Dynamic TDD Systems for 5G and Beyond: A Survey of Cross-Link Interference Mitigation

Kim, Hye-Jin; Kim, Jintae; Hong, Daesik

doi:10.1109/comst.2020.3008765

Cited by 57 publications

(34 citation statements)

References 116 publications

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“…However, the uplink data rate challenge still exists for users with the general mobile device based MAR applications. Flexible adjustments of uplink and downlink resources enabled by dynamic Time-Division Duplexing (TDD) with cross-link interference mitigation [276] is a viable approach for MNOs to maximize the uplink capacity on-demand with the small cell deployments. This makes the network responsive based on the traffic demand in events such as a sudden increase of MAR users in a particular area.…”

Section: H Remaining Challenges and Open Issues (Deploymentmentioning

confidence: 99%

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects

Siriwardhana

Porambage

Liyanage

et al. 2021

IEEE Commun. Surv. Tutorials

404

108

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The Augmented Reality (AR) technology enhances the human perception of the world by combining the real environment with the virtual space. With the explosive growth of powerful, less expensive mobile devices, and the emergence of sophisticated communication infrastructure, Mobile Augmented Reality (MAR) applications are gaining increased popularity. MAR allows users to run AR applications on mobile devices with greater mobility and at a lower cost. The emerging 5G communication technologies act as critical enablers for future MAR applications to achieve ultra-low latency and extremely high data rates while Multi-access Edge Computing (MEC) brings enhanced computational power closer to the users to complement MAR. This paper extensively discusses the landscape of MAR through the past and its future prospects with respect to the 5G systems and complementary technology MEC. The paper especially provides an informative analysis of the network formation of current and future MAR systems in terms of cloud, edge, localized, and hybrid architectural options. The paper discusses key application areas for MAR and their future with the advent of 5G technologies. The paper also discusses the requirements and limitations of MAR technical aspects such as communication, mobility management, energy management, service offloading and migration, security, and privacy and analyzes the role of 5G technologies.

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Section: H Remaining Challenges and Open Issues (Deploymentmentioning

confidence: 99%

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects

Siriwardhana

Porambage

Liyanage

et al. 2021

IEEE Commun. Surv. Tutorials

404

108

View full text Add to dashboard Cite

show abstract

“…Following the guidelines in [207], Figure 3 shows 5G deployment options with frequency division and the supported existing architecture. Signaling techniques are capable of transmitting lower to higher data rates while considering several issues; i.e., slow beam-forming, lethargic synchronization consisting of large gaps in time slots, and pitiable channel efficiency as compared to existing techniques and conventional OFDM [208]. Generalized frequency division multiplexing (GFDM) improves the SE and weak out-of-band emission compared to the 4G network.…”

Section: Signaling Techniques For 5gmentioning

confidence: 99%

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

et al. 2021

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Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.

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“…As the DL generally has much higher transmit power than the UL, the impact of BS-to-BS interference is usually more adverse. Current research to mitigate CLI is extensive, covering clustering schemes, scheduling, and resource allocation techniques, advanced power control, MIMO beamforming techniques or coordinated UL/DL configuration, among others [8].…”

Section: Complex Interference Dynamicsmentioning

confidence: 99%

“…There is inter-numerology interference due to the use of mixed numerologies at the same time [1]. Furthermore, dynamic time division duplexing (TDD), which adds the concept of flexible slots, makes interference dynamics more intricate by adding cross-link interference [8]. The complexity is exacerbated with the existence of tropospheric ducting that augment the distance between interfering and victim nodes.…”

Section: Introductionmentioning

confidence: 99%

Emerging Tools for Link Adaptation on 5G NR and Beyond: Challenges and Opportunities

Martín-Vega¹,

Ruiz-Sicilia

Aguayo

et al. 2021

IEEE Access

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With the speeding up of the fifth generation (5G) new radio (NR) worldwide commercialization, one of the paramount questions for operators and vendors is how to optimize the radio links, considering the widely diverse scenarios envisioned. One of the key pillars of 5G has been an unprecedented flexibility on the configuration of the radio access network (RAN) on scenarios that include cellular, vehicular, and industrial networks among others. This flexibility has its main exponent on link adaptation (LA), which has evolved into a multi-domain technique where a plethora of parameters, like numerology, bandwidth part, radio frequency beam, power, modulation and coding scheme (MCS) or multiple antenna precoding can be adapted to the instantaneous link conditions. Although such enhancements open the door to a significant performance improvement, they also pose many challenges to LA optimization. In this article, we first present the signaling aspects of NR technology for multi-domain LA and the challenges that need to be faced. Then, we explore the latest advances on LA for wireless networks. We envision a combination of machine learning (ML) tools with multi-domain LA as a key enabler for 5G and beyond networks. Finally, we investigate emerging ML approaches for LA and present a promising application of ML for LA that is assessed with simulations. With this scheme, the training is performed at the network side to relieve the user equipment (UE) to do such a complex task. It is shown with simulations that our ML approach outperforms the well known outer loop link adaptation (OLLA) algorithm in terms of instantaneous block error rate (BLER), while reaching the same average spectral efficiency (SE). Interestingly, it is shown that the proposed scheme only requires 4 bits to represent the features used to train the model, which makes it suitable for implementation in real systems with limited feedback.INDEX TERMS 5G networks, link adaptation, machine learning, supervised learning.

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Dynamic TDD Systems for 5G and Beyond: A Survey of Cross-Link Interference Mitigation

Cited by 57 publications

References 116 publications

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

Emerging Tools for Link Adaptation on 5G NR and Beyond: Challenges and Opportunities

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