Channel Capacity Maximization using NQHN Approach at Heterogeneous Network

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 9, No. 4, August 2019, pp. 2593~2602
ISSN: 2088-8708, DOI: 10.11591/ijece.v9i4.pp2593-2602  2593
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Channel capacity maximization using NQHN approach at
heterogeneous network
Savita Patil1
, A. M. Bhavikatti2
1
AMC Engineering College, India
2
Computer Science and Engineering, BKIT, India
Article Info ABSTRACT
Article history:
Received Jun 30, 2018
Revised Feb 7, 2019
Accepted Mar 9, 2019
In present scenario, the high speed data transmission services has pushed
limits for wireless communication network capacity, at same time
multimedia transmission in real-time needs provision of QoS, therefore the
network capacity and small cell coverage has comes with lots of challenges.
Improving the channel capacity and coverage area within the available
bandwidth is necessary to provide better QoS to users, and improved channel
capacity for the FCUs and MCUs in network. In this paper, we are proposing
an NQHN approach that incorporate with efficient power allocation,
improving the channel capacity by optimized traffic scheduling process in a
small cell HetNets scenario. This work efficiently handle the interference
with maintaining the user QoS and the implemented power controller uses
HeNB power as per the real time based approach for macro-cell and femto-
cell. Moreover, we consider the real traffic scenario to check the performance
of our proposed approach with respect to existing algorithm.
Keywords:
Base stations (BSs)
Optimized traffic scheduling
(OTS)
Wireless communication (WC)
Quality-of-service (QoS)
Novel QoS aware HetNets
(NQHN)
Copyright © 2019 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Savita Patil,
AMC Engineering College,
Bangalore-83, India.
Email: sampatil949@gmail.com
1. INTRODUCTION
HetNets are a future generation WC networks that have been used to reduce the unsighted area of
the mobile communication with improving the present network coverage area in compared with traditional
WC networks. A WAN (Wide Area Network) can use macro-cell and, femto-cell or pico-cell to provide the
wide coverage area in a wireless coverage environment such as; homes, office buildings, underground areas
and an open outdoor area. The usage of mobile data are growing exponentially through several type of
communication applications like as; multimedia phones and, Wi-Fi etc. It is not possible to satisfy the larger
communication requirements like as coverage and throughput using the traditional WC network by macro-
cell BSs (Base Stations).
Moreover, to provide the novel applicant methodology in LTE-A based WC networks, the HetNets
has been propose in [1-3] that enhances the data rate and network area coverage. In HetNets, there are several
low-energy and low-cost femto-cell are distributed around the macro-cell BSs, said to be as femto-cell users,
which shares the same available spectrum bandwidth with the macro-cell users in order to get optimized
spectral efficiency in a cellular network. Therefore, the interference from the users of femto-cell to macro-
cell BSs should be monitor and control strictly, also the mitigation of interference is very necessary for the
control power based ‘resource allocation’ and used as practical approach in wireless HetNets [4, 5].
The resource allocation approach for HetNets has concern from many researchers and its importance is
growing extremely, the major aim in ‘resource allocation’ for existing femto-cell networks is to decrease the
received interference at macro-cell users, simultaneously achieve the femto-cells performance from using
power control approach that has been studied in [6-8].

