696 research outputs found
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
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
Feasibility of Simultaneous Information and Energy Transfer in LTE-A Small Cell Networks
Simultaneous information and energy transfer is attracting much attention as
an effective method to provide green energy supply for mobiles. However the
very low power level of the harvested energy from RF spectrum limits the
application of such technique. Thanks to the improvement of sensitivity and
efficiency of RF energy harvesting circuit as well as the dense deployment of
small cells base stations, the SIET becomes more practical. In this paper, we
propose a unified receiver model for SIET in LTE-A small cell base staion
networks, formulate the feasibility problem with Poisson point process model
and analysis the feasibility for a special and practical senario. The results
shows that it is feasible for mobiles to charge the secondary battery wih
harvested energy from BSs, but it is still infeasible to directly charge the
primary battery or operate without any battery at all
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