scholarly journals Limited Feedback Precoding for Massive MIMO

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Xin Su ◽  
Jie Zeng ◽  
Jingyu Li ◽  
Liping Rong ◽  
Lili Liu ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Limited Feedback ◽  
Antenna System ◽  
Base Station ◽  
Array Antenna ◽  
Codebook Design ◽  
Input Multiple Output ◽  
Scenarios Simulation

The large-scale array antenna system with numerous low-power antennas deployed at the base station, also known as massive multiple-input multiple-output (MIMO), can provide a plethora of advantages over the classical array antenna system. Precoding is important to exploit massive MIMO performance, and codebook design is crucial due to the limited feedback channel. In this paper, we propose a new avenue of codebook design based on a Kronecker-type approximation of the array correlation structure for the uniform rectangular antenna array, which is preferable for the antenna deployment of massive MIMO. Although the feedback overhead is quite limited, the codebook design can provide an effective solution to support multiple users in different scenarios. Simulation results demonstrate that our proposed codebook outperforms the previously known codebooks remarkably.

scholarly journals Downlink Spectral Efficiency Analysis in Distributed Massive MIMO with Phase Noise

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 317 ◽  
Author(s):  
Qian Lv ◽  
Jiamin Li ◽  
Pengcheng Zhu ◽  
Dongming Wang ◽  
Xiaohu You
Keyword(s):  
Phase Noise ◽  
Spectral Efficiency ◽  
Massive Mimo ◽  
Large Scale ◽  
Channel Model ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Small Scale ◽  
Multiple Input ◽  
Input Multiple Output

To achieve the advantages provided by massive multiple-input multiple-output (MIMO), a large number of antennas need to be deployed at the base station. However, for the reason of cost, inexpensive hardwares are employed in the realistic scenario, which makes the system distorted by hardware impairments. Hence, in this paper, we analyze the downlink spectral efficiency in distributed massive MIMO with phase noise and amplified thermal noise. We provide an effective channel model considering large-scale fading, small-scale fast fading and phase noise. Based on the model, the estimated channel state information (CSI) is obtained during the pilot phase. Under the imperfect CSI, the closed-form expressions of downlink achievable rates with maximum ratio transmission (MRT) and zero-forcing (ZF) precoders in distributed massive MIMO are derived. Furthermore, we also give the user ultimate achievable rates when the number of antennas tends to infinity with both precoders. Based on these expressions, we analyze the impacts of phase noise on the spectral efficiency. It can be concluded that the same limit rate is achieved with both precoders when phase noise is present, and phase noise limits the spectral efficiency. Numerical results show that ZF outdoes MRT precoder in spectral efficiency and ZF precoder is more affected by phase noise.

scholarly journals Deep Learning Based Analog Beamforming Design for Millimetre Wave Massive MIMO System

2021 ◽  
Author(s):  
Rajdeep Singh Sohal ◽  
Vinit Grewal ◽  
Jaipreet Kaur ◽  
Maninder Lal Singh
Keyword(s):  
Deep Learning ◽  
Massive Mimo ◽  
Large Scale ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Design Procedure ◽  
Base Station ◽  
Small Scale ◽  
Millimetre Wave ◽  
Input Multiple Output

Abstract Analog beamforming (ABF) architectures for both large-scale antennas at base station (BS) and small-scale antennas at user side in millimetre wave (mmWave) channel are constructed and investigated in this paper with the aid of deep learning (DL) techniques. Transmit and receive beamformers are selected through offline training of ABF network that accepts input as channel. The joint optimization of both beamformers based on DL for maximization of spectral efficiency (SE) for massive multiple input multiple output (M-MIMO) system has been employed. This design procedure is carried out under imperfect channel state information (CSI) conditions and the proposed design of precoders and combiners shows robustness to imperfect CSI. The simulation results verify the superiority in terms of SE of deep neutral network (DNN) enabled beamforming (BF) design of mmWave massive MIMO system compared with the conventional BF algorithms while lessening the computational complexity.

