Hybrid Inter Cell Interference Coordination in 5G Networks · Hybrid Inter Cell Interference Coordination in 5G Networks Ayoob A. Ayoob, Hussein Amer Abdulazeez , Gang SU, Li Tan

Hybrid Inter Cell Interference Coordination in 5G Networks

Ayoob A. Ayoob, Hussein Amer Abdulazeez, Gang SU, Li Tan Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan, 430074.P.R.China

[email protected], [email protected], [email protected], [email protected]

Abstract

The high demand for mobile network broadband has led to the dense deployment of cellular networks

with assertive frequency reuse patterns., Inter-Cell Interference (ICI) produced by the simultaneous

usage of the same spectrum in different cells,. In this paper, a hybrid Inter Cell Interference

Coordination (ICIC) scheme as a negotiation between the integrated and the decentralized

methodologies was proposed. For a cluster of adjacent cells, resource and power allocation decisions

are made in a collective manner. First, the transmission power is fine-tuned after receiving the

necessary intelligence from the neighboring cells. Second, resource allocation between cells zones is

locally adjusted, according to throughput demands in each zone. Finally, the proposed technique

shows efficient joint distributed cell connection and power control (CAPC) methods that satisfy

objectives such as maximizing system throughput, less delay, less latency and balance traffic load

matter to a minimum SIR for high priority users.

Keywords

Inter-cell interference management, 5G, dense small cell networks, spectral efficiency, resource provision.

1. Introduction

The substantial enhancements in cellular networks and mobile devices have led to a rapidly

growing constraint for high speed multimedia applications. To support this swelling data traffic, the

ability of cellular networks can be improved via the dense deployment of small cells with antagonistic frequency reuse. Thus, resource allocation and interference administration is a key

research challenge in present and future cellular networks. In this chapter, we provide a

comprehensive explanation of the inter-cell interference problems in cellular networks as well as

the reason behind our research work on interference lessening techniques, the main contributions of

the thesis also given. Recently, the traffic burdens in mobile networks have tremendously increased.

The mobile data traffic[1][2], and it has up by 81% in 2013[3]. Consequently, mobile data traffic in

2017 will be 13 times that of 2012. This rapidly developing demand drove the 3GPP to initiate the

Long Term Evolution (LTE) of the Universal Mobile Terrestrial radio access System (UMTS).

LTE-Advanced (LTE-A) [4]was also proposed to recuperate cell-edge spectral efficiency, and to

increase the highest communication rates. However, network capacity and spectral efficiency

should be additional upgraded in order to address the exponentially swelling demands for mobile

broadband infrastructures. Network capacity improvement can be achieved through the dense

deployment of base stations with small coverage areas, within the coverage zones of macro cells

and using the same frequency spectrum. Although it improves the overall spectral efficiency, the

aggressive frequency reuse scheme increases the interference caused by UEs using the same radio

resources. Given the negative impact of ICI on system performance, on cell-edge UEs throughput,

and on network capacity, the utilization of adequate interference mitigation techniques becomes a

necessity for the next generation cellular networks[1][5].This paper focus on ICIC techniques

which designed to alleviate the impact of ICI, and to improve system performance. These target

objectives are achieved by modifying various system resources allocation such as frequency

resources and transmission power. For instance, several RRM schemes perform resource allocation

International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 13105-13116ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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between the different cells, and packet scheduling among the active UEs in each cell, in order to

improve system performance and to increase its spectral efficiency.

2. Related Works

2.1 Prioritized Power Control

To guarantee interference protection for HPUEs (high-priority users), a possible strategy is to modify

the existing resource management and power control methods discussed in above chapters in this

thesis. The prioritized power control is the method in which LPUEs limit their transmit power to

keep the interference caused to the HPUEs below a predefined threshold[6][7], while tracking their

own objectives. In other words, as long as the HPUEs are protected against existence of LPUEs

(low-priority users), the LPUEs could employ an existing distributed power control algorithm to

satisfy a predefined goal. This offers some fruitful direction for future research and investigation as

stated in to address these open problems in a distributed manner, the existing schemes should be

modified so that the LPUEs in addition to setting their transmit power for tracking their objectives,

limit their transmit power to keep their interference on receivers of HPUEs below a given threshold.

This could be implemented by sending a command from HPUEs to its nearby LPUEs (like a closed-

loop power control command used to address the near-far problem), when the interference caused by

the LPUEs to the HPUEs exceeds a given threshold.

2.2 Resources-Aware Cell Association (RACA) Schemes

Cell association schemes need to be devised that can balance the traffic load as well as minimize

interference or maximize SIR levels at the same time and can achieve a good balance between these

objectives without the need of static biasing-based CRE or ABS schemes. As an example, instead of

sacrificing the resources of a high-power BS to protect the offloaded users, user association schemes

can also be developed in which a user always prefers to associate with a low-power BS (with no bias)

as long as the received interference from high-power BS remains below a threshold. The high-power

BS may consider minimizing it’s transmit power subject to a maximum interference level experienced

by the off-loaded users (i.e., prioritized power control in the downlink)[8].The CRE technique forces

the users to select low power nodes by adding a fixed bias to them for traffic load balancing.

