mlsanityData to 7 Oct 2026

Millimeter Wave Cellular Wireless Networks: Potentials and Challenges

arXiv 1401.2560 ↗First posted 11 Jan 2014cs.NIcited by 19

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Abstract (arXiv)

Millimeter wave (mmW) frequencies between 30 and 300 GHz are a new frontier for cellular communication that offers the promise of orders of magnitude greater bandwidths combined with further gains via beamforming and spatial multiplexing from multi-element antenna arrays. This paper surveys measurements and capacity studies to assess this technology with a focus on small cell deployments in urban environments. The conclusions are extremely encouraging; measurements in New York City at 28 and 73 GHz demonstrate that, even in an urban canyon environment, significant non-line-of-sight (NLOS) outdoor, street-level coverage is possible up to approximately 200 m from a potential low power micro- or picocell base station. In addition, based on statistical channel models from these measurements, it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities. Cellular systems, however, will need to be significantly redesigned to fully achieve these gains. Specifically, the requirement of highly directional and adaptive transmissions, directional isolation between links and significant possibilities of outage have strong implications on multiple access, channel structure, synchronization and receiver design. To address these challenges, the paper discusses how various technologies including adaptive beamforming, multihop relaying, heterogeneous network architectures and carrier aggregation can be leveraged in the mmW context.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jan 2014.

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Citing papers per year: 2021: 8; 2022: 2; 2023: 4; 2024: 1; 2025: 2; 2026: 2.

19 papers.

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  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  2. 2021Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulationcited by 11
  3. 2023Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learningcited by 11
  4. 2024AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Designcited by 8
  5. 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Controlcited by 5
  6. 2021Machine Learning on Camera Images for Fast mmWave Beamformingcited by 4
  7. 2021Learn to Communicate with Neural Calibration: Scalability and Generalizationcited by 3
  8. 2021Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Fastercited by 3
  9. 2021Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networkscited by 2
  10. 2026QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networkscited by 1
  11. 2021Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learningcited by 0
  12. 2021On Single-User Interactive Beam Alignment in Next Generation Systems: A Deep Learning Viewpointcited by 0
7 more
  1. 2022Proactive Resilient Transmission and Scheduling Mechanisms for mmWave Networkscited by 0
  2. 2023Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networkscited by 0
  3. 2023RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimationcited by 0
  4. 2023Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networkscited by 0
  5. 2025Machine Learning for LiDAR-Based Indoor Surface Classification in Intelligent Wireless Environmentscited by 0
  6. 2025Refined Bayesian Optimization for Efficient Beam Alignment in Intelligent Indoor Wireless Environmentscited by 0
  7. 2026When Simultaneous Localization and Mapping Meets Wireless Communications: A Surveycited by 0