Transmit and Receive Antenna Selection Based Resource Allocation for Self-Backhaul 5G Massive MIMO HetNets
Farah Akif1, Aqdas Malik1, Ijaz Qureshi2, and Ayesha Abassi1
1Department of Electrical Engineering, International Islamic University, Pakistan
2Department of Electrical Engineering, Air University, Pakistan
Abstract: With the advancement in wireless communication technology, the ease of accessibility and increasing coverage area is a major challenge for service providers. Network densification through Small cell Base Stations (SBS) integration in Heterogeneous Networks (HetNets) promises to improve network performance for cell edge users. Since providing wired backhaul for small cells is not cost effective or practical, the third-Generation Partnership Project (3GPP) has developed architecture for self-backhaul known as Integrated Access and Backhaul (IAB) for Fifth Generation (5G). This allows for Main Base Station (MBS) resources to be shared between SBS and MBS users. However, fair and efficient division of MBS resources remains a problem to be addressed. We develop a novel transmit antenna selection/partitioning technique for taking advantage of IAB 5G standard for Massive Multiple Input Multiple Output (MIMO) HetNets. Transmit antenna resources are divided among access for MBS users and for providing wireless backhaul for SBS. We develop A Genetic Algorithm (GA) based Transmit Antenna Selection (TAS) scheme and compare with random selection, eigenvalue-based selection and bandwidth portioning. Our analysis show that GA based TAS has the ability to converge to an optimum antenna subset providing better rate coverage. Furthermore, we also signify the performance of TAS based partitioning over bandwidth partitioning and also show user association can also be controlled using number of antennas reserved for access or backhaul.
Keywords: Antenna selection, Massive MIMO, heterogeneous networks, genetic algorithm.
Received January 9, 2020; accepted January 13, 2021