Impact and compensation of I/Q imbalance on channel reciprocity of time-division-duplexing multiple-input multiple-output systems

2013 ◽  
Vol 7 (7) ◽  
pp. 663-672 ◽  
Author(s):  
Zu-Jun Liu ◽  
De-Chun Sun ◽  
Ke-Chu Yi ◽  
Jie-Ling Wang
Keyword(s):  
Multiple Input Multiple Output ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Time Division ◽  
Time Division Duplexing ◽  
Channel Reciprocity

scholarly journals Emulating Realistic Bidirectional Spatial Channels for MIMO OTA Testing

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Fan ◽  
Pekka Kyösti ◽  
Jesper Ø. Nielsen ◽  
Lassi Hentilä ◽  
Gert F. Pedersen
Keyword(s):  
Communication Systems ◽  
Experimental Validation ◽  
Multiple Input Multiple Output ◽  
Line Of Sight ◽  
Channel Models ◽  
Bidirectional Communication ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Time Division ◽  
Space Line

This paper discusses over the air (OTA) testing for multiple input multiple output (MIMO) capable terminals with emphasis on modeling bidirectional spatial channel models in multiprobe anechoic chamber (MPAC) setups. In the literature, work on this topic has been mainly focused on how to emulate downlink channel models, whereas uplink channel is often modeled as free space line-of-sight channel without fading. Modeling realistic bidirectional (i.e., both uplink and downlink) propagation environments is essential to evaluate any bidirectional communication systems. There have been works stressing the importance of emulating full bidirectional channel and proposing possible directions to implement uplink channels in the literature. Nevertheless, there is no currently published work reporting an experimental validation of such concepts. In this paper, a general framework to emulate bidirectional channels for time division duplexing (TDD) and frequency division duplexing (FDD) communication systems is proposed. The proposed technique works for MPAC setups with arbitrary uplink and downlink probe configurations, that is, possibly different probe configurations (e.g., number of probes or their configurations) in the uplink and downlink. The simulation results are further supported by measurements in a practical MPAC setup. The proposed algorithm is shown to be a valid method to emulate bidirectional spatial channel models.

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 Pilot Decontamination Using Asynchronous Fractional Pilot Scheduling in Massive MIMO Systems

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6213
Author(s):  
Muhammad Irshad Zahoor ◽  
Zheng Dou ◽  
Syed Bilal Hussain Shah ◽  
Imran Ullah Khan ◽  
Sikander Ayub ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Ideal Number ◽  
Sum Rate ◽  
Inter Cell Interference ◽  
Time Division

Due to large spectral efficiency and low power consumption, the Massive Multiple-Input-Multiple-Output (MIMO) became a promising technology for the 5G system. However, pilot contamination (PC) limits the performance of massive MIMO systems. Therefore, two pilot scheduling schemes (i.e., Fractional Pilot Reuse (FPR) and asynchronous fractional pilot scheduling scheme (AFPS)) are proposed, which significantly mitigated the PC in the uplink time division duplex (TDD) massive MIMO system. In the FPR scheme, all the users are distributed into the central cell and edge cell users depending upon their signal to interference plus noise ratio (SINR). Further, the capacity of central and edge users is derived in terms of sum-rate, and the ideal number of the pilot is calculated which significantly maximized the sum rate. In the proposed AFPS scheme, the users are grouped into central users and edge users depending upon the interference they receive. The central users are assigned the same set of pilots because these users are less affected by interference, while the edge users are assigned the orthogonal pilots because these users are severely affected by interference. Consequently, the pilot overhead is reduced and inter-cell interference (ICI) is minimized. Further, results verify that the proposed schemes outperform the previous proposed traditional schemes, in terms of improved sum rates.

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.

MIMO Beamforming

2009 ◽  
pp. 240-263
Author(s):  
Qinghua Li ◽  
Xintian Eddie Lin ◽  
Jianzhong ("Charlie") Zhang
Keyword(s):  
Multiple Input Multiple Output ◽  
Industrial Applications ◽  
Antenna System ◽  
Limiting Factor ◽  
Transmit Beamforming ◽  
Underlying Principle ◽  
Input Multiple Output ◽  
Time Division ◽  
Channel Reciprocity ◽  
Mimo Beamforming

Transmit beamforming improves the performance of multiple-input multiple-output antenna system (MIMO) by exploiting channel state information (CSI) at the transmitter. Numerous MIMO beamforming schemes are proposed in open literature and standard bodies such as 3GPP, IEEE 802.11n and 802.16d/e. This chapter describes the underlying principle, evolving techniques, and corresponding industrial applications of MIMO beamforming. The main limiting factor is the cumbersome overhead to acquire CSI at the transmitter. The solutions are categorized into FDD (Frequency Division Duplex) and TDD (Time Division Duplex) approaches. For all FDD channels and radio calibration absent TDD channels, channel reciprocity is not available and explicit feedback is required. Codebook-based feedback techniques with various quantization complexities and feedback overheads are depicted in this chapter. Furthermore, we discuss transmit/receive (Tx/Rx) radio chain calibration and channel sounding techniques for TDD channels, and show how to achieve channel reciprocity by overcoming the Tx/Rx asymmetry of the RF components

scholarly journals General Signal Model for Multiple-Input Multiple-Output GMTI Radar

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2576 ◽  
Author(s):  
Fuyou Li ◽  
Feng He ◽  
Zhen Dong ◽  
Manqing Wu ◽  
Yongsheng Zhang
Keyword(s):  
Multiple Access ◽  
Multiple Input Multiple Output ◽  
Compensation Method ◽  
Fast Time ◽  
Signal Model ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Improved Performance ◽  
Code Division Multiple ◽  
Time Division

Multiple-input multiple-output (MIMO) ground moving target indication (GMTI) radar has been studied recently because of its excellent performance. In this paper, a general signal model is established for the MIMO GMTI radar with both fast-time and slow-time waveforms. The general signal model can be used to evaluate the performance of the MIMO GMTI radar with arbitrary waveforms such as the ideal orthogonal, code division multiple access (CDMA), frequency-division multiple access (FDMA), time division multiple access (TDMA), and Doppler division multiple access (DDMA) waveforms. We proposed a range-compensation method to eliminate the range-dependence of the FDMA waveforms. The simulation results indicate that the improved performance of FDMA waveforms is achieved utilizing the range-compensation method.

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