439 research outputs found
MMSE-based Transceiver Design Algorithms for Interference MIMO Relay Systems
In this paper, we investigate the transceiver design for amplify-and-forward interference multiple-input multipleoutput (MIMO) relay communication systems, where multiple transmitter-receiver pairs communicate simultaneously with the aid of a relay node. The aim is to minimize the mean-squared error (MSE) of the signal waveform estimation at the receivers subjecting to transmission power constraints at the transmitters and the relay node. As the transceiver optimization problem is nonconvex with matrix variables, the globally optimal solution is intractable to obtain. To overcome the challenge, we propose an iterative transceiver design algorithm where the transmitter, relay, and receiver matrices are optimized iteratively by exploiting the optimal structure of the relay precoding matrix. To reduce the computational complexity of optimizing the relay precoding matrix, we propose a simplified relay matrix design through modifying the transmission power constraint at the relay node. The modified relay optimization problem has a closedform solution. Simulation results demonstrate that the proposed algorithms perform better than the existing techniques in terms of both MSE and bit-error-rate
Hardware Impairments Aware Transceiver Design for Full-Duplex Amplify-and-Forward MIMO Relaying
In this work we study the behavior of a full-duplex (FD) and
amplify-and-forward (AF) relay with multiple antennas, where hardware
impairments of the FD relay transceiver is taken into account. Due to the
inter-dependency of the transmit relay power on each antenna and the residual
self-interference in an FD-AF relay, we observe a distortion loop that degrades
the system performance when the relay dynamic range is not high. In this
regard, we analyze the relay function in presence of the hardware inaccuracies
and an optimization problem is formulated to maximize the signal to
distortion-plus-noise ratio (SDNR), under relay and source transmit power
constraints. Due to the problem complexity, we propose a
gradient-projection-based (GP) algorithm to obtain an optimal solution.
Moreover, a nonalternating sub-optimal solution is proposed by assuming a
rank-1 relay amplification matrix, and separating the design of the relay
process into multiple stages (MuStR1). The proposed MuStR1 method is then
enhanced by introducing an alternating update over the optimization variables,
denoted as AltMuStR1 algorithm. It is observed that compared to GP, (Alt)MuStR1
algorithms significantly reduce the required computational complexity at the
expense of a slight performance degradation. Finally, the proposed methods are
evaluated under various system conditions, and compared with the methods
available in the current literature. In particular, it is observed that as the
hardware impairments increase, or for a system with a high transmit power, the
impact of applying a distortion-aware design is significant.Comment: Submitted to IEEE Transactions on Wireless Communication
Interference Alignment for Cognitive Radio Communications and Networks: A Survey
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe
Transceiver Optimization for Interference MIMO Relay Communication Systems
This thesis focuses on transceivers design for interference MIMO relay systems. Based on the MMSE criterion, several iterative algorithms are proposed to jointly optimize the source, relay, and receiver matrices subjecting to the individual power constraints at the source and the relay nodes. These algorithms have better performance than some existing algorithms and provide a better performance-complexity trade-off which is interesting in practical interference MIMO relay communication systems
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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