However, this strategy is immune to the resource allocation criterion employed in the corresponding

cell. For instance, if a low-power BS performs greedy scheduling, it is highly unlikely that an off-

loaded user will get a channel (i.e., low channel access probability) even if the RSRP with bias is the

best towards that BS among all other BSs. For round-robin scheduling, if the low-power BS has a

large number of users, it may keep the off-loaded users in starvation for long time and therefore cause

delay. Clearly, the channel access probability plays a major role in cell-association methods. Thus,

the bias selection should be adaptive (instead of static) to the resource allocation criterion, traffic

load, and distance/channel corresponding to the different BSs[9][10].

2.3 Distance –Aware Cell Association (DACA) Schemes

In this context, new cell association schemes/metrics need to be developed that can optimize multiple

objectives, e.g., traffic-load balancing and rate-maximization at the same time. To illustrate this, we

introduce a new resource-aware cell association criterion in which each user selects a BS with

maximum channel access probability, i.e., maxfpig, where pi is the channel access probability of a

cell I. Note that, the metric pi varies for different resource allocation criteria at the BSs. For instance,

in round-robin scheduling, pi is the reciprocal of the number of users. On the other hand, for greedy

scheduling pi is the probability that the channel gain of a potential admitting user exceeds the

channel gain of all existing users in cell I and thus depends on both channel and number of users in

cell I[3]. This new metric implicitly tends to balance the traffic load since if the number of users

grows in a cell, pi reduces and stops any further associations or vice versa. In this way, the proposed

criterion pi provides an adaptive biasing to different BSs considering their corresponding scheduling

scheme, traffic load and channel gains (if opportunistic scheduling is employed)[11][1].Note that, in

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distance-aware cell association, each user selects a cell with minimum distance which tends to

improve the sum-rate performance. However, this criterion is immune to traffic load conditions.

Combining the aforementioned resource-aware and distance-aware criteria, by consider a hybrid cell

association. The hybrid cell association scheme allows a typical user to select a cell with the

maximum of product of distance-based channel gain and pi. If pi = 0 (i.e., high/infinite traffic load),

a user will not select cell I even if it’s the closest cell and vice versa. Thus, hybrid schemes assist in

achieving a good balance between traffic-load balancing and throughput maximization [9][12].

3. Proposed work

Control Simultaneous connections to multiple BSs and different BS association for uplink and

downlink would increase the degrees of freedom which can be exploited to further improve the

network capacity and balance the load among different BSs in different tiers. The existing criteria or

cell association can be generalized to support simultaneous connection to multiple BSs. For instance,

the minimum effective-interference-based cell association can be generalized so that when the

differences among effective-interference levels between a given user and some BSs which offer that

user the lowest effective interference levels is not large, that user can simultaneously connects to

those BSs. The proposed resource-aware criterion for cell association can then be combined with this

criterion to balance the traffic load.These cell-association methods can be combined with the

prioritized power control schemes depending on the desired objectives. An important issue in this

regard is to select a correct combination of cell-association and power control method to achieve a

given objective. For instance, joint minimum effective-interference based cell association and PC is not capable of addressing the objective of throughput maximization (P3) in the uplink, as in this case

Figure 1. Proposed Technique


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All users try to associate with a BS of minimum effective interference which ultimately results in high transmit power of all users. Although the system throughput is improved when users with good channel conditions increase their transmit power, it degrades when users with poor channel conditions increase their transmit power.

Algorithm: Input Set RPk = relaxed sub-problem at node k

(bk, pk)= solution of RPk yk= value of the objective function at (bk, pk) which corresponds to the lower bound of node k

(bMIP, pMIP) = best obtained MIP solution of the primary MIP problem; yMIP= best obtained value at (bMIP, pMIP) which corresponds to the upper bound of the primary MIP problem.

The node k has no branches in the following cases: RPk=has no feasible solutions

bk = is integer; bk= non-integer and worse than the best obtained integer solution (bMIP, pMIP)(yk>yMIP for minimization problem).

4. Result and Analyses

In this section, we will present the simulation results by implementing the existing and proposed resource management methods in NS2. The following section is showing the NAM

results for depicting the visualization of small cell networks and macro cell networks. Figure 2 shows the Network animator (NAM) result for 20 number of small cell nodes of 5G network

simulation result, The nodes with blue circle indicating the 20 small cell users. Nodes with black color circles indicating the macro cell users. Nodes with red color square indicating small

cells BSs. Below Figure 3 is showing the above network in zooming condition in which it is clearly showing the nodes and their positions and names.

Figure 2. NAM visualization for 20 Number of Small Cell Nodes


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Figure 3. NAM Visualization for 20 Number of Small cell Nodes by Zooming.

Figure 4, shows the visualization result for 40 small cells nodes for 5G multi-tier networks. The big circles in figure showing that communication and network traffic is happening in that 5G area of network. Similarity the other networks NAM results comes for three underlying methods such as RACA, DACA and proposed HCA.

Figure 4. NAM Visualization for 40 Number of Small Cell Nodes by Zooming.


