659 research outputs found
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
Interference Alignment-Aided Base Station Clustering using Coalition Formation
Base station clustering is necessary in large interference networks, where
the channel state information (CSI) acquisition overhead otherwise would be
overwhelming. In this paper, we propose a novel long-term throughput model for
the clustered users which addresses the balance between interference mitigation
capability and CSI acquisition overhead. The model only depends on statistical
CSI, thus enabling long-term clustering. Based on notions from coalitional game
theory, we propose a low-complexity distributed clustering method. The
algorithm converges in a couple of iterations, and only requires limited
communication between base stations. Numerical simulations show the viability
of the proposed approach.Comment: 2nd Prize, Student Paper Contest. Copyright 2015 SS&C. Published in
the Proceedings of the 49th Asilomar Conference on Signals, Systems and
Computers, Nov 8-11, 2015, Pacific Grove, CA, US
Cooperative Transmission for Downlink Distributed Antenna in Time Division Duplex System
Multi-user distributed antenna system (MU-DAS) systems play the
essential role in improving throughput performance in wireless communications. This improvement can be achieved by exploiting the spatial
domain and without the need of additional power and bandwidth. In
this thesis, three main issues which are of importance to the data rate
transmission have been investigated.
Firstly, user clustering in MU-DAS downlink systems has been considered, where this technique can be effciently used to reduce the complexity and cost caused by radio frequency chains, associated with antennas while keeping most of the diversity advantages of the system.
The proposed user clustering algorithm which can select an optimal set
of antennas for transmission. The capacity achieved by the proposed
algorithm is almost same as the capacity of the optimum search method,
with much lower complexity.
Secondly, interference alignment in MU-DAS downlink systems has
been studied. The inter-cluster interference is uncoordinated and limits
the system performance. The inter-cluster interference should be eliminated or minimized carefully. The interference alignment is proposed to
consolidate the strong inter-cluster interference into smaller dimensions
of signal space at each user and use the remaining dimensions to transmit
the desired signals without any interference. The performance of single
cluster is better than the proposed algorithm due to the absence of intercluster interference in the single cluster. The numerical shows that the
proposed algorithm is more suitable in multi-cell DAS environment due
to the presence of inter-cell interference.
Finally, the impact of different user mobility on TDD downlink MUDAS has been studied. The downlink data transmission in time division
duplex (TDD) systems is optimized according to the channel state information (CSI) which is obtained at the uplink time slot. However, the
actual channel at downlink time slot may be different from the estimated
channel due to channel variation in mobility environment. Based on mobility state information (MSI), an autocorrelation based feedback interval
adjustment technique is proposed. The proposed technique adjusts the
CSI update interval and mitigates the performance degradation imposed
by the user mobility and the transmission delay. Cooperative clusters are
formed to maximize sum rate. In order to reduce the computational complexity, a channel gain based antenna selection and signal-to-interference
plus noise ratio (SINR) based user clustering are developed. A downlink
ergodic capacity is derived in single user clustering. The derived analytical expressions of the downlink ergodic capacity are verified by system
simulations. Numerical results show that the proposed scheme can improved sum rate over the non cooperative system and no MSI knowledge.
The proposed technique has good performance for a wide range of user
speed and suitable for future wireless communications systems
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