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 4, August 2019 : 2593 - 2602
2594
In paper [6], they proposed an approach of interference mitigation in order to enhance the uplink
throughput via providing a stable interference threshold value, also through regulating transmit power of
femto-cell user. The two-tier femto-cell system has considered in [7], where resource allocation has provided
in both uplink and downlink for enhancing the sensitivity capacity of femto-cells users, decreasing the delay
timing at femto-cell users under macro-cell user interference constraint and quality service constraint for
sensitive users. In paper [8], they proposed an energy efficient HetNets by using game theory at resource
allocation process in a downlink transmission under a multichannel HetNets. It is point to be considered that
the mostly approach related to resource allocation required perfect knowledge of CSI (i.e., channel state
information) at transmitter side, however, it is generally considered that all system knowledge such as
perfect-CSI are accessible to femto-users, due to arbitrary behavior of incorrect channel estimation, channel
delays and wireless channels. Therefore, it is difficult for femto-users to acquire the desirable system
parameter values such as; interference power and channel gains from different networks. In paper [9, 10],
they proposed a robust optimization approach that exhibits robustifying resource allocation with imperfect-
CSI, which has drawn significant attention in order to handle the uncertainty in HetNets. The major aim of
power controller is to minimalize the power in transmission, therefore decrease the high power consumption
and reducing the inter-cell interferences in necessary as we discussed previously. Through properly
regulating the downlink power transmission as per resource block is necessary to get achievable bit-rate in
femto-cells, all interference that generated in small cell network can be reduce significantly.
Therefore improving the channel capacity and coverage area within available bandwidth is
necessary to provide better quality of service to users, though protecting macro-cell users in network through
maintaining the interference under a threshold level. The effectiveness QoS at traffic users is also key factor
and without any provision, the level of QoS can be mishandled in LTE-A (long-term evolution advanced)
based small network. Moreover, the increment in mobile users causes the degradation in QoS, due to its more
data usage (i.e., more bandwidth) applications. In this paper, we are proposing an NQHN approach that
incorporate with efficient power allocation, improving the channel capacity by optimized traffic scheduling
process in a small cell HetNets scenario. This work efficiently handle the interference with maintaining the
user QoS, the implemented power controller uses HeNB power as per the real time based approach for
macro-cell and femto-cell. Moreover, the power controller approach uses 3GPP [11] standard for dynamic
representation of efficient ‘power switching’ points and optimized traffic scheduling (OTS) approach to
perform QoS aware scheduling by considering traffic parameters with real-time HetNets condition. In result
section, we consider the number of femto-cell user and macro-cell users in a traffic scenario to check the
performance of our proposed approach and providing comparison analysis with existing algorithm.
2. LITERATURE SURVEY
In order to face the traffic related issues in WC networks, it is necessary to coordinate and utilize the
several large throughput ‘small-cell’ like as wireless LAN (local area networks). Moreover, the number of
large throughput ‘small-cell’ has considered in [12], where they constructed the small outdoor cells via
access set up points at indoors. To validate the system performance, the indoor-outdoor field measurement
has done in order to propagate in multiple direction; also, they focused on 3.5GHz that used in small-cell of
LTE-A system. In this paper [13], they used tool such name as stochastic geometry, also designed a
framework model for the downlink data-rate coverage probability in a small cell network with enabling
MIMO at wireless backhaul. The small cell network is consist of several small cells, which can configured
either in out-band and in-band types of backhaul under an assured probability. The user performance has
consider in hierarchical network and limited through several interferences sources such as; small-cell BS
interference, backhaul interfaces, etc. The effect of channel difficulty under MIMO and wireless backhaul
faces long-term channel arrangement, where the access link involved in both long and short term of
channel effects.
The general grid approach has become stubborn as per the increasing in network size, also it cannot
handle the structure of outgeneral networks, therefore it is become challenging to compute the accurate
performance of WC cellular network, because of propagation effect in path and network prototype
complexity. Therefore, a way should be there in order to simulate the cellular networks and in [14],
the several network model was compared by simulation. However, estimating the performance of network
via simulation can deliver understanding on specific setting thus the outcome may not differ at other
scenarios as well as the computational complexity, in [15] they also proposed the work based on cellular
network enhancement with fixed approach. Their proposed approach has efficiently work to achieve the
optimum result at a small-cell HetNets, while considering the large HetNets with this approach may create
more complexity. Furthermore, the cooperation with sophisticated BS and local ‘or’ global CSI are required
to get output of achievable performance under a communal network setting.

