Enhanced CSI Acquisition for FDD Multi-User Massive MIMO Systems

Zhang, Fangchao; Sun, Shaojun; Gao, Qiubin; Tang, Wanwei

doi:10.1109/access.2018.2828979

Cited by 10 publications

(19 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Online Runningmentioning

confidence: 99%

“…where h (1) u and h (2) u are the outputs of despreading and interference reduction (from UL-US), respectively. In TABLE 2, h (1) u and h (2) u can be obtained according to step 2) and step 6), respectively. Specially, h (2) u is viewed as the estimated downlink CSI according to the proposed ELM-based network in this paper.…”

Section: Online Runningmentioning

confidence: 99%

“…In TABLE 2, h (1) u and h (2) u can be obtained according to step 2) and step 6), respectively. Specially, h (2) u is viewed as the estimated downlink CSI according to the proposed ELM-based network in this paper.…”

Section: Online Runningmentioning

confidence: 99%

“…u and d (2) u are used to serve the UL-US's detections of DET-ELM1 and DET-ELM2, respectively. Detection of UL-US: Based on the captured d (i) u , i = 1, 2, the UL-US can be detected by using DET-ELMi, which can be expressed as…”

Section: Online Runningmentioning

confidence: 99%

“…In (15), d (1) u and d (2) u are detected according to step 5) and step 9) in TABLE 2, respectively. In the proposed ELM-based network, d…”

Section: Online Runningmentioning

confidence: 99%

See 4 more Smart Citations

ELM-Based Superimposed CSI Feedback for FDD Massive MIMO System

et al. 2020

View full text Add to dashboard Cite

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO), deep learning (DL)-based superimposed channel state information (CSI) feedback has presented promising performance. However, it is still facing many challenges, such as the high complexity of parameter tuning, large number of training parameters, and long training time, etc. To overcome these challenges, an extreme learning machine (ELM)-based superimposed CSI feedback is proposed in this paper, in which the downlink CSI is spread and then superimposed on uplink user data sequence (UL-US) to feed back to base station (BS). At BS, an ELM-based network is constructed to recover both downlink CSI and UL-US. In the constructed ELM-based network, we employ the simplified versions of ELM-based subnets to replace the subnets of DL-based superimposed feedback, yielding less training parameters. Besides, the input weights and hidden biases of each ELM-based subnet are loaded from the same matrix by using its full or partial entries, which significantly reduces the memory requirement. With similar or better recovery performances of downlink CSI and UL-US, the proposed ELM-based method has less training parameters, storage space, offline training and online running time than those of DL-based superimposed CSI feedback.INDEX TERMS massive multiple-input multiple-output (MIMO), channel state information (CSI), superimposed feedback, extreme learning machine (ELM). He is the author of more than 40 papers and more than 20 chinese inventions. His research interests include detection and estimation, massive MIMO systems, and deep learning in physical layer of wireless communications.

show abstract

Section: Online Runningmentioning

confidence: 99%

Section: Online Runningmentioning

confidence: 99%

Section: Online Runningmentioning

confidence: 99%

Section: Online Runningmentioning

confidence: 99%

“…In (15), d (1) u and d (2) u are detected according to step 5) and step 9) in TABLE 2, respectively. In the proposed ELM-based network, d…”

Section: Online Runningmentioning

confidence: 99%

See 3 more Smart Citations

ELM-Based Superimposed CSI Feedback for FDD Massive MIMO System

et al. 2020

View full text Add to dashboard Cite

show abstract

Robust channel estimation based on the maximum entropy principle

Hu,

Xue,

et al. 2023

Sci. China Inf. Sci.

View full text Add to dashboard Cite

Massive MIMO systems for 5G: A systematic mapping study on antenna design challenges and channel estimation open issues

Benzaghta

Rabie

2021

IET Communications

View full text Add to dashboard Cite

The next generation of mobile networks (5G) is expected to achieve high data rates, reduce latency, as well as improve the spectral and energy efficiency of wireless communication systems. Several technologies are being explored to be used in 5G systems. One of the main promising technologies that is seen to be the enabler of 5G is massive multipleinput multiple-output (mMIMO) systems. Numerous studies have indicated the utility of mMIMO in upcoming wireless networks. However, there are several challenges that needs to be unravelled. In this paper, the latest progress of research on challenges in mMIMO systems is tracked, in the context of mutual coupling, antenna selection, pilot contamination and feedback overhead. The results of a systematic mapping study performed on 63 selected primary studies, published between the year 2017 till the second quarter of 2020, are presented. The main objective of this secondary study is to identify the challenges regarding antenna design and channel estimation, give an overview on the state-of-the-art solutions proposed in the literature, and finally, discuss emerging open research issues that need to be considered before the implementation of mMIMO systems in 5G networks. INTRODUCTIONThe next-generation of mobile networks, also known as the fifth-generation (5G) is foreseen to deliver a solution for bandwidth-hungry application, due to the dramatic increase in traffic demand of users. 5G aims to meet much higher data rates with fast connectivity, more robust reliability, spectral efficiency (SE) and energy efficiency (EE) [1]. However, there are technical requirements and challenging issues that 5G needs to overcome so that it can be implementable. One of the major challenges is that the new generation must support much higher data rate (approximately 1000× compared to legacy networks), which makes it essential to go to higher frequencies, 30-300 GHz (Mm-Wave) spectrum, to allow more bandwidth. The use of the mm-wave spectrum requires the use of many antennas allowing more throughput gain in the spatial dimension to overcome high path loss. Furthermore, smaller cells provided by the use of these many antenna are needed to avoid interference [2]. Recently, massive multiple-input multiple-output (mMIMO) has become the most promising wireless technology and the This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Enhanced CSI Acquisition for FDD Multi-User Massive MIMO Systems

Cited by 10 publications

References 16 publications

ELM-Based Superimposed CSI Feedback for FDD Massive MIMO System

ELM-Based Superimposed CSI Feedback for FDD Massive MIMO System

Robust channel estimation based on the maximum entropy principle

Massive MIMO systems for 5G: A systematic mapping study on antenna design challenges and channel estimation open issues

Contact Info

Product

Resources

About