A low complexity channel estimation scheme for Massive MIMO systems

2017 ◽  
Author(s):  
Afonso Ferreira ◽  
Guilherme Gaspar ◽  
Paulo Montezuma ◽  
Rui Dinis ◽  
Rodolfo Oliveira
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity ◽  
Estimation Scheme

Low-Complexity Downlink Channel Estimation for Millimeter-Wave FDD Massive MIMO Systems

2019 ◽  
Vol 8 (4) ◽  
pp. 1103-1107 ◽  
Author(s):  
Xianda Wu ◽  
Guanghua Yang ◽  
Fen Hou ◽  
Shaodan Ma
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity

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 Energy Efficiency-Oriented Resource Allocation for Massive MIMO Systems with Separated Channel Estimation and Feedback

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 582
Author(s):  
Feng Hu ◽  
Kaiyue Wang ◽  
Shufeng Li ◽  
Libiao Jin
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Spatial Separation ◽  
Low Complexity ◽  
Maximization Problem

This paper proposes a dynamic resource allocation scheme to maximize the energy efficiency (EE) for Massive MIMO Systems. The imperfect channel estimation (CE) and feedback are explicitly considered in the EE maximization problem, which aim to optimize the power allocation, the antenna subset selection for transmission, and the pilot assignment. Assuming CE error to be bounded for the complex-constrained Cramer–Rao Bound (CRB), theoretical results show that the lower bound is directly proportional to its number of unconstrained parameters. Utilizing this perspective, a separated and bi-directional estimation is developed to achieve both low CRB and low complexity by exploiting channel and noise spatial separation. Exploiting global optimization procedure, the optimal resource allocation can be transformed into a standard convex optimization problem. This allows us to derive an efficient iterative algorithm for obtaining the optimal solution. Numerical results are provided to demonstrate that the outperformance of the proposed algorithms are superior to existing schemes.

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