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 Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1844
Author(s):  
Minhoe Kim ◽  
Woongsup Lee ◽  
Dong-Ho Cho
Keyword(s):  
Communication Systems ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Q Learning ◽  
Multiple Input ◽  
Beam Selection ◽  
Input Multiple Output ◽  
Optimal Beam ◽  
Resource Allocation Scheme

In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

scholarly journals A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 164 ◽  
Author(s):  
Zahra Mokhtari ◽  
Maryam Sabbaghian ◽  
Rui Dinis
Keyword(s):  
Massive Mimo ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo Systems ◽  
Low Cost ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Aging Effects ◽  
Hardware Impairments ◽  
Single Carrier ◽  
Input Multiple Output

Massive multiple input multiple output (MIMO) technology is one of the promising technologies for fifth generation (5G) cellular communications. In this technology, each cell has a base station (BS) with a large number of antennas, allowing the simultaneous use of the same resources (e.g., frequency and/or time slots) by multiple users of a cell. Therefore, massive MIMO systems can bring very high spectral and power efficiencies. However, this technology faces some important issues that need to be addressed. One of these issues is the performance degradation due to hardware impairments, since low-cost RF chains need to be employed. Another issue is the channel estimation and channel aging effects, especially in fast mobility environments. In this paper we will perform a comprehensive study on these two issues considering two of the most promising candidate waveforms for massive MIMO systems: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain processing (SC-FDP). The studies and the results show that hardware impairments and inaccurate channel knowledge can degrade the performance of massive MIMO systems extensively. However, using suitable low complex estimation and compensation techniques and also selecting a suitable waveform can reduce these effects.

scholarly journals A Study of a Competitive Reinforcement Learning Approach for Joint Spatial Division and Multiplexing in Massive MIMO

2021 ◽  
Vol 19 (1) ◽  
pp. 83-93
Author(s):  
Tanyaluk Deeka ◽  
Boriboon Deeka ◽  
Surajate On-rit
Keyword(s):  
Massive Mimo ◽  
Energy Use ◽  
Multiple Input Multiple Output ◽  
Main Idea ◽  
Base Station ◽  
Machine Learning Algorithms ◽  
System Throughput ◽  
Computational Time ◽  
User Grouping ◽  
Input Multiple Output

Massive Multiple-Input Multiple-Output (MIMO) is widely considered a pivotal communication technology for future generations of wireless networks. Massive MIMO uses a large number of antennas at the base station, which offers better effectiveness in spectral and energy use. However, a Frequency Division Duplex (FDD) system is challenging in reciprocity since it is difficult to estimate channels and requires feeding back channel state information. Joint Spatial Division and Multiplexing (JSDM) is a simplified FDD technique to provide massive MIMO gains. The main idea of JSDM is related to grouping users with approximately similar channel covariance. Many machine learning algorithms have been applied to conduct user grouping. In this paper, to improve the user grouping, we employ Reinforcement Guided Competitive Learning (RGCL) to the user grouping and then compare it with clustering techniques, including K-means, and sequential K-means to achieve the appropriate user grouping. The experimental results show that the RGCL technique represents better performance in computational time and system throughput than the other two above mentioned techniques, since RGCL can avoid being trapping in local minima.

A Review of Massive Multiple Input Multiple Output for 5G Communication: Benefits and Challenges

2020 ◽  
Vol 1 (1) ◽  
pp. 22-26
Author(s):  
Shaik Nilofer ◽  
Keyword(s):  
Wireless Communication ◽  
Channel Capacity ◽  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
System Model ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Primary Advantage ◽  
Time Division

Massive MIMO (mMIMO) systems become a primary advantage to overcome the problem of bandwidth restrictions. It improves the channel capacity of remote systems.The paper reviews about mMIMO systems. mMIMO consists of several number of antennas at base station (BS) which improves spectrum efficacy. The extra benefit of the mMIMO system is that the components cost is low because of utilization of less power components. The paper also discusses about the channel estimation at the BS and generally time division mode (TDD) is assumed for mMIMO systems. The paper also discusses system model, benefits for 5G wireless communication and its challenges.

scholarly journals Secure Massive MIMO System with Two-Way Relay Cooperative Transmission in 6G Networks

