# Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks
## 1 Introduction
More specifically, in each primary receiver, the cognitive interference caused by IoT devices should be aligned into the subspace orthogonal to the primary link so that the desired signal can be recovered free of cross-tier interference [13]. A number of state-of-the-art cognitive IA algorithms have been proposed in the literature, so as to ensure the coexistence of such two tiers and enhance the effective utilization of the licensed spectrum. For example, in [14], [PERSON] et al. proposed a selective interference alignment with user selection. By judiciously choosing the set of cognitive transmitters to be aligned at each primary receiver, the selective interference alignment achieved a trade-off between algorithm complexity and achievable capacity. In [15], [PERSON] et al. considered the scenario with just one cognitive user and proposed a cooperative IA scheme. The proposed scheme minimizes the distance between the subspaces of the co-tier and cross-tier interferences, and aligns them into the same orthogonal subspace at each primary user. In [16], [PERSON] et al. focus on the uplink interference management in heterogeneous cellular systems, and designed an IA based precoder. The precoding design was accomplished using successive semi-definite programming (SDP), aiming to minimize the cross-tier interference leakage while providing a suboptimal received signal to interference plus noise ratio (SINR). Although the successive SDP approximation reduces the complexity, the proposed algorithm requires cooperation from macrocell users (MUs) with the closest femtocell base station (FBS), and only achieves a fraction of the optimal system capacity. In [17], [PERSON] et al. proposed a distributed cognitive interference alignment (DCIA) algorithm in the absence of cooperation between two tiers. The DCIA algorithm only considers the interference to the primary tier, which reduces the overhead of the channel estimation in tiered networks. In [18], for the case of a dense heterogeneous cellular layout, [PERSON] et al. proposed an orthogonal transmission precoding scheme named MU-VFDM. By exploiting the cooperation and designing the precoder as a cascaded linear structure, the MU-VFDM algorithm achieves a comparable performance to the homogeneous scenario with multi-user coding techniques. As can be seen, existing cognitive IA schemes mainly focus on the cross-tier interference caused to primary users. There is no suppression on the interference from primary users to cognitive users, which may cause severe performance degradation. It is also significant to note that most existing schemes require several degrees of co-tier and cross-tier coordination, which involves dedicated bidirectional connection between the macrocell and femtocell. However, due to the unplanned deployment and self-organizing features of IoT networks, it may be impractical to provide the bidirectional cooperative links that meet the latency and delay requirements [19]. Moreover, for cooperative IA, rather than designing the precoder to achieve the optimal capacity, primary users must compromise its performance to help cognitive users to mitigate the cross-tier interference. This turns out to be a major limitation that cooperative IA may degrade the channel capacity of the primary links. It is also overlooking the principle that cognitive radio networks should be transparent to primary users without causing any undesired performance degradation to them [20]. Motivated by these observations, in this paper, we consider a two-tiered network comprised of a pre-existing macrocell tier and a cognitive IoT femtocell tier, and propose two novel self-organized cognitive IA schemes to manage the interference and improve the network capacity. The main contributions of this paper can be summarized as follows:  
* To fully eliminate the interference, we first propose an interference nulling based cognitive IA (IN-CIA) scheme.