6G Communication: Envisioning the Key Issues and Challenges

Nayak, Sabuzima; Patgiri, Ripon

doi:10.4108/eai.11-11-2020.166959

Cited by 54 publications

(27 citation statements)

References 23 publications

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“…6G vehicular networks will be using emerging technology such as edge and artificial intelligence in its network nodes that require high energy [10], and they will have ultra-high throughput, ultra-wide bandwidth, and ultralarge-scale ubiquitous wireless nodes, which will pose a huge challenge to energy consumption [149]. These networks may face stringent requirements to achieve green vehicular communications which enable energy conservation, decreased emissions, and lower environmental pollution [150].…”

Section: Green Vehicular Networkmentioning

confidence: 99%

“…Furthermore, 6G aims to connect any smart device, from smartphones to intelligent vehicles, to the Internet. It will deliver innovative and highquality services like holographic communication, augmented reality/virtual reality, and many others [10]. In addition, It will focus on Quality of Experience (QoE) to provide rich experiences from 6G technology.…”

Section: Introductionmentioning

confidence: 99%

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Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

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Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performance under uncertainty. To achieve this goal, Reinforcement Learning (RL) can effectively solve decision-making problems. However, the state and action spaces are massive and complex in large-scale wireless networks. Hence, RL may not be able to find the best strategy in a reasonable time. Therefore, Deep Reinforcement Learning (DRL) has been developed to combine RL with Deep Learning (DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief overview of RL and DRL concepts. Then we review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. We finally discuss and highlight some unresolved challenges for further study.

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Section: Green Vehicular Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

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“…As aforementioned, 6G is expected to fulfill more stringent requirements on transmission capacity, reliability, latency, coverage, energy consumption, and connection density [2,3]. Specifically, 6G should support the following typical usage scenarios: (1) Ultra-massive machine-type communications (umMTC): the device connectivity density is larger than 10 7 devices/km 2 [2]; (2) Extremely reliable and low-latency communications (ERLLC): the reliability should be higher than 99.9999% and the latency should be smaller than 100 µs [133]; (3) Further-enhanced mobile broadband (FeMBB): the peak data rate should be at least 1 Tb/s and the user-experienced data rate should be higher than 1 Gb/s [2]; (4) Extremely low-power communications (ELPC): the energy efficiency should be ten or hundred times of 5G.…”

Section: Potential Applications In 6g Networkmentioning

confidence: 99%

Reconfigurable intelligent surfaces for smart wireless environments: channel estimation, system design and applications in 6G networks

Liang

Chen

Long

et al. 2021

Sci. China Inf. Sci.

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Reconfigurable intelligent surface (RIS), one of the key enablers for the sixth-generation (6G) mobile communication networks, is considered by designers to smartly reconfigure the wireless propagation environment in a controllable and programmable manner. Specifically, an RIS consists of a large number of low-cost and passive reflective elements (REs) without radio frequency chains. The system gain of RIS wireless systems can be achieved by adjusting the phase shifts and amplitudes of the REs so that the desired signals can be added constructively at the receiver. However, an RIS typically has limited signal processing capability and cannot perform active transmitting/receiving in general, which leads to new challenges in the physical layer design of RIS wireless systems. In this paper, we provide an overview of the RIS-aided wireless systems, including the reflection principle, channel estimation, and system design. In particular, two types of emerging RIS systems are considered: RIS-aided wireless communications (RAWC) and RISbased information transmission (RBIT), where the RIS plays the role of the reflector and the transmitter, respectively. We also envision the potential applications of RIS in 6G networks.

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“…Nevertheless, there are some challenges and difficulties that might face the practical implementation of this use case on a wider scale. This includes the physical installation of the unlimited number of energy nodes to increase the resolution, which also means massive capacity demand and the need for increasing the spectrum bandwidth, implementing new modulation techniques, and others, to overcome this [80].…”

Section: Challenges and Future Directions: An Evaluation Of The Wireless Electricity Data Logger Systemmentioning

confidence: 99%

6G Opportunities Arising from Internet of Things Use Cases: A Review Paper

et al. 2021

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The race for the 6th generation of wireless networks (6G) has begun. Researchers around the world have started to explore the best solutions for the challenges that the previous generations have experienced. To provide the readers with a clear map of the current developments, several review papers shared their vision and critically evaluated the state of the art. However, most of the work is based on general observations and the big picture vision, and lack the practical implementation challenges of the Internet of Things (IoT) use cases. This paper takes a novel approach in the review, as we present a sample of IoT use cases that are representative of a wide variety of its implementations. The chosen use cases are from the most research-active sectors that can benefit from 6G and its enabling technologies. These sectors are healthcare, smart grid, transport, and Industry 4.0. Additionally, we identified some of the practical challenges and the lessons learned in the implementation of these use cases. The review highlights the cases’ main requirements and how they overlap with the key drivers for the future generation of wireless networks.

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6G Communication: Envisioning the Key Issues and Challenges

Cited by 54 publications

References 23 publications

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Reconfigurable intelligent surfaces for smart wireless environments: channel estimation, system design and applications in 6G networks

6G Opportunities Arising from Internet of Things Use Cases: A Review Paper

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