An interference alignment scheme based on limited feedback with differentiation in heterogeneous networks. Issue 12 (6th October 2021)

• Record Type: Journal Article
• Authors: Li, Shibao; Wang, Mingyu; Wang, Xiaoli; Liu, Jianhang; Cui, Xuerong; Chen, Haihua
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 32:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-06
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.4375
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: The traditional limited feedback scheme can use imperfect channel state information to achieve interference alignment (IA) in a fully connected situation. But these solutions could no longer be used directly in heterogeneous networks where partial connectivity is considered. To solve this problem, this article proposes an IA scheme based on limited feedback with differentiation for the two‐tiered heterogeneous network. First, users who are affected by different types and numbers of interference and corresponding base stations are divided into different units according to the partial connectivity of heterogeneous networks. Then, we design interference alignment algorithm for each unit separately. Next, considering the imperfect channel state information, we derive the leakage of interference caused by the limited feedback, and give expressions for the upper bound of sum rate loss in various units. Finally, dynamic feedback bit allocation is carried out to each unit with the goal of minimizing the throughput loss in the limited feedback system. The simulation results show that the proposed IA scheme can eliminate interference well and efficiency of limited feedback can be improved through dynamic feedback bit allocation, so that system can obtain higher sum rate than other schemes. Abstract : Considering the partial connectivity of heterogeneous networks, we propose an interference alignment scheme based on limited feedback with differentiation. The proposed scheme can eliminate interference in these networks effectively and reduce performance loss caused by limited feedback.