2020 ◽  
Author(s):  
Yumeng Su ◽  
Hongyuan Gao ◽  
Shibo Zhang
Keyword(s):  
Massive Mimo ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Cooperative Transmission ◽  
Transmission Mode ◽  
Security Issue ◽  
Legitimate User ◽  
Input Multiple Output ◽  
Multiple Relay

Abstract With the advent of Internet of Everything (IoE) and the era of big data, massive multiple-input multiple-output (MIMO) is considered an essential technology to meet the growing communication requirements for beyond 5G and the forthcoming 6G networks. This paper considers a secure massive MIMO system, where the legitimate user and the base station (BS) exchange messages via two-way relays with the presence of passive eavesdroppers. To achieve the trade-off between the physical-layer security and communication reliability, we design a cooperative transmission mode based on multiple-relay collaboration, where some relays broadcast the received signals and other relays act as friendly jammers to prevent the interception by eavesdroppers. A quantum chemical reaction optimization (QCRO) algorithm is proposed to find the most suitable scheme for multiple-relay collaboration. Simulation results highlight excellent performance of the proposed transmission mode under QCRO in different communication scenarios, which can be considered a potential solution for the security issue in future wireless networks.

scholarly journals Performance enhancement of maximum ratio transmission in 5G system with multi-user multiple-input multiple-output

2022 ◽  
Vol 12 (2) ◽  
pp. 1650
Author(s):  
Sarmad K. Ibrahim ◽  
Saif A. Abdulhussien
Keyword(s):  
Massive Mimo ◽  
Orthogonal Frequency Division Multiplexing ◽  
Performance Enhancement ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Standard System ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ratio Transmission ◽  
Maximum Ratio Transmission

<span>The downlink multi-user precoding of the multiple-input multiple-output (MIMO) method includes optimal channel state information at the base station and a variety of linear precoding (LP) schemes. Maximum ratio transmission (MRT) is among the common precoding schemes but does not provide good performance with massive MIMO, such as high bit error rate (BER) and low throughput. The orthogonal frequency division multiplexing (OFDM) and precoding schemes used in 5G have a flaw in high-speed environments. Given that the Doppler effect induces frequency changes, orthogonality between OFDM subcarriers is disrupted and their throughput output is decreased and BER is decreased. This study focuses on solving this problem by improving the performance of a 5G system with MRT, specifically by using a new design that includes weighted overlap and add (WOLA) with MRT. The current research also compares the standard system MRT with OFDM with the proposed design (WOLA-MRT) to find the best performance on throughput and BER. Improved system results show outstanding performance enhancement over a standard system, and numerous improvements with massive MIMO, such as best BER and throughput. Its approximately 60% more throughput than the traditional systems. Lastly, the proposed system improves BER by approximately 2% compared with the traditional system.</span>

scholarly journals Capacity Analysis and Linear Detection in Massive MIMO for 5G Wireless Systems

2019 ◽  
Vol 8 (9) ◽  
pp. 2938-2943
Keyword(s):  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
Symbol Error Rate ◽  
Base Station ◽  
Matlab Simulation ◽  
Maximum Ratio Combining ◽  
Single Antenna ◽  
Air Interface ◽  
Multiple Input ◽  
Input Multiple Output

Multiple Input Multiple Output (MIMO) is an attractive air interface solution which is used in the 4 th generation wireless networks to achieve higher data rate. With a very large antenna array in Massive MIMO the capacity will increase drastically. In this paper channel capacity comparison for MIMO using known Channel State Information (CSI) and unknown CSI has been carried out for a higher number of antennas at transmitter and receiver side. It has shown that at lower SNR known CSI will give better performance compared to unknown CSI. At higher SNR known CSI and unknown CSI will provide similar results. Capacity comparison has been evaluated with help of MATLAB for known CSI and unknown CSI from a small number of antennas to hundred of antennas. Also, the performance evaluated with MATLAB simulation of linear detectors zero-forcing (ZF) and maximum ratio combining (MRC) method for large number of antennas at Base station (BS) which are serving a small number of single antenna users. Performance is evaluated in terms of Symbol Error Rate (SER) for ZF and MRC, and results show that ZF will outperform MRC. It has also been analyzed that increasing the antennas at BS for a small number of users will also help to reduce SER.

Sign in / Sign up

Export Citation Format

Share Document