scholarly journals Downlink Channel Estimation in Massive Multiple-Input Multiple-Output with Correlated Sparsity by Overcomplete Dictionary and Bayesian Inference

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 473 ◽  
Author(s):  
Wei Lu ◽  
Yongliang Wang ◽  
Xiaoqiao Wen ◽  
Shixin Peng ◽  
Liang Zhong
Keyword(s):  
Bayesian Inference ◽  
Channel Estimation ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Temporal Correlation ◽  
Angular Domain ◽  
Multiple Input ◽  
Overcomplete Dictionary ◽  
Input Multiple Output

We exploited the temporal correlation of channels in the angular domain for the downlink channel estimation in a massive multiple-input multiple-output (MIMO) system. Based on the slow time-varying channel supports in the angular domain, we combined the channel support information of the downlink angular channel in the previous timeslot into the channel estimation in the current timeslot. A downlink channel estimation method based on variational Bayesian inference (VBI) and overcomplete dictionary was proposed, in which the support prior information of the previous timeslot was merged into the VBI for the channel estimation in the current timeslot. Meanwhile the VBI was discussed for a complex value in our system model, and the structural sparsity was utilized in the Bayesian inference. The Bayesian Cramér–Rao bound for the channel estimation mean square error (MSE) was also given out. Compared with other algorithms, the proposed algorithm with overcomplete dictionary achieved a better performance in terms of channel estimation MSE in simulations.

A companding approach for PAPR suppression in OFDM based massive MIMO system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ajay Kumar Yadav ◽  
Pritam Keshari Sahoo ◽  
Yogendra Kumar Prajapati
Keyword(s):  
Massive Mimo ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Average Power ◽  
Base Station ◽  
Convex Optimization Problem ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm

Abstract Orthogonal frequency division multiplexing (OFDM) based massive multiuser (MU) multiple input multiple output (MIMO) system is popularly known as high peak-to-average power ratio (PAPR) issue. The OFDM-based massive MIMO system exhibits large number of antennas at Base Station (BS) due to the use of large number of high-power amplifiers (HPA). High PAPR causes HPAs to work in a nonlinear region, and hardware cost of nonlinear HPAs are very high and also power inefficient. Hence, to tackle this problem, this manuscript suggests a novel scheme based on the joint MU precoding and PAPR minimization (PP) expressed as a convex optimization problem solved by steepest gradient descent (GD) with μ-law companding approach. Therefore, we develop a new scheme mentioned to as MU-PP-GDs with μ-law companding to minimize PAPR by compressing and enlarging of massive MIMO OFDM signals simultaneously. At CCDF = 10−3, the proposed scheme (MU-PP-GDs with μ-law companding for Iterations = 100) minimizes the PAPR to 3.70 dB which is better than that of MU-PP-GDs, (iteration = 100) as shown in simulation results.

scholarly journals Energy and Information Beamforming in Airborne Massive MIMO System for Wireless Powered Communications

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3540 ◽  
Author(s):  
Yurong Wang ◽  
Aijun Liu ◽  
Kui Xu ◽  
Xiaochen Xia
Keyword(s):  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Hardware Complexity ◽  
Mimo Channels ◽  
Channel State ◽  
Active Antennas ◽  
Large Numbers ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Critical Problems

Energy supply and information backhaul are critical problems for wireless sensor networks deployed in remote places with poor infrastructure. To deal with these problems, this paper proposes an airborne massive multiple-input multiple-output (MIMO) system for wireless energy transfer (WET) and information transmission. An air platform (AP) equipped with a two-dimensional rectangular antenna array is employed to broadcast energy and provide wireless access for ground sensors. By exploiting the statistical property of air-terrestrial MIMO channels, the energy and information beamformers are jointly designed to maximize the average received signal-to-interference-plus-noise ratio (SINR), which gives rise to a statistical max-SINR beamforming scheme. The scheme does not rely on the instantaneous channel state information, but still requires large numbers of RF chains at AP. To deal with this problem, a heuristic strongest-path energy and information beamforming scheme is proposed, which can be implemented in the analog-domain with low computational and hardware complexity. The analysis of the relation between the two schemes reveals that, with proper sensor scheduling, the strongest-path beamforming is equivalent to the statistical max-SINR beamforming when the number of AP antennas tends to infinity. Using the asymptotic approximation of average received SINR at AP, the system parameters, including transmit power, number of active antennas of AP and duration of WET phase, are optimized jointly to maximize the system energy efficiency. The simulation results demonstrate that the proposed schemes achieve a good tradeoff between system performance and complexity.

Precoding for Multiuser MIMO

2012 ◽  
pp. 130-156 ◽  
Author(s):  
Elsadig Saeid ◽  
Varun Jeoti ◽  
Brahim Belhaouari Samir
Keyword(s):  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Achievable Rate ◽  
Multiple Access Channels ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Single User ◽  
A New Technique ◽  
Improved Technique

Future Wireless Networks are expected to adopt multi-user multiple input multiple output (MU-MIMO) systems whose performance is maximized by making use of precoding at the transmitter. This chapter describes the recent advances in precoding design for MU-MIMO and introduces a new technique to improve the precoder performance. Without claiming to be comprehensive, the chapter gives deep introduction on basic MIMO techniques covering the basics of single user multiple input multiple output (SU-MIMO) links, its capacity, various transmission strategies, SU-MIMO link precoding, and MIMO receiver structures. After the introduction, MU-MIMO system model is defined and maximum achievable rate regions for both MU-MIMO broadcast and MU-MIMO multiple access channels are explained. It is followed by critical literature review on linear precoding design for MU-MIMO broadcast channel. This paves the way for introducing an improved technique of precoding design that is followed by its performance evaluation.

An Adaptive Multiple LS Channel Estimation Method for MIMO System

2012 ◽  
Vol 239-240 ◽  
pp. 1084-1088
Author(s):  
Jin Lun Chen
Keyword(s):  
Numerical Simulation ◽  
Channel Estimation ◽  
Communication Systems ◽  
Mimo System ◽  
Estimation Method ◽  
Linear Least Squares ◽  
Mimo Communication ◽  
Channel Estimate ◽  
Wide Range ◽  
Multiple Input

Accurate channel estimation plays a key role in multiple-input and multiple-output (MIMO) communication systems. In this paper, we firstly discuss the popular linear least squares (LS) channel estimation and the multiple LS (MLS) channel estimation. Then an adaptive multiple LS (AMLS) channel estimate approach is proposed. Using numerical simulation, it is found that the proposed estimation method outperforms LS and MLS for a wide range of training SNRs.

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