Beam Grouping Based User Scheduling in Multi-Cell Millimeter-Wave MIMO Systems

Hu, Anzhong

doi:10.1109/access.2018.2872516

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…The proposed algorithm is evaluated here in this selection compared with the QAFAS algorithm simulated in [1]. The simulation is performed for BS antennas M = 128 and 64 and the number of users in the network N = 10 and 20.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The noise distribution is Additive White Gaussian Noise (AWGN). The antenna selection algorithms used are the Fast Antenna Selection, Random Antenna Selection, and QAFAS algorithm proposed in [1].…”

Section: System Model and Methodologymentioning

confidence: 99%

“…The increasing demand for the wireless applications with the large number of users who need to be served with a high data rate is a key feature of modern wireless communication such as Fifth Generation (5G) [1]. Using a large number of antennas which can reach tens or hundreds of antennas at the Base Station (BS) to serve many users in the same network with the same time-frequency resource is a great physical BS development nowadays which is called Massive Multiple-Input Multiple-Output (MIMO) configuration [2].…”

Section: Introductionmentioning

confidence: 99%

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Quantization-Aware Greedy Antenna Selection for Multi-User Massive Mimo Systems

Mahdi¹,

Al-Heety²,

Hamid³

et al. 2021

PIER C

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Using multiple-Input Multiple-Output (MIMO) configuration is not new in the field of wireless communication to increase the capacity of the system. This configuration is still valid to use nowadays with the modern wireless configuration such as the Fifth generation (5G). Massive MIMO is the key resource of the 5G systems due to its huge ability to increase the capacity of the network and on the other hand its ability to enhance both spectral and transmit-energy efficiency. The need for using Massive MIMO comes from the increase in using smartphones, tablets, and the rise of the Internet of Things. This increasing demand for the use of wireless applications requires networking and Internet infrastructures to meet the needs of current and future multimedia applications which massive MIMO satisfies. The key limitation of using massive MIMO is the cost of installation of these antennas and how to multiplex between them. In addition to this, the Radio Frequency (RF) links are also increased where this increase leads to high system complexity and hardware energy consumption. Because of this, reducing the required number of RF chains is essential to use by performing antenna selection which this paper aims to evaluate without significant performance loss which can be performed by employing low-resolution Analog-to-Digital Converter (ADC) to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. In this paper, Quantization-Aware Greedy Antenna Selection (QAGAS) algorithm has been proposed and compared with other antenna selection algorithms especially simple algorithms like random selection and Fast Antenna Selection (FAS) algorithm. The achieved capacity is compared with that of a very simple scheme that selects the antennas with the highest received power. The system capacity obtained from QAGAS is evaluated related to the transmit power of the Base Station (BS) and the quantization bits used in the lowresolution ADC. The simulation is also performed for a different numbers of users served by the BS and with the number of antennas at the BS. The simulation results show that the proposed algorithm indicates a potential for significant reductions of massive MIMO implementation complexity, by reducing the number of RF links and performing antenna selection using simple algorithms.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: System Model and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantization-Aware Greedy Antenna Selection for Multi-User Massive Mimo Systems

Mahdi¹,

Al-Heety²,

Hamid³

et al. 2021

PIER C

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“…While the spectrum efficiency as one of the QoS parameters in wireless communication has been studied in Esswie and Pedersen [31] by dealing with scheduling when there is an important issue in the network. Hu [32] examines the QoS of the system by dealing with the interference comes from the channel and other users that affects the scheduling process and affect the response of the scheduling algorithm used. They evaluate the scheduling process using the greedy algorithm which limits the interference comes from the surrounding area.…”

Section: Literature Reviewmentioning

confidence: 99%

Vehicular Networks Performance Evaluation Based on Downlink Scheduling Algorithms for High-Speed Long Term Evolution – Vehicle

Mahdi

Al-Bander

Alwan

et al. 2021

Int. J. Interact. Mob. Technol.

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Moving is the key to modern life. Most things are in moving such as vehicles and user mobiles, so the need for high-speed wireless networks to serve the high demand of the wireless application becomes essential for any wireless network design. The use of web browsing, online gaming, and on-time data exchange like video calls as an example means that users need a high data rate and fewer error communication links. To satisfy this, increasing the bandwidth available for each network will enhance the throughput of the communication, but the bandwidth available is a limited resource which means that thinking about techniques to be used to increase the throughput of the network is very important. One of the techniques used is the spectrum sharing between the available networks, but the problem here is when there is no available channel to connect with. This encourages researchers to think about using scheduling as a technique to serve the high capacity on the network. Studying scheduling techniques depends on the Quality-of-Service (QoS) of the network, so the throughput performance is the metric of this paper. In this paper, an improved Best-CQI scheduling algorithm is proposed to enhance the throughput of the network. The proposed algorithm was compared with three user scheduling algorithms to evaluate the throughput performance which are Round Robin (RR), Proportional Fair (PF), and Best-CQI algorithms. The study is performed under Line-of-Sight (LoS) link at carrier frequency 2.6 GHz to satisfy the Vehicular Long Term Evolution (LTE-V) with the high-speed scenario. The simulation results show that the proposed algorithm outperforms the throughput performance of the other algorithms.

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