Abstract
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.
Highlights
The increased deployment of wireless services has on the one hand, consistently led to greater scarcity of the licensed frequency spectrum [1] and on the other hand has resulted in underutilization of frequency spectrum
This paper initially shows that minimal ranked cognitive radios (CRs) interference is desirable for increasing the efficiency of the primary users (PUs) in comparison to spreading a lesser amount of power over additional transmit signal dimensions, followed by a water-filling solution that uses a negligible sum of power to attain the rate limitation with a low-rank transfer of covariance matrix
A number of enhancements have been incorporated in both paradigms of Interference alignment (IA) in CR to improve low signal-to-noise ratio (SNR) performance, enhance distributed computation and are more robust to channel state information (CSI) imperfections
Summary
The increased deployment of wireless services has on the one hand, consistently led to greater scarcity of the licensed frequency spectrum [1] and on the other hand has resulted in underutilization of frequency spectrum. This paradigm proposes that under power-limitation, a PU that maximizes its own rate by using appropriate algorithms (usually water-filling algorithms) on its MIMO channel singular values might leave some of them unused, i.e., no transmission takes place along the corresponding spatial directions These unused directions may be opportunistically utilized by one of the SU-Tx by designing an IA technique for the SUs, in which a linear pre-coder perfectly aligns the interference generated by the SU-Tx with such unused spatial directions, thereby enabling the SUs to share the licensed spectrum with zero interference to the PU transmission [24]. The definitions of the acronyms that will be frequently used in this paper are summarized in Table 1 for ease of reference
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