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A line-of-sight optimised MIMO architecture for outdoor environments
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3-D Statistical Channel Model for Millimeter-Wave Outdoor Mobile Broadband Communications
This paper presents an omnidirectional spatial and temporal 3-dimensional
statistical channel model for 28 GHz dense urban non-line of sight
environments. The channel model is developed from 28 GHz ultrawideband
propagation measurements obtained with a 400 megachips per second broadband
sliding correlator channel sounder and highly directional, steerable horn
antennas in New York City. A 3GPP-like statistical channel model that is easy
to implement in software or hardware is developed from measured power delay
profiles and a synthesized method for providing absolute propagation delays
recovered from 3-D ray-tracing, as well as measured angle of departure and
angle of arrival power spectra. The extracted statistics are used to implement
a MATLAB-based statistical simulator that generates 3-D millimeter-wave
temporal and spatial channel coefficients that reproduce realistic impulse
responses of measured urban channels. The methods and model presented here can
be used for millimeter-wave system-wide simulations, and air interface design
and capacity analyses.Comment: 7 pages, 6 figures, ICC 2015 (London, UK, to appear
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling
For a base station that supports cellular communications in sub-6 GHz LTE and
millimeter (mmWave) bands, we propose a supervised machine learning algorithm
to improve the success rate in the handover between the two radio frequencies
using sub-6 GHz and mmWave prior channel measurements within a temporal window.
The main contributions of our paper are to 1) introduce partially blind
handovers, 2) employ machine learning to perform handover success predictions
from sub-6 GHz to mmWave frequencies, and 3) show that this machine learning
based algorithm combined with partially blind handovers can improve the
handover success rate in a realistic network setup of colocated cells.
Simulation results show improvement in handover success rates for our proposed
algorithm compared to standard handover algorithms.Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
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