analog-to-digital components in millimeter-wave (mmWave) massive MIMO systems. We employ non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. The simultaneous wireless information and power transmission technology (SWIPT) will be used in mmWave massive Multiple-Input multiple-Output MIMO systems. The utilization of SWIPT contributes to prolonging the battery life of mobile users (MUs) and enhances the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). However, we initially designed a user grouping algorithm based on the affinity propagation clustering algorithm, which preferentially groups the user equipment (UE) based on their channel correlation and distance. Then, we design the analog RF precoder based on the selected user grouping for all beams, followed by a low-dimensional digital baseband precoder design to further mitigate inter-beam interference and maximize the achievable sum-rate for the considered system. Subsequently, we transform the original optimization problem into a joint power allocation and power-splitting maximization problem. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of coupled variables and inter-user interference. To cope with this problem, a decoupled approach is adopted, in which the power allocation and power splitting are separated, and the corresponding sub-problems are solved using the Lagrangian duality method. Simulation results confirm the effectiveness of the proposed method and demonstrate that the proposed method is near-optimal and enjoys higher spectrum and energy efficiency compared with state-of-the-art designs and the conventional SWIPT-enabled mmWave MIMO-NOMA system.

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Section: User Groupingmentioning

confidence: 99%

Section: User Groupingmentioning

confidence: 99%

Section: User Groupingmentioning

confidence: 99%

Section: User Groupingmentioning

confidence: 99%

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