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Figure 5. NAM Visualization of Small Cell Nodes

The results so far are showing the NAM results for each network scenarios using RACA, DACA and

proposed HCA resource management methods. From this NAM file, it can differentiate between the exact difference between both these different methods. The performance difference between both this

techniques is evaluated using the trace file of each scenario and AWK script to measure each performance metrics. Next section presents the graphical comparative analysis of both fingerprinting

methods.

4.1 Throughput vs. Number of Small Cells

Throughput is nothing but the ratio of total amount of data in the form of packets the receiver will

receive from the source of the data within the specified time frame. Thus, throughput calculates the

fraction of the channel capacity which is used in order to transfer the important information. Figure 6

is showing the comparative study among different resource scheduling techniques. The proposed

HCA method showing the improvement in overall data rate performance as compared to existing

RACA (resource aware) and DACA (distance aware) methods. This claims that proposed HCA

method efficiently manage the radio resources for data communication in networks.

Figure 6. Throughput Performance Evaluation of Various Resources


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4.2 Delays vs. Number of Small Cell

Figure 7. and 8 are showing the comparative study among existing and proposed resource

management methods in terms of end to end delay and jitter by varying number of small number

cells. For 5G wireless communication networks achieving efficient delay and jitter performance as

compared to existing techniques of CAPC. The performance of RACA method is poor as compared

to DACA, and then we further improved the performance of DACA by introducing the concept of

queue management which is called as HCA. Based on practical results, it is showing that HCA

outperforming both DACA and RACA methods.

Figure 7. End to End Delay Performance Evaluation of Various Resources

Figure 8. Jitter Performance Evaluation


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Our system model consists of seven adjacent macro base stations serving active UEs within their

coverage area. Base station coverage is modeled as a sectored hexagonal layout, as shown in Figure

9, and CI denotes the cell identifier. Each site consists of three adjacent hexagonal sectors, where

each sector is served by an eNodeB having its own scheduler, bandwidth, and power allocation

policy.

Figure 9. Small Cell Distribution

4.3 UE Throughput

In order to investigate the impact of each technique on UE performance in each zone and on the

overall system performance, we use the following metrics: Mean throughput per UE [Mbit/s],

Mean throughput per GR UE [Mbit/s] Mean throughput per BR UE [Mbit/s].For each simulation run,

mean throughput is the average throughput achieved by UEs throughout the simulation time. These

three metrics give an overview about how the throughput of each zone is modified when applying an

ICIC technique. Thus, they allow carrying out a more detailed performance comparison using

significant throughput information.


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Figure 10. Mean Throughput per GR, BR and all UEs

4.4 Throughput Cumulative Distribution Function

The throughput CDF for the compared techniques, under the same simulation scenario. It allows us to

study throughput distribution among active UEs in the network. CDF for reuse-1, reuse-3, FFR, and

SFR techniques is illustrated in Figure 11 and 12. For a given throughput value, CDF represents the

probability to find a UE characterized by a lower throughput. The lower the CDF is, the better the

quality of service is. It can be notice that throughput CDF of reuse-3 model is the first to reach the

maximum. In other words, the probability to find a UE served with a throughput less than 1 Mbit/s

tends to one. FFR improves throughput CDF function in comparison with reuse-3. However, it

reaches the maximum before reuse-1 CDF. When using SFR, the number of UEs suff ering from bad

quality of service is reduced. For relatively low throughput values (less than 1 Mbit/s) throughput

CDF for SFR is the lowest curve; thus, it shows the lowest percentage of UEs served with low

throughputs. Moreover, SFR curve is the last one to reach its maximum (at 3 Mbit/s approximately).

Consequently, when mobile network operators seek to improve throughput CDF for the entire system,

SFR is the most adequate technique among the compared ICIC schemes. It succeeds in reducing the

percentage of UEs with relatively low throughputs, while also improving the maximum achievable

throughput in the network. Through restrictions made on downlink transmission power allocation,

SFR reduces ICI for BR UEs, and provides enough bandwidth for GR UEs to achieve higher data

rates.

Figure 11. Throughput Cumulative Distribution Function


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Figure 12. Throughput cumulative distribution function per node

5. Conclusion

This paper proposed the hybrid technique for radio resource management with goal of improving the

performance of jitter and delay. The proposed HCA method is based on two solutions of designing

the network architectures by adopting the millimeter wave and small cell technologies in order to

improve the performance of jitter and latency such as RACA and DACA. From the practical results,

it is showing the performance of throughput as compared to DACA method is improved by 35 %.

Whereas the performance of delay is minimized by 32 % as compared to DACA. The jitter

performance HCA is minimized by 28 % as compared to DACA. For future work, real time

deployment and evaluation of proposed technique should be done. The main conclusion of this study

confirms that within the scope of the envisioned 5G small cell system, fully flexible TDD is a viable

solution for indoor small cells. For this conclusion to be valid one needs to consider the efficacy of

the various building blocks available in the envisioned 5G concept.

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Hybrid Inter Cell Interference Coordination in 5G Networks · Hybrid Inter Cell Interference Coordination in 5G Networks Ayoob A. Ayoob, Hussein Amer Abdulazeez , Gang SU, Li Tan