In 5G ultra-dense networks, a distributed wireless backhaul is an attractive solution for forwarding traffic to the core. The macro-cell coverage area is divided into many small cells. A few of these cells are designated as gateways and are linked to the core by high-capacity fiber optic links. Each small cell is associated with one gateway and all small cells forward their traffic to their respective gateway through multi-hop mesh networks. We investigate the gateway location problem and show that finding near-optimal gateway locations improves the backhaul network capacity. An exact $p$ -median integer linear program is formulated for comparison with our novel K-GA heuristic that combines a Genetic Algorithm (GA) with $K$ -means clustering to find near-optimal gateway locations. We compare the performance of K-GA with six other approaches in terms of average number of hops and backhaul network capacity at different node densities through extensive Monte Carlo simulations. All approaches are tested in various user distribution scenarios, including uniform distribution, bivariate Gaussian distribution, and cluster distribution. In all cases, K-GA provides near-optimal results, achieving average number of hops and backhaul network capacity within 2 % of optimal while saving an average of 95 % of the execution time.

/pdf/a-fast-heuristic-for-gateway-location-in-wireless-backhaul-1nlxnsp6be.pdf

A Fast Heuristic for Gateway Location in Wireless Backhaul of 5G Ultra-Dense Networks

This paper proposes a power allocation mechanism for the HetCNs which is both dynamic and location-based. This mechanism could be adopted for both macrocells (MCs) and superimposed fixed or mobile small cells (SCs) to mitigate the inter-cell interference (ICI) effects. The proposed power allocation scheme could be termed as Dynamic Power Allocation based on User Location (DPAUL) mechanism, which dynamically changes the transmit power of serving base stations (BSs) depending on the location of users in the cell and then allocates to the concerned BSs. The work illustrates the dynamic downlink interference mitigation occurring within the cells, mainly due to the mobile SCs and mobile users. The Cell-User Mobility model has been adopted in this work, to analyze the mobility of cells and its users within the network. The proposed DPAUL scheme has been compared with some other works, including the author’s previously proposed DPAM mechanism. The proficiency of the proposed scheme on setup network has been investigated with sub 6 GHz and mmWave spectrums, and then effect on performance metrics such as sumrate, user throughput, energy-efficiency, and outage probability have been observed.

Energy-Efficient ICI Mitigation with Dynamic and Location-Based Power Allocation in Mobility-Based 5G HetCN

In this study, two dynamic mechanisms are proposed to mitigate inter-cell interference (ICI) in the heterogeneous cellular network (HetCN) namely: Dynamic Fixed Region Cooperation (DFRC) and Dynamic Power Allocation Mechanism (DPAM). The DFRC allows serving mSCs and other interfering base stations (BSs) to cooperate during transmission of a signal to a user. The DPAM dynamically allocates power to serving BS based on the farthest user in a cell. The study illustrates downlink interferences typically linked with a HetCN. For analysis, a macrocell (MC) network distributed with heterogeneous users is overlaid with both fixed and mobile small cells (SCs). The authors also introduced the cell-user mobility model for deployed mobile SCs in the network. The effects on network performance metrics: Sumrate, Average user throughput, and Energy efficiency are investigated with an allocation of sub 6 GHz and mmWave bands at MC and deployed SCs, respectively.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-com.2019.0898

Dynamic and energy-efficient ICI mitigation techniques for mobility-based 5G HetCN

Dynamic and location‐based power allocation mechanism for inter‐cell interference mitigation in 5G heterogeneous cellular network

The explosive growth of mobile devices is the main engine to continue evolution in the communications field. The amount of traffic generated by today’s users in applications such as high definition videos, cloud computing, and wearable devices, require a drastic change in mobile telecommunications. 5G Ultra Dense Network (UDN) is one of the key components leading in achieving the high capacity for all users. In UDN, the number of base stations or access nodes equals or exceeds the number of active users by unit area. In this paper, different modeling techniques of UDN are studied. Moreover, a heterogeneous framework modeling was proposed. This framework illustrated a system model for UDN based on Urban Macro (UMa) Scenario. The distance dependent path loss model for UMa was presented and analyzed. The Simulation results of path loss model indicated an increase in the path loss with increasing the distance range from 10m to 500m. The received power simulation results of User Terminal (UT) displayed the power is approaching zero when the distance between the BS and UT goes beyond 250m. Therefore, it is assumed that UTs located 250m away from the BS can reuse the subchannel of AN in another sector with negligible interference.

/pdf/heterogeneous-modelling-framework-for-5g-urban-macro-ultra-1uor2xikln.pdf

Heterogeneous modelling framework for 5G urban macro ultra dense networks

The explosive growth of the mobile traffic demand has triggered the investigation of 5th generation mobile networks (5G) for the future development of wireless communications. The ultra-dense network (UDN) has the ability to effectively improve system throughput and spectrum utilization by deploying a large number of low-power, low-cost, low-power home base stations or micro base stations. The dense distribution of micro base stations poses new challenges in terms of energy loss and interference management. Firstly, the overview of 5G is briefly introduced in this paper. Then, interference management method for UDN is summarized. So far, interference management methods for UDN mainly include interference alignment, clustering, and resource allocation. The principles of the three methods are described in detail. Finally, we conclude that clustering and resource allocation are more suitable for UDN.

Danqing Zhang

Papers

Overview on Interference Management Technology for Ultra-Dense Network