Abstract
This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service.
Highlights
Generation wireless networks are expected to have an “everything, everywhere and always connected” future, where the end users shift from individuals to things
By slightly loosening the interference-free constraint on the cognitive femtocell, we present a partial cognitive interference alignment (IA) (P-CIA) scheme that can further improve the network performance under low and intermediate signal to noise ratio (SNR) conditions; the feasibility condition, the proof of convergence, and the capacity analyses are derived to demonstrate the effectiveness of the proposed cognitive IA schemes; and note that the proposed cognitive IA schemes can be performed in an autonomous way that requires no explicit cooperation between the two tiers
macrocell users (MUs) and a dual antenna macrocell base stations (MBSs) of the macrocell, and that identical transmit power is allocated for each user, i.e., K = 3, NT,p = 1, NR,p = 2 and Pk = P, for k = p, 1, 2, 3
Summary
Generation wireless networks are expected to have an “everything, everywhere and always connected” future, where the end users shift from individuals to things. To cope with the ever-increasing wireless traffic demands induced by IoT, a hierarchical approach to network deployment with a densely populated femtocell base station has been proposed Applications, such as cloudified mobile networks [1], virtual infrastructure [2], and industrial communication, exploit heterogeneous architecture to support ubiquitous, flexible, and reliable connectivity [3]. A tier of IoT oriented femtocell base stations coexists with a tier of pre-existing macrocell base stations (MBSs) and shares the licensed spectrum This two-tiered deployment aims at breaking away from the traditional cellular layout to provide very high data rates for short-range IoT devices, and reduces the load on the macrocell network [5]
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