4,593 research outputs found
Efficient Detectors for MIMO-OFDM Systems under Spatial Correlation Antenna Arrays
This work analyzes the performance of the implementable detectors for
multiple-input-multiple-output (MIMO) orthogonal frequency division
multiplexing (OFDM) technique under specific and realistic operation system
condi- tions, including antenna correlation and array configuration.
Time-domain channel model has been used to evaluate the system performance
under realistic communication channel and system scenarios, including different
channel correlation, modulation order and antenna arrays configurations. A
bunch of MIMO-OFDM detectors were analyzed for the purpose of achieve high
performance combined with high capacity systems and manageable computational
complexity. Numerical Monte-Carlo simulations (MCS) demonstrate the channel
selectivity effect, while the impact of the number of antennas, adoption of
linear against heuristic-based detection schemes, and the spatial correlation
effect under linear and planar antenna arrays are analyzed in the MIMO-OFDM
context.Comment: 26 pgs, 16 figures and 5 table
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
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