Resource Allocation in OFDM-Based Cognitive Two-Way Relay Networks

Cognitive radio (CR), nowadays, is considered as one of the most promising techniques which introduce the flexible usage of radio spectrum and improve the spectral efficiency by enabling unlicensed users to exploit the licensed spectrum in an opportunistic manner. Moreover, the two-way relay communication has attracted a great attention as it introduces a relaying scheme with a bidirectional transmission to exchange information between two nodes. This strategy assumed to improve the overall capacity, since less time slots are needed than the one-way strategy, besides extending the radio coverage range of networks. Another common technique that improves the bandwidth efficiency and spectrum utilization is the orthogonal frequency division multiplexing (OFDM) technique which exhibits a distinctive efficiency in mitigating inter-symbol interference (ISI) and combating frequency selective fading. Therefore, two-way relay CR communication among OFDM terminals can take advantage of these three techniques to boost up the capacity together with the networks quality. In this thesis, an OFDM-based amplify and forward (AF), cognitive two-way multiple-relay network is considered where two transceiver nodes exchange information via relay nodes. The full transmission happens in two time slots. In the first time slot, multiple access phase (MA), the transceiver nodes send their signals to the relay nodes while in the second time slot, broadcast phase (BC), the relay nodes broadcast the received signals to the transceivers. In this dissertation, the problem to jointly optimize the network resources is considered. The first is the transmission power of transceivers and relay nodes to ensure suitable allocated power for best signals transmission besides ensuring no harmful interference is caused to the primary system. The other important resource to be optimized is the subcarrier pairing where the first and second time slots subcarriers have to be matched such that the subcarriers with the best conditions is reserved. The final tuned resource, in this work, is the relay selection where the relay node that assures the best transition of the received signal is selected. The dual decomposition technique is applied to get the optimal resource allocation. Moreover, suboptimal algorithms are proposed to perform the resource allocation reducing, significantly, the computational complexity compared with the optimal solution with small performance degradation. Finally, simulation results of the suggested AF OFDM cognitive relaying network are shown to compare the performance gain of the different algorithms which reveals that the proposed suboptimal algorithm achieves good performance with much less computational complexity than the optimal one

Relay subset selection in cognitive networks with imperfect CSI and individual power constraints

This paper considers the relay subset selection problem in an underlay cognitive network in which two secondary users communicate assisted by a set of N potential relays. More specifically, this paper deals with the joint problem of choosing the best subset of L secondary relays and their corresponding weights which maximize the Signal-to-Interference-plus-Noise ratio (SINR) at the secondary user receiver, subject to per-relay power constraints and interference power constraints at the primary user. This problem is a combinatorial problem with a high computational burden. Nevertheless, we propose a sub-optimal technique, based on a convex relaxation of the problem, which achieves a near-optimal performance with a reduced complexity. Contrary to other approaches in the literature, the secondary relays are not limited to cooperate at full power.Peer ReviewedPostprint (author's final draft

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