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 Multiple-Beam Selection With Limited Feedback for Hybrid Beamforming in Massive MIMO Systems

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 13327-13335 ◽  
Author(s):  
Yuwei Ren ◽  
Yingmin Wang ◽  
Can Qi ◽  
Yinjun Liu
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Limited Feedback ◽  
Multiple Beam ◽  
Beam Selection ◽  
Hybrid Beamforming

scholarly journals Design Simulation and Performance Assessment of Improved Channel Estimation for Millimeter Wave Massive MIMO Systems

2021 ◽  
Vol 9 (5) ◽  
pp. 56-61
Author(s):  
Aditi Sharma ◽  
Ashish Kumar Sharma ◽  
Laxmi Narayan Balai
Keyword(s):  
Channel Estimation ◽  
Antenna Array ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Structural Characteristics ◽  
Base Station ◽  
Accurate Information ◽  
Lens Antenna ◽  
Beam Selection ◽  
Estimation Scheme

In this paper, we have optimize specificities with the use of massive MIMO in 5 G systems. Massive MIMO uses a large number, low cost and low power antennas at the base stations. These antennas provide benefit such as improved spectrum performance, which allows the base station to serve more users, reduced latency due to reduced fading power consumption and much more. By employing the lens antenna array, beam space MIMO can utilize beam selection to reduce the number of required RF chains in mm Wave massive MIMO systems without obvious performance loss. However, to achieve the capacity-approaching performance, beam selection requires the accurate information of beam space channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. In this work we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beam space channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beam space channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beam space channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beam space channel can be exploited.

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 A Robust Channel Estimation Scheme for 5G Massive MIMO Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Imran Khan ◽  
Joel J. P. C. Rodrigues ◽  
Jalal Al-Muhtadi ◽  
Muhammad Irfan Khattak ◽  
Yousaf Khan ◽  
...  
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Base Station ◽  
Separation Mechanism ◽  
Channel Matrix ◽  
Large Channel ◽  
Estimation Scheme ◽  
Improved Performance ◽  
Csi Feedback

Channel state information (CSI) feedback in massive MIMO systems is too large due to large pilot overhead. It is due to the large channel matrix dimension which depends on the number of base station (BS) antennas and consumes the majority of scarce radio resources. To solve this problem, we proposed a scheme for efficient CSI acquisition and reduced pilot overhead. It is based on the separation mechanism for the channel matrix. The spatial correlation among multiuser channel matrices in the virtual angular domain is utilized to split the channel matrix. Then, the two parts of the matrix are estimated by deploying the compressed sensing (CS) techniques. This scheme is novel in the sense that the user equipment (UE) directly transmits the received symbols from the BS to the BS, so a joint CSI recovery is performed at the BS. Simulation results show that the proposed channel estimation scheme effectively estimates the channel with reduced pilot overhead and improved performance as compared with the state-of-the-art schemes.

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>

Hybrid Beamforming Design for Massive MIMO Systems with Limited CSI Feedback

2019 ◽  
Author(s):  
Francisco Costa ◽  
Daniel Araújo ◽  
Tarcisio Maciel
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Hybrid Beamforming ◽  
Csi Feedback

Optical True Time Delay-Based Hybrid Beamforming for Limited-Feedback Millimeter-Wave Massive MIMO Systems

2021 ◽  
pp. 1-1
Author(s):  
Xiaowen Wang ◽  
Huan Huang ◽  
Chongfu Zhang ◽  
Chongfu Zhang ◽  
Sinian Liu ◽  
...  
Keyword(s):  
Time Delay ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Limited Feedback ◽  
True Time Delay ◽  
Hybrid Beamforming ◽  
True Time

scholarly journals Performance Analysis of 3D Massive MIMO Cellular Systems with Collaborative Base Station

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xingwang Li ◽  
Lihua Li ◽  
Ling Xie ◽  
Xin Su ◽  
Ping Zhang
Keyword(s):  
Lower Bound ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Base Station ◽  
Cellular Systems ◽  
Small Scale ◽  
Pilot Contamination ◽  
3D Mimo ◽  
Sum Rate

Massive MIMO have drawn considerable attention as they enable significant capacity and coverage improvement in wireless cellular network. However, pilot contamination is a great challenge in massive MIMO systems. Under this circumstance, cooperation and three-dimensional (3D) MIMO are emerging technologies to eliminate the pilot contamination and to enhance the performance relative to the traditional interference-limited implementations. Motivated by this, we investigate the achievable sum rate performance of MIMO systems in the uplink employing cooperative base station (BS) and 3D MIMO systems. In our model, we consider the effects of both large-scale and small-scale fading, as well as the spatial correlation and indoor-to-outdoor high-rise propagation environment. In particular, we investigate the cooperative communication model based on 3D MIMO and propose a closed-form lower bound on the sum rate. Utilizing this bound, we pursue a “large-system” analysis and provide the asymptotic expression when the number of antennas at the BS grows large, and when the numbers of antennas at transceiver grow large with a fixed ratio. We demonstrate that the lower bound is very tight and becomes exact in the massive MIMO system limits. Finally, under the sum rate maximization condition, we derive the optimal number of UTs to be served.

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