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

The Practical Challenges of Interference Alignment

Interference alignment (IA) is a revolutionary wireless transmission strategy that reduces the impact of interference. The idea of interference alignment is to coordinate multiple transmitters so that their mutual interference aligns at the receivers, facilitating simple interference cancellation techniques. Since IA's inception, researchers have investigated its performance and proposed improvements, verifying IA's ability to achieve the maximum degrees of freedom (an approximation of sum capacity) in a variety of settings, developing algorithms for determining alignment solutions, and generalizing transmission strategies that relax the need for perfect alignment but yield better performance. This article provides an overview of the concept of interference alignment as well as an assessment of practical issues including performance in realistic propagation environments, the role of channel state information at the transmitter, and the practicality of interference alignment in large networks.Comment: submitted to IEEE Wireless Communications Magazin

Exploiting Interference Alignment in Multi-Cell Cooperative OFDMA Resource Allocation

This paper studies interference alignment (IA) based multi-cell cooperative resource allocation for the downlink OFDMA with universal frequency reuse. Unlike the traditional scheme that treats subcarriers as separate dimensions for resource allocation, the IA technique is utilized to enable frequency-domain precoding over parallel subcarriers. In this paper, the joint optimization of frequency-domain precoding via IA, subcarrier user selection and power allocation is investigated for a cooperative three-cell OFDMA system to maximize the downlink throughput. Numerical results for a simplified symmetric channel setup reveal that the IA-based scheme achieves notable throughput gains over the traditional scheme only when the inter-cell interference link has a comparable strength as the direct link, and the receiver SNR is sufficiently large. Motivated by this observation, a practical hybrid scheme is proposed for cellular systems with heterogenous channel conditions, where the total spectrum is divided into two subbands, over which the IAbased scheme and the traditional scheme are applied for resource allocation to users located in the cell-intersection region and cellnon- intersection region, respectively. It is shown that this hybrid resource allocation scheme flexibly exploits the downlink IA gains for OFDMA-based cellular systems.Comment: 5 pages, 5 figures, GC2011 conferenc