710 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles
In recent years, there has been a dramatic increase in the use of unmanned
aerial vehicles (UAVs), particularly for small UAVs, due to their affordable
prices, ease of availability, and ease of operability. Existing and future
applications of UAVs include remote surveillance and monitoring, relief
operations, package delivery, and communication backhaul infrastructure.
Additionally, UAVs are envisioned as an important component of 5G wireless
technology and beyond. The unique application scenarios for UAVs necessitate
accurate air-to-ground (AG) propagation channel models for designing and
evaluating UAV communication links for control/non-payload as well as payload
data transmissions. These AG propagation models have not been investigated in
detail when compared to terrestrial propagation models. In this paper, a
comprehensive survey is provided on available AG channel measurement campaigns,
large and small scale fading channel models, their limitations, and future
research directions for UAV communication scenarios
Improving UAV Communication in Cell Free MIMO Using a Reconfigurable Intelligent Surface
Communication with unmanned aerial vehicles (UAVs) in current terrestrial
networks suffers from poor signal strength due to the down-tilt of the access
points (APs) that are optimized to serve ground users ends (GUEs). To solve
this, one could tilt the AP antenna upwards or allocate more power to serve the
UAV. However, this negatively affects GUE downlink (DL) rates. In this paper,
we propose to solve this challenge using a reconfigurable intelligent surface
(RIS) to enhance the UAV communication while preserving the 3GPP- prescribed
downwards antenna tilt and potentially improving the DL performance of the GUE.
We show that under conjugate beamforming (CB) precoding and proper power split
between GUEs and the UAV at the APs, an RIS with phase-shifts configured to
reflect radio signals towards the UAV can significantly improve the UAV DL
throughput while simultaneously benefiting the GUEs. The presented numerical
results show that the RIS- aided system can serve a UAV with a required data
rate while improving the GUEs DL performance relative to that in a CF- MIMO
system without a UAV and an RIS. We support this conclusion through simulations
under a varying numbers of RIS reflecting elements, UAV heights, and power
split factor
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