Integrated Millimeter Wave and Sub-6 GHz Wireless Networks: A Roadmap for Joint Mobile Broadband and Ultra-Reliable Low-Latency Communications
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Abstract (arXiv)
Next-generation wireless networks must enable emerging technologies such as augmented reality and connected autonomous vehicles via wide range of wireless services that span enhanced mobile broadband (eMBB), as well as ultra-reliable low-latency communication (URLLC). Existing wireless systems that solely rely on the scarce sub-6 GHz, microwave (μW) frequency bands will be unable to meet such stringent and mixed service requirements for future wireless services due to spectrum scarcity. Meanwhile, operating at high-frequency millimeter wave (mmWave) bands is seen as an attractive solution, primarily due to the bandwidth availability and possibility of large-scale multi-antenna communication. However, mmWave communication is inherently unreliable due to its susceptibility to blockage, high path loss, and channel uncertainty. Hence, to provide URLLC and high-speed wireless access, it is desirable to seamlessly integrate the reliability of μW networks with the high capacity of mmWave networks. To this end, in this paper, the first comprehensive tutorial for integrated mmWave-μW communications is introduced. This envisioned integrated design will enable wireless networks to achieve URLLC along with eMBB by leveraging the best of two worlds: reliable, long-range communications at the μW bands and directional high-speed communications at the mmWave frequencies. To achieve this goal, key solution concepts are discussed that include new architectures for the radio interface, URLLC-aware frame structure and resource allocation methods along with mobility management, to realize the potential of integrated mmWave-μW communications. The opportunities and challenges of each proposed scheme are discussed and key results are presented to show the merits of the developed integrated mmWave-μW framework.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2018.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 1.
6 papers.
- 2022Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approachcited by 28
- 2021Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networkscited by 6
- 2021Reinforcement Learning for Optimized Beam Training in Multi-Hop Terahertz Communicationscited by 0
- 2021Deep Learning Based Hybrid Precoding in Dual-Band Communication Systemscited by 0
- 2022Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networkscited by 0
- 2024Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approachcited by 0
- 2024Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach5 Jan 2024
- 2022Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach17 Aug 2022
- 2022Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks25 Jan 2022
- 2021Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks23 Nov 2021
- 2021Deep Learning Based Hybrid Precoding in Dual-Band Communication Systems16 Jul 2021
- 2021Reinforcement Learning for Optimized Beam Training in Multi-Hop Terahertz Communications10 Feb 2021