Int J Elec & Comp Eng ISSN: 2088-8708 
Channel capacity maximization using NQHN approach at heterogeneous network (Savita Patil)
2595
The application of WC dense devices and services access required high consumption of energy, due
to real time processing, for that energy efficient design has consider for financial and environment cause.
Therefore, it become trend to find out best energy efficient process and as per our information, generally
OFDM is used in small cell HetNets to provide power allocation, higher energy efficiency, bandwidth
allocation in wireless backhaul, and user QoS. Where the QoS is novel approach for this field, which
investigated less and in paper [16], they proposed an energy efficient allocation technique for wireless
backhaul network that based on OFDM access HetNets small cell. There are also some existing technique of
resource allocation, which increases the throughput and increases the efficiency of energy through allocating
dual transmit power level at individual small-cell BS to users and channel bandwidth, that based on circuit
power ingestion and CSI. The present backhaul networks consist of statically resource allocation that result
little allocations when the several small-cells are present in a cellular network with given resources,
therefore, in [17] they proposed new access backhaul network design that based on Smart-GW (Gateway) in
between BSs and small-cell. Specifically, they applied modest LTE protocol, which add the Smart-GW into
advanced LTE HetNets.
In paper [18], they proposed a random spatial methodology where base stations are modified as
spatial PPP (Poisson point process), these type of random network topology has widely used in wireless
ad-hoc network [19-22] and it has performed well under small cell network scenario where the position of BS
are in irregular form. In paper [23], they proposed LAA (‘licensed-assisted access’) for the investigation of
small cell network and a framework called LTE with unlicensed incumbent model has introduced here,
where they give expression for both transmission strategies; wireless fidelity (Wi-Fi) and LTE system under
an unlicensed spectrum. In [24-26], the point process has consider with the stochastic geometry theory, this
methodology shows the appropriate and tractable performance that can used to examine the throughput and
probability in cellular networks. In addition, a random spatial network approach can be used in different type
of network such as distributed antenna structures [27] and HetNets [28-31], but from the above study,
we have adopted that still a lag in optimizing the HetNets performance with maintaining the user QoS.
3. PROPOSED METHODOLOGY
Here, we consider femto-cells that has ability to avoid the interference with different channel
signals; also, deliver high quality data transmission to mobile users, therefore femto-cells enhances the
spectral efficiency at number of user per unit coverage area. Moreover, the BS present at shorter distance,
which help mobile terminals to get much energy efficiency through decreasing the transmission power and
that, increases the battery life. The use of femto-cells at indoor location, the macro-cells can also provide
much reliable service to outdoor users because of the overhead reduction. Figure 1 shows the proposed model
block diagram, which shows two major part such as power controller approach and optimized traffic
scheduling algorithm in a real-time streaming scenario with maintain users QoS, the QoS at heterogeneous
network dynamically considered for the users. In HetNets scenario, femto-cells users and macro-cells users
are makes request, for that acquired channel state and traffic information are forwarded to scheduling and
power controller process, so that we can achieve optimized trans-receiver BS (TBS) and user throughput.
Figure 1. Block Diagram of Proposed NQHN Approach
3.1. Optimized traffic scheduling (OTS) algorithm
In this section, we describe the optimized scheduling algorithm in order to handle the traffic
occurrence effectively in a small-cell HetNets, also provide acceptable capacity to a system. The acquired
channel state and traffic information are given input to OTS algorithm to make the scheduling result at a
period of time, which also based on utility computation function [32]. The utility function aim is obtain the
standardized QoS objective that realized through user network scenario and in general, the packet holding
time of a user are high so the requirement of QoS also become more for that user.

