scholarly journals Joint Analog Beam Selection and Digital Beamforming in Millimeter Wave Cell-Free Massive MIMO Systems

2021 ◽  
Author(s):  
Cenk M. Yetis ◽  
Emil Björnson ◽  
Pontus Giselsson
Keyword(s):  
Millimeter Wave ◽  
Massive Mimo ◽  
Digital Filters ◽  
Mimo Systems ◽  
Low Cost ◽  
Supervised Machine Learning ◽  
Rate Maximization ◽  
Selection Decisions ◽  
Beam Selection ◽  
Sum Rate

<p>Cell-free massive MIMO systems consist of many distributed access points with simple components that jointly serve the users. In millimeter wave bands, only a limited set of predetermined beams can be supported. In a network that consolidates these technologies, downlink analog beam selection stands as a challenging task for the network sum-rate maximization. Low-cost digital filters can improve the network sum-rate further. In this work, we propose low-cost joint designs of analog beam selection and digital filters. The pro-posed joint designs achieve significantly higher sum-rates than the disjoint design benchmark. Supervised machine learning (ML) algorithms can efficiently approximate the input-output mapping functions of the beam selection decisions of the joint designs with low computational complexities. Since the training of ML algorithms is performed off-line, we pro-pose a well-constructed joint design that combines multiple initializations, iterations, and selection features, as well as beam conflict control, i.e., the same beam cannot be used for multiple users. The numerical results indicate that ML algorithms can retain 99-100% of the original sum-rate results achieved by the proposed well-constructed designs.</p>

scholarly journals Joint Analog Beam Selection and Digital Beamforming in Millimeter Wave Cell-Free Massive MIMO Systems

2021 ◽  
Author(s):  
Cenk M. Yetis ◽  
Emil Björnson ◽  
Pontus Giselsson
Keyword(s):  
Millimeter Wave ◽  
Massive Mimo ◽  
Digital Filters ◽  
Mimo Systems ◽  
Low Cost ◽  
Supervised Machine Learning ◽  
Rate Maximization ◽  
Selection Decisions ◽  
Beam Selection ◽  
Sum Rate

<p>Cell-free massive MIMO systems consist of many distributed access points with simple components that jointly serve the users. In millimeter wave bands, only a limited set of predetermined beams can be supported. In a network that consolidates these technologies, downlink analog beam selection stands as a challenging task for the network sum-rate maximization. Low-cost digital filters can improve the network sum-rate further. In this work, we propose low-cost joint designs of analog beam selection and digital filters. The pro-posed joint designs achieve significantly higher sum-rates than the disjoint design benchmark. Supervised machine learning (ML) algorithms can efficiently approximate the input-output mapping functions of the beam selection decisions of the joint designs with low computational complexities. Since the training of ML algorithms is performed off-line, we pro-pose a well-constructed joint design that combines multiple initializations, iterations, and selection features, as well as beam conflict control, i.e., the same beam cannot be used for multiple users. The numerical results indicate that ML algorithms can retain 99-100% of the original sum-rate results achieved by the proposed well-constructed designs.</p>

Low-Complexity Precoding for Sum Rate Maximization in Downlink Massive MIMO Systems

2017 ◽  
Vol 6 (2) ◽  
pp. 186-189 ◽  
Author(s):  
Hieu V. Nguyen ◽  
Van-Dinh Nguyen ◽  
Oh-Soon Shin
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity ◽  
Rate Maximization ◽  
Sum Rate Maximization ◽  
Sum Rate

Beam Selection for Cell-free Millimeter-Wave Massive MIMO Systems

2021 ◽  
Author(s):  
Qiulin Xue ◽  
Qingqing Li ◽  
Chao Dong ◽  
Shiqiang Suo ◽  
Kai Niu
Keyword(s):  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Beam Selection ◽  
Selection For

scholarly journals Spatio-Radio Resource Management and Hybrid Beamforming for Limited Feedback Massive MIMO Systems

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1061 ◽  
Author(s):  
Hedi Khammari ◽  
Irfan Ahmed ◽  
Ghulam Bhatti ◽  
Masoud Alajmi
Keyword(s):  
Massive Mimo ◽  
Fourier Transforms ◽  
Mimo Systems ◽  
Limited Feedback ◽  
Base Station ◽  
Discrete Fourier Transforms ◽  
Beam Selection ◽  
Sum Rate ◽  
Hybrid Beamforming ◽  
Csi Feedback

In this paper, a joint spatio–radio frequency resource allocation and hybrid beamforming scheme for the massive multiple-input multiple-output (MIMO) systems is proposed. We consider limited feedback two-stage hybrid beamformimg for decomposing the precoding matrix at the base-station. To reduce the channel state information (CSI) feedback of massive MIMO, we utilize the channel covariance-based RF precoding and beam selection. This beam selection process minimizes the inter-group interference. The regularized block diagonalization can mitigate the inter-group interference, but requires substantial overhead feedback. We use channel covariance-based eigenmodes and discrete Fourier transforms (DFT) to reduce the feedback overhead and design a simplified analog precoder. The columns of the analog beamforming matrix are selected based on the users’ grouping performed by the K-mean unsupervised machine learning algorithm. The digital precoder is designed with joint optimization of intra-group user utility function. It has been shown that more than 50 % feedback overhead is reduced by the eigenmodes-based analog precoder design. The joint beams, users scheduling and limited feedbacK-based hybrid precoding increases the sum-rate by 27 . 6 % compared to the sum-rate of one-group case, and reduce the feedback overhead by 62 . 5 % compared to the full CSI feedback.

scholarly journals Non-Iterative Downlink Training Sequence Design Based on Sum Rate Maximization in FDD Massive MIMO Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 108731-108747 ◽  
Author(s):  
Muntadher Qasim Alsabah ◽  
Mikko Vehkapera ◽  
Timothy O'Farrell
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Training Sequence ◽  
Sequence Design ◽  
Rate Maximization ◽  
Training Sequence Design ◽  
Sum Rate Maximization ◽  
Sum Rate

scholarly journals Hybrid Multi-User Precoding with Manifold Discriminative Learning for Millimeter-Wave Massive MIMO Systems

2021 ◽  
pp. 411-422
Author(s):  
Xiaoping Zhou ◽  
◽  
Bin Wang ◽  
Jing Zhang ◽  
Qian Zhang ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Mimo Systems ◽  
Gradient Methods ◽  
Maximization Problem ◽  
High Signal ◽  
User Clustering ◽  
Rate Maximization ◽  
Sum Rate Maximization ◽  
Sum Rate ◽  
Low Dimensional

In large-array millimeter-wave (mmWave) systems, hybrid multi-user precoding is one of the most attractive research topics. This paper first presents a low-dimensional manifolds architecture for the analog precoder. An objective function is formulated to maximize the Energy Efficiency (EE) in consideration of the insertion loss for hybrid multi-user precoder. The optimal scheme is intractable to achieve, so that we present a user clustering hybrid precoding scheme. By modeling each user set as a manifold, we formulate the problem as clustering-oriented multi-manifolds learning. We discuss the effect of non-ideal factors on the EE performance. Through proper user clustering, the hybrid multi-user precoding is investigated for the sum-rate maximization problem by manifold quasi conjugate gradient methods. The high signal to interference plus noise ratio (SINR) is achieved and the computational complexity is reduced by avoiding the conventional schemes to deal with high-dimensional channel parameters. Performance evaluations show that the proposed scheme can obtain near-optimal sum-rate and considerably higher spectral efficiency than some existing solutions.

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