Limited Feedback MU-MIMO in LTE-A System: Concepts, Performance, and Future Works

Behjati, Mehran; Ismail, Mahamod; Nordin, Rosdiadee

doi:10.1007/s11277-015-2669-7

Cited by 11 publications

(3 citation statements)

References 55 publications

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“…Beamforming is directly linked with the improvement of signal quality at the receiver end. When this attribute is coupled with high energy efficiency, the digital beamforming method can use the standards of massive MIMO technology to enhance spectral efficiency [17]. To elaborate on this, in a standard beamforming synthesis for massive MIMO, the power radiated by a single-antenna element is limited, which leads to energy efficiency [18,19].…”

Section: Digital Beamformermentioning

confidence: 99%

Beamformer development challenges for 5G and beyond

Abbasi

Fusco

2020

Antennas and Propagation for 5G and Beyond

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Antenna's beamforming technology [1] is vital in the utilization of the microwave and millimeter-wave (mmWave) frequency bands for the fifth-generation (5G) communication technology, and beyond, applications that include the sixth generation (6G), Internet of Things and Industry 4.0 [2,3]. Antenna beamforming can be defined as the ability of an antenna array to steer the maximum radiation toward a prescribed direction, or, conversely, the ability of the antenna array to estimate the direction of arrival (DOA) of an impinging signal. Beamforming is generally achieved by using multiple antennas, spatially colocated to either function as an independent radiator or as a unit cell in larger array arrangements. Recently, the term multiple-input-multiple-output (MIMO) has been used in conjugation with the beamforming technology since it also involves multiple antenna systems, thus linking it to the beamforming technology [4]. When the number of antennas used in the MIMO operation becomes very large, it is generally termed massive MIMO technology [5]. Different beamforming concepts are used for the two frequency spectrum classifications in cellular technology, i.e., sub-6 GHz and mmWave. The majority of beamformer challenges at sub-6 GHz are already resolved, and prototypes are now available [6]; however, there are still major fundamental challenges that engineers face while developing beamformers for use in the mmWave bands, i.e., >28 GHz. Challenges include, but are not limited to, high path loss, power generation, adaption to account for realistic channel estimation, hardware impairments, energy leakage in circuits, high development cost, spatial-temporal channel variations, signal blockages due to the presence of obstacles such as beamformer casing, user body and hand. Also, multiuser MIMO techniques are not easily implementable in mmWave frequencies. In addition to this, beam coordination at transmitter and receiver antenna systems especially in mmWave spectrum is very challenging. In this chapter, we will first classify the beamformers based on the system architecture, operational frequency

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Section: Digital Beamformermentioning

confidence: 99%

Beamformer development challenges for 5G and beyond

Abbasi

Fusco

2020

Antennas and Propagation for 5G and Beyond

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“…One can define this parameter with reference to peak effective power radiation in desired direction. In an antenna radiation patter, in the main lobe angle between two half power points or -3DB point is referred as HPBW [8]. This angle should be minimum value.…”

Section: B Half Power Bandwidth (Hpbw)mentioning

confidence: 99%

Massive MIMO and Hybrid Optimization for It's Beamforming

Patel*¹,

Bhatt²

2019

IJRTE

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The tremendous growth of traffic in wireless communication (WC) system has resulted in inadequate network capacity. 5 th Generation (5G) is seen as next generation wireless communication system implemented with massive multiple-input multiple-output (M-MIMO) technology. It will play major role in future communication system. M-MIMO objective is high throughput and high speed. Beamforming technique is a key to high throughput objective -achieved by reduction in errors occurring in data transmission and reception. The intent of this research paper is to review -beam forming techniques implemented in M-MIMO and research work in this particular area. Paper classifies optimized beamforming techniques in detail for determining appropriate techniques that can be deployed in M-MIMO. Understanding limitations of present techniques and suggesting new approach for better throughput is outcome of retrospective analysis.

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“…The main question is whether a cellular network can provide a reliable communication link to ensure that UAVs can fly BVLOS safely. In general, the existing 4G and 5G cellular networks provide ubiquitous coverage with low latency and a high-speed data rate for terrestrial users [ 10 ], whereas, at the altitudes below base stations’ (BSs) heights, the existing cellular networks can meet the CC and payload communication requirements to provide safe mobile connectivity for UAVs [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach

Behjati

Zulkifley

Alobaidy

et al. 2022

Sensors

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The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aerial coverage under some circumstances, which mainly depends on the distance between the UAV and BS and flight height. The performance evaluation shows that the proposed RSRP and RSRQ models achieved RMSE of 4.37 dBm and 2.71 dB for testing samples, respectively.

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Limited Feedback MU-MIMO in LTE-A System: Concepts, Performance, and Future Works

Cited by 11 publications

References 55 publications

Beamformer development challenges for 5G and beyond

Beamformer development challenges for 5G and beyond

Massive MIMO and Hybrid Optimization for It's Beamforming

Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach

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