300 research outputs found
Joint Design of Multi-Tap Analog Cancellation and Digital Beamforming for Reduced Complexity Full Duplex MIMO Systems
Incorporating full duplex operation in Multiple Input Multiple Output (MIMO)
systems provides the potential of boosting throughput performance. However, the
hardware complexity of the analog self-interference canceller scales with the
number of transmit and receive antennas, thus exploiting the benefits of analog
cancellation becomes impractical for full duplex MIMO transceivers. In this
paper, we present a novel architecture for the analog canceller comprising of
reduced number of taps (tap refers to a line of fixed delay and variable phase
shifter and attenuator) and simple multiplexers for efficient signal routing
among the transmit and receive radio frequency chains. In contrast to the
available analog cancellation architectures, the values for each tap and the
configuration of the multiplexers are jointly designed with the digital
beamforming filters according to certain performance objectives. Focusing on a
narrowband flat fading channel model as an example, we present a general
optimization framework for the joint design of analog cancellation and digital
beamforming. We also detail a particular optimization objective together with
its derived solution for the latter architectural components. Representative
computer simulation results demonstrate the superiority of the proposed low
complexity full duplex MIMO system over lately available ones.Comment: 8 pages, 4 figures, IEEE ICC 201
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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