Design of a cell selection mechanism to mitigate interference for cell-edge macro users in femto-macro heterogeneous network

The Femto-Macro heterogeneous network is a promising solution to improve the network capacity and coverage in mobile network. However interference may rise due to femtocell deployment nearby to macro user equipment (MUE) within macrocell network coverage. Femtocell offers main priority in resource allocation to its subscribed femto user equipment (FUE) rather than unsubscribed MUE. MUEs will suffer severe interference when they are placed near or within the femtocell area range especially at the cell edge. This phenomenon occurs due to the distance is far from its serving macro base station (MBS) to receive good signal strength. This paper presents a design of cell selection scheme for cell-edge MUE to select an optimal femto base station (FBS) as its primary serving cell in physical resource block allocation. In this study, the proposed cell selection consists of four main elements: measuring the closest FBS distance, Signal to Interference-plus- Noise-Ratio (SINR), physical resource block (PRB) availability and node density level for the selected base station. The main goal is to ensure celledge MUE has priority fairly with FUE in physical resource block allocation per user bandwidth demand to mitigate interference. Hence, the cell-edge MUE has good experienced on receiving an adequate user data rate to improve higher network throughput.

Download Full-text

QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity

Applied Sciences ◽

10.3390/app9153018 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3018 ◽

Cited By ~ 2

Author(s):

Ren-Hung Hwang ◽

Min-Chun Peng ◽

Kai-Chung Cheng

Keyword(s):

Transfer Rate ◽

Data Transfer ◽

Base Stations ◽

Cell Selection ◽

Data Transfer Rate ◽

Selection Scheme ◽

Cell Edge ◽

Q Learning ◽

Dual Connectivity

Dual connectivity (DC) was first proposed in 3GPP Release 12 which allows one piece of user equipment (UE) to connect to two base stations in heterogeneous networks (HetNet) at the same time, to increase the flexibility of resource utilization. DC has been further extended to multiple connectivity in 5G New Radio (NR). On the other hand, different UE tends to have different bandwidth requirements. Thus, in DC, one of the challenging issues is how to integrate resources from two base stations to enhance the quality of service (QoS) as well as the data transfer rate of each UE. In this paper, we proposed novel resource management mechanisms to improve the QoS of UE in the co-channel dual connectivity network. In terms of resource allocation, we designed the (MTS) which, in principle, allocates a resource block to the UE with the best channel quality while considering the issues of intercell resource allocation and the QoS requirement of each UE. In order to balance the load of different cells, we designed a novel cell selection scheme based on the HetNet Congestion Indicator (HCI) which considers not only the signal quality of UE but also the remaining resources of each base station. To improve the QoS of cell edge UE, cell range expansion (CRE) and the Almost Blank Subframe (ABS) were proposed in 3GPP. In this paper, based on Q-learning, we designed an adaptive mechanism which dynamically adjusts the ABS ratio according to the network condition to improve resource utilization. Our simulation results showed that our MTS scheduler was able to achieve a 31.44% higher data rate than the Proportional Fairness Scheduler; our HCI cell selection scheme yielded a 2.98% higher data rate than the signal-to-interference plus noise ratio (SINR) cell selection scheme; the QoS satisfaction rate of our Q-learning dynamic ABS scheme was 4.06% higher than that of the Static ABS scheme. Finally, for the cell edge users who often suffer poor data transfer rate, by integrating the mechanisms of DC, CRE, and ABS, our experimental results showed that the QoS satisfaction ratio of cell edge UEs could be improved by 10.76% as compared to the single connectivity and no ABS situation.

Download Full-text

An efficient location aware distributed physical resource block assignment for dense closed access femtocell networks

Computer Networks ◽

10.1016/j.comnet.2015.11.013 ◽

2016 ◽

Vol 94 ◽

pp. 164-175 ◽

Cited By ~ 2

Author(s):

Sudeepta Mishra ◽

C. Siva Ram Murthy

Keyword(s):

Resource Block ◽

Femtocell Networks ◽

Physical Resource ◽

Location Aware ◽

Physical Resource Block

Download Full-text

Dynamic physical resource block allocation algorithms for uplink long term evolution

IET Communications ◽

10.1049/iet-com.2010.0316 ◽

2011 ◽

Vol 5 (7) ◽

pp. 1020-1027 ◽

Cited By ~ 20

Author(s):

O. Nwamadi ◽

A.K. Nandi ◽

X. Zhu

Keyword(s):

Long Term Evolution ◽

Resource Block ◽

Physical Resource ◽

Term Evolution ◽

Physical Resource Block ◽

Allocation Algorithms

Download Full-text

Cell Selection Mechanism Based on Q-learning Environment in Femtocell LTE-A Networks

Journal of ICT Research and Applications ◽

10.5614/itbj.ict.res.appl.2021.15.1.4 ◽

2021 ◽

Vol 15 (1) ◽

pp. 56-70

Author(s):

Ammar Abdulrazzak Bathich ◽

Saiful Izwan Suliman ◽

Hj. Mohd Asri Hj. Mansor ◽

Sinan Ghassan Abid Ali ◽

Raed Abdulla

Keyword(s):

Quality Of Service ◽

Learning Environment ◽

Base Station ◽

System Throughput ◽

Cell Selection ◽

Selection Mechanism ◽

Selection Scheme ◽

Selection Procedures ◽

Q Learning

Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased.

Download Full-text