 ISSN: 2088-8708
2596
Algorithm for Optimized Traffic Scheduling (𝑂𝑇𝑆)
𝑆𝑡𝑒𝑝1: − for in t time period
𝑆𝑡𝑒𝑝2: − for traffic requested by individual user 𝑎
𝑆𝑡𝑒𝑝3: − Computing average time utility based function 𝐵[𝐴 𝑎(𝑡)]
𝑆𝑡𝑒𝑝4: − Computing maximal utility threshold function Ath
𝑆𝑡𝑒𝑝5: − if 𝐵[𝐴 𝑎(𝑡)] < Ath
𝑆𝑡𝑒𝑝6: − Anticipate 𝑎 user request
𝑆𝑡𝑒𝑝7: − end
𝑆𝑡𝑒𝑝9: − if request from a novel user 𝑐 comes
𝑆𝑡𝑒𝑝10: − Instantly computing novel time utility fun 𝐵[𝐴 𝑐(𝑡)]
𝑆𝑡𝑒𝑝11: − if 𝐵[𝐴 𝑐(𝑡)] < Ath
𝑆𝑡𝑒𝑝12: − New 𝑐 user request has not responded yet
𝑆𝑡𝑒𝑝13: − else
𝑆𝑡𝑒𝑝14: − Proceed for user request, start from step 1 and activate
power controller approach
𝑆𝑡𝑒𝑝16: − else
𝑆𝑡𝑒𝑝17: − Process continue
Moreover, the QoS has provided in controller multimedia transmission and, for real-time scenario,
we can use any data transmission so that the delay in performance may occurs. The delay and throughput
performance are major in lower priority users but it is not much critical, due to regulating the angle of delay
bounds that can vary utility functional metric instantly. In addition it is found that the above OTS algorithm
has achieve better performance in a period when the users number are not very large and the femto-cell users
move closely towards BS in HetNets. The user movement and handover request distant from the femto-cell
center needs more ‘load balancing’, which causes falls in system capacity and the performance services.
3.2. Robust user quality based power controller
A multiuser OFDM based HetNets is considered which contains 𝐷 number of femto-cell users
(FCUs) and communicating with associated femto-cell BSs (FCBSs) over 𝐸 number of subcarrier. FCUs are
used to utilize the macro-cell users (MCUs) via FC-BSs, where 𝐷 and 𝐸 are varies according to active user’s
number and available subcarrier, that can be indexed as;
𝑑 ∈ 𝔇 ≜ {1, 2,3 . . . . . , 𝐷} (1)
𝑒 ∈ ℰ ≜ {1, 2,3 . . . . . , 𝐸} (2)
Here, we assumed that ℰ ≥ 𝔇, the subcarrier bandwidth is assumed to be 𝐹Hz that is very less
compare to the wireless channel bandwidth, therefore applying Shannon Hartley Theorem (SHT) [33]
corresponding FCU data rate 𝑑 at subcarrier 𝑒 is written as.
𝑔 𝑑,𝑒 = 𝐹ℎ 𝑑,𝑒 log2 (1 +
𝐼 𝑑,𝑒 𝐽 𝑑,𝑒
𝐾𝑑,𝑒
⁄ ) (3)
Where, 𝐾𝑑,𝑒 denotes the 𝑑 FCU background noise at 𝑒 subcarrier, ℎ 𝑑,𝑒 denotes the 𝑑 FCU
subcarrier assignment at 𝑒 subcarrier, 𝐼 𝑑,𝑒 denotes the 𝑑 FCU transmit power at 𝑒 subcarrier and 𝐽 𝑑,𝑒 denotes
the 𝑑 FCU direct channel gain at 𝑒 subcarrier. The subcarrier assignment will be 0 or 1 that shows the 𝑒
subcarrier is used by 𝑑 FCU or not. The major constraint is battery capacity at 𝑚th FCU transmitter and the
individual FCU can use limited amount of power, therefore the constraint is given as;
∑ ℎ 𝑑,𝑒
𝐸
𝑒=1 𝐼 𝑑,𝑒 ≤ 𝐼 𝑑
𝑚𝑎𝑥
, ∀𝑑 ∈ 𝔇 (4)
In (4), 𝐼 𝑑
