# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 1 Introduction  
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 however, has resulted in underutilization of frequency spectrum. This is largely due to the current fixed spectrum access (FSA) policy, which is based on a static allocation of the spectrum resources. This gives licensed users exclusive rights to the spectrum and results in several portions of the licensed spectrum being underutilized [2; 3; 4]. This justifies demand for a more flexible access policy called dynamic spectrum access (DSA) because it ensures better utilization of the spectrum resources. The spectrum resources are still allocated to the licensed/primary users (PUs) but its usage is not exclusively granted. Unlicensed/secondary users (SUs) with cognition capability [5] are able to access the spectrum resources opportunistically, as long as the PUs remain idle. These SUs or cognitive radios (CRs) can also concurrently share the spectrum with the PUs, as long as the PUs transmissions are adequately protected. The radio spectrum can therefore be reused in an opportunistic manner or shared all the time, to improve spectral efficiency [6]. This cognition capability of CRs can be divided into three main components namely spectrum sensing, spectrum analysis and spectrum access decision. Spectrum sensing is by far the most important component in the establishment of CRs as it provides the awareness of the overall spectrum utilization as well as which bands to sense for gaps in the spectrum (also called spectrum holes), when to sense for those gaps and for how long to sense in real-time [7]. There are two techniques usually involved in spectrum sensing (SS), namely direct spectrum sensing and indirect spectrum sensing, the formerbeing used to detect the receiver (Rx) of the PU, while the latter is used to detect the transmit (Tx) of the PU. Even though it is more effective for SS to be employed directly, it is the more challenging of the two techniques because the Rx does not usually transmit when it works [7; 8]. Therefore, most of the existing SS schemes employ indirect SS techniques. Such schemes are generally classified to matched filter detection, energy detection, feature detection, and interference temperature measurement. The reality however, is that SUs are almost unable to operate concurrently with the PUs without causing harmful interference to the PU system, thus a very crucial task in the design of CR networks is about how best the SUs can avoid interfering with the PUs in their vicinity [9; 10; 11]. This becomes particularly challenging nowadays due to data communication, where the PU is seldom idle, such that the availability of spectrum holes becomes diminished [12]. In attempting to solve to this issue, the emphasis of research has moved in the direction of a innovative interference management strategy called interference alignment (IA) [13; 14; 15]. It is a radical cooperative interference management strategy that has recently emerged through rigorous analysis of interference channels and networks. This approach exploits the availability of multiple signaling dimensions either from multiple antennas, time slots or frequency blocks as the case may be. The transmitters linearly encode their signals over these multiple signaling dimensions, such that the resulting interference signal observed at each receiver lies in a lower dimensional subspace and is orthogonal to the one spanned by the signal of interest at each receiver while the other subspace is reserved for interference free communication. This helps to achieve the interference channels maximum multiplexing gain, otherwise known as degrees of freedom (DoF) [16; 17]. DoF is an essential measurement used for capacity approximation.