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Millimeter Wave Channel Modeling and Cellular Capacity Evaluation

arXiv 1312.4921 ↗First posted 17 Dec 2013cs.NIcited by 29

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Abstract (arXiv)

With the severe spectrum shortage in conventional cellular bands, millimeter wave (mmW) frequencies between 30 and 300 GHz have been attracting growing attention as a possible candidate for next-generation micro- and picocellular wireless networks. The mmW bands offer orders of magnitude greater spectrum than current cellular allocations and enable very high-dimensional antenna arrays for further gains via beamforming and spatial multiplexing. This paper uses recent real-world measurements at 28 and 73 GHz in New York City to derive detailed spatial statistical models of the channels and uses these models to provide a realistic assessment of mmW micro- and picocellular networks in a dense urban deployment. Statistical models are derived for key channel parameters including the path loss, number of spatial clusters, angular dispersion and outage. It is found that, even in highly non-line-of-sight environments, strong signals can be detected 100 m to 200 m from potential cell sites, potentially with multiple clusters to support spatial multiplexing. Moreover, a system simulation based on the models predicts that mmW systems can offer an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks with no increase in cell density from current urban deployments.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Dec 2013.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 10; 2023: 4; 2024: 4; 2025: 2; 2026: 1.

29 papers.

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  1. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  2. 2021Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updatescited by 10
  3. 2021Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networkscited by 8
  4. 2022Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relayingcited by 5
  5. 2023A Multi-Modal Simulation Framework to Enable Digital Twin-based V2X Communications in Dynamic Environmentscited by 5
  6. 2021Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Fastercited by 3
  7. 2022Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approachcited by 3
  8. 2022UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithmscited by 3
  9. 2021Learning and Adaptation for Millimeter-Wave Beam Tracking and Training: a Dual Timescale Variational Frameworkcited by 2
  10. 2023Learning-Based Adaptive User Selection in Millimeter Wave Hybrid Beamforming Systemscited by 2
  11. 2024Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networkscited by 2
  12. 2024Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networkscited by 2
17 more
  1. 2025Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamformingcited by 2
  2. 2021Channel Estimation and Hybrid Architectures for RIS-Assisted Communicationscited by 1
  3. 2021CSI Clustering with Variational Autoencodingcited by 1
  4. 2022Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systemscited by 1
  5. 2022Wireless Channel Prediction in Partially Observed Environmentscited by 1
  6. 2022Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systemscited by 1
  7. 2022Learning Optimal Phase-Shifts of Holographic Metasurface Transceiverscited by 1
  8. 2022UB3: Best Beam Identification in Millimeter Wave Systems via Pure Exploration Unimodal Banditscited by 1
  9. 2024Channel Modeling for FR3 Upper Mid-band via Generative Adversarial Networkscited by 1
  10. 2021On Single-User Interactive Beam Alignment in Next Generation Systems: A Deep Learning Viewpointcited by 0
  11. 2021Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communicationscited by 0
  12. 2022ProSky: NEAT Meets NOMA-mmWave in the Sky of 6Gcited by 0
  13. 2023Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approachcited by 0
  14. 2023HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceiverscited by 0
  15. 2024Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systemscited by 0
  16. 2025DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Userscited by 0
  17. 2026GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channelscited by 0