An energy‐efficient joint antenna and user selection algorithm for multi‐user massive MIMO downlink

Olyaee, Maryam; Eslami, Mohsen; Haghighat, Javad

doi:10.1049/iet-com.2017.0905

Cited by 25 publications

(15 citation statements)

References 21 publications

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“…• Propagation models: In the literature, most work on massive MIMO show that as the number of antennas increases, under favourable propagation conditions, each user channels are spatially not correlated and their channel vectors asymptotically become pairwise orthogonal [92]. The authors in [100] demonstrate that the antenna correlation coefficients are extensively greater than what should be expected under independent and identically distributed (i.i.d.) channel assumptions.…”

Section: ) Massive Mimo Technologymentioning

confidence: 99%

Combination of Ultra-Dense Networks and Other 5G Enabling Technologies: A Survey

2020

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Recently, to address the astonishing capacity requirement of 5G, researchers are investigating the possibility of combining different technologies with ultra-dense networks (UDNs). However, the ultradense deployment of small cells in the coverage area of conventional macrocells known as UDNs introduces new technical challenges such as severe interference, unfairness in radio resource sharing, unnecessary handover, a significant increase in energy consumption, and degraded quality-of-service (QoS). To overcome these challenges and achieve the performance requirements in 5G, there is a need to combine UDNs with other 5G enabling technologies and then, design intelligent management techniques for better performance of the overall networks. Hence, in this paper, we present a comprehensive survey on different generations of wireless networks, 5G new radio (NR) standards, 5G enabling technologies and the importance of combining UDNs with other 5G technologies. Also, we present an extensive overview of the recent advances and research challenges in intelligent management techniques and backhaul solutions in the last five years for the combination of UDNs and other enabling technologies that offers the visions of 5G. We summarise the mathematical tools widely exploited in solving these problems and the performance metrics used to evaluate the intelligent management algorithms. Moreover, we classify various intelligent management algorithms according to the adopted enabling technologies, benefits, challenges addressed, mathematical tools and performance metrics used. Finally, we summarise the open research challenges, provide design guidelines and potential research directions for the development of intelligent management techniques and backhaul solutions for the combination of UDNs and other 5G technologies. INDEX TERMS Macrocells, small cells, ultra-dense networks, QoS.

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Section: ) Massive Mimo Technologymentioning

confidence: 99%

Combination of Ultra-Dense Networks and Other 5G Enabling Technologies: A Survey

2020

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“…The authors in [24] have proposed a heuristic JAUS algorithm for sum rate maximization, in which starting with all antennas activated, the antennas are deactivated one by one in a greedy manner until the number of active antennas equals the number of RF chains, while user selection is conducted using the semi-orthogonal user selection algorithm (SUS) [27] that selects a user subset in such a way that their channel orthogonality becomes maximized. In [25], the authors have developed another heuristic JAUS algorithm for maximizing the sum rate per unit energy consumption, in which users with superior channels are selected first, and then antennas with the least contributions to the sum rate are iteratively deleted until the number of active antennas equals the number of RF chains. In [26], the authors have developed a probabilitybased JAUS algorithm that finds the probabilities that each antenna and each user will be activated, respectively, with the aim of maximizing the sum capacity.…”

Section: Introductionmentioning

confidence: 99%

Joint Antenna and User Scheduling in the Massive MIMO System Over Time-Varying Fading Channels

2021

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Massive multiple-input multiple-output (MIMO) technology, mainly equipped with dozens or even hundreds of antennas at transmitter and/or receiver, is one of the most important technologies in 5G/6G era due to its capability of achieving high transmission rate. However, since each transmit antenna typically needs a complete radio frequency (RF) chain, a large number of RF chains need to be installed accordingly, resulting in high economic cost, high hardware complexity, and high power consumption. To resolve these problems, architectures where a small number of RF chains are installed have been proposed in recent years, and an antenna selection technique that activates antennas only as many as the number of RF chains has been envisioned as one of solutions. In this paper, we study the joint antenna and user scheduling problem for the downlink massive MIMO system over time-varying fading channels to maximize the weighted average sum rate while ensuring users' minimum average data rate requirements. To solve the problem, we first develop an opportunistic joint antenna and user scheduling algorithm (OJAUS) using the dual and stochastic subgradient methods, which makes it possible to schedule antennas and users without any underlying distributions of the fading channels. However, it requires solving a joint antenna and user selection (JAUS) problem to maximize the instantaneous weighted sum rate in every time slot. Thus, we additionally develop a simple heuristic JAUS algorithm with low computational complexity, called JAUS-LCC, which is executed in every time slot within OJAUS. Finally, through simulation results, we first show that our JAUS-LCC provides near-optimal performance despite requiring very low computational complexity, and then show that our OJAUS with JAUS-LCC well guarantees given minimum average data rate requirements.INDEX TERMS Antenna selection, multiple-input multiple-output (MIMO), quality-of-service (QoS), timevarying fading channels, user scheduling, weighted sum rate maximization, zero-forcing (ZF) precoding.

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“…The well‐known user‐scheduling techniques, as well as multiple‐input multiple‐output (MIMO) systems, are two efficient ways to exploit user and spatial diversities, and several research works have been conducted to evaluate the efficiency of the proposed methods, e.g. [15, 16]. Several interesting studies have been carried out to investigate the achievements obtained from spectrum‐sharing in CRN utilising MIMO and orthogonal space‐time block coding (OSTBC) with uncorrelated antennas, e.g.…”

Section: Introductionmentioning

confidence: 99%

Analysis and outage performance evaluation of a fair scheduling for independent non‐identically distributed users in a cognitive radio using OSTBC with equally correlated transmit antennas

Torabi

Haccoun

2020

IET Communications

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An energy‐efficient joint antenna and user selection algorithm for multi‐user massive MIMO downlink

Cited by 25 publications

References 21 publications

Combination of Ultra-Dense Networks and Other 5G Enabling Technologies: A Survey

Combination of Ultra-Dense Networks and Other 5G Enabling Technologies: A Survey

Joint Antenna and User Scheduling in the Massive MIMO System Over Time-Varying Fading Channels

Analysis and outage performance evaluation of a fair scheduling for independent non‐identically distributed users in a cognitive radio using OSTBC with equally correlated transmit antennas

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