Research On Interference Alignment Technique In 5G Ultra-dense Networks

Wireless communication has experienced explosive growth over the past decade, along with a variety of services as well as devices ranging from pocket phones to laptops, tablets and sensors. The wireless industry has taken on the challenge of cost-effectively supporting a 1000-fold increase in traffic demand over the next decade.Ultra-dense networks are regarded as a promising candidate for providing high data rate at low implementation cost. With a dense and irregular deployment of access points, spatial reuse will be improved, and varying cellular coverage also known as the amorphous cell is provided,which leads to a more complicated interference environment.This article first introduces the background, including the characteristics of Ultra-Dense Networks deployment as well as the theoretical foundation and limitation of Interference Alignment technology.Then, based on the amorphous architecture of the Ultra-Dense Networks,RANaaS evolution is discussed. Considering the users and data services with the "tidal effect", dense networks in the future will take the user as the center, utilizing multiple wireless technology to realize "soft" regional coverage, and offering "dynamic" and "smart" transmission service.Therefore, RANaaS will be more flexibility and extensibility than C-RAN.In the following part, a novel interference coordination strategy, based on applying interference alignment (IA) in each disjoint sub-cluster of these amorphous cells, is proposed as a key empowering technology for next generation (5G) ultra-dense networks. A main merit of the proposed strategy is its ability to effectively identify and mitigate the dominant interference for each victim user. Different from existing works, the coordinating base stations (BSs) in the sub-clusters are selected based on the ratio between the dominant interference (DI) and the rest of the perceived interference, called the dominant interference ratio (DIR), and thus interference alignment adapts to the varying interference situation by controlling the overall high-level interference. By adopting a random spatial network model, we can illustrate the variation of the interference relations with the network topology and victim users' location in the unplanned future dense networks. How to efficiently form IA sub-clusters has been well studied by a modified k-means algorithm. Simulation results demonstrate significant performance gains of the proposed clustering IA strategy...