2 research outputs found
Distributed Wideband Sensing-based Architecture for Unlicensed Massive IoT Communications
Providing Internet connectivity to a massive number of Internet-of-things
(IoT) objects over the unlicensed spectrum requires: (i) identifying a very
large number of narrowband channels in a wideband spectrum and (ii)
aggressively reusing the available channels over space to accommodate the high
density of IoT devices. To this end, we propose a sensing-based architecture
that identifies spectral and spatial resources at a fine resolution. In
particular, we first propose a sensing assignment scheduler, where each base
station (BS) is assigned a subset of the spectrum to sense at a high
resolution. We then propose a distributed sensing algorithm, where BSs locally
process and share their sensing reports, so that each BS obtains occupancy
information of the wideband spectrum at its location. Once the spatio-spectral
resource blocks are identified, we further propose a distributed resource
allocation algorithm that maintains high spatial reuse of spectral
opportunities while limiting the intra-network and inter-network interference.
Numerical simulations are presented to validate the effectiveness of the
proposed distributed algorithms, comparing them to centralized and
non-cooperative schemes. It is shown that our architecture identifies more
spatio-spectral resources, with lower misdetection of incumbents. As a result,
more IoT devices are connected with limited interference into incumbents.Comment: The paper is submitted to the IEEE for possible publicatio
Unlicensed Spectrum Sharing for Massive Internet-of-Things Communications
The unlicensed spectrum, although free, has become an invaluable resource
toward enabling massive Internet-of-things (IoT) applications, where
Internet-enabled devices are deployed at a large scale. However, realizing
massive IoT connectivity over unlicensed bands requires efficient spectrum
sharing among IoT devices and fair coexistence with other wireless networks. In
this article, we discuss several spectrum sharing methods to address these
intra- and inter-network sharing issues. To this end, we first consider
ALOHA-based networks that avoid spectrum sensing, yet rely on diversity to
improve connection density of random access. Then, we present sensing-based
solutions such as unlicensed cellular access that help support a wider range of
applications with different rate requirements and connection densities.
Finally, we highlight future research directions for massive IoT connectivity
over the unlicensed spectrum