Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges
Sundeep Rangan,Theodore S. Rappaport,Elza Erkip +2 more
- Vol. 102, Iss: 3, pp 366-385
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TLDR
Measurements and capacity studies are surveyed to assess mmW technology with a focus on small cell deployments in urban environments and 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.Abstract:
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 multielement 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 microcell 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.read more
Citations
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Throughput Characterization and Beamwidth Selection for Positioning-Assisted mmWave Service
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Evaluation of mmWave 5G Performance by Advanced Ray Tracing Techniques
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Parametric Sparse Channel Estimation Using Long Short-Term Memory for mmWave Massive MIMO Systems
TL;DR: A new type of deep learning (DL)-based parametric channel estimation technique, exploiting the long short-term memory (LSTM) as a main deep neural network (DNN) engine, to make a fast yet accurate estimation of the mmWave MIMO channel with relatively small pilot overhead.
Book ChapterDOI
Wireless Power Transfer in Millimeter Wave
TL;DR: In this article, a case study illustrating how mmWave can be used for wireless power transfer despite the presence of blockages is presented, where the authors examine the network level performance trends in terms of key parameters such as the mmWave network density and the antenna geometry parameters for both operating modes of the energy harvesting devices.
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