Transparent Spectrum Co-Access in Cognitive Radio Networks

The licensed wireless spectrum is currently under-utilized by as much as 85%. Cognitive radio networks have been proposed to employ dynamic spectrum access to share this under-utilized spectrum between licensed primary user transmissions and unlicensed secondary user transmissions. Current secondary user opportunistic spectrum access methods, however, remain limited in their ability to provide enough incentive to convince primary users to share the licensed spectrum, and they rely on primary user absence to guarantee secondary user performance. These challenges are addressed by developing a Dynamic Spectrum Co-Access Architecture (DSCA) that allows secondary user transmissions to co-access transparently and concurrently with primary user transmissions. This work exploits dirty paper coding to precode the cognitive radio channel utilizing the redundant information found in primary user relay networks. Subsequently, the secondary user is able to provide incentive to the primary user through increased SINR to encourage licensed spectrum sharing. Then a region of co-accessis formulated within which any secondary user can co-access the licensed channel transparently to the primary user. In addition, a Spectrum Co-Access Protocol (SCAP) is developed to provide secondary users with guaranteed channel capacity and while minimizing channel access times. The numerical results show that the SCAP protocol build on the DSCA architecture is able to reduce secondary user channel access times compared with opportunistic spectrum access and increased secondary user network throughput. Finally, we present a novel method for increasing the secondary user channel capacity through sequential dirty paper coding. By exploiting similar redundancy in secondary user multi-hop networks as in primary user relay networks, the secondary user channel capacity can be increased. As a result of our work in overlay spectrum sharing through secondary user channel precoding, we provide a compelling argument that the current trend towards opportunistic spectrum sharing needs to be reconsidered. This work asserts that limitations of opportunistic spectrum access to transparently provide primary users incentive and its detrimental effect on secondary user performance due to primary user activity are enough to motivate further study into utilizing channel precoding schemes. The success of cognitive radios and its adoption into federal regulator policy will rely on providing just this type of incentive

The Achievable Rate of Interweave Cognitive Radio in the Face of Sensing Errors

Cognitive radio (CR) systems are potentially capable of mitigating the spectrum shortage of contemporary wireless systems. In this paper, we provide a brief overview of CR systems and the important research milestones of their evolution, along with their standardization activities, as a result of their research. This is followed by the detailed analysis of the interweave policy-based CR network (CRN) and by a detailed comparison with the family of underlay-based CRNs. In the interweave-based CRN, sensing of the primary user's (PU) spectrum by the secondary user's (SU) has remained a challenge, because the sensing errors prevent us from fulfilling the significant throughput gains that the concept of CR promises. Since missed detection and false alarm errors in real-time spectrum sensing cannot be avoided, based on a new approach, we quantify the achievable rates of the interweave CR by explicitly incorporating the effect of sensing errors. The link between the PU transmitter and the SU transmitter is assumed to be fast fading. Explicitly, the achievable rate degradation imposed by the sensing errors is analyzed for two spectrum sensing techniques, namely, for energy detection and for magnitude squared coherence-based detection. It is demonstrated that when the channel is sparsely occupied by the PU, the reusing techniques that are capable of simultaneously providing low missed detection and false alarm probabilities cause only a minor degradation to the achievable rates. Furthermore, based on the achievable rates derived for underlay CRNs, we compare the interweave CR and the underlay CR paradigms from the perspective of their resilience against spectrum sensing errors. Interestingly, in many practical regimes, the interweave CR paradigm outperforms the underlay CR paradigm in the presence of sensing errors, especially when the SNR at the SU is below 10 dB and when the SNR at the PU is in the range of 10-40 dB. Furthermore, we also provide rules of thumb that identify regimes, where the interweave CR outperforms the underlay CR

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