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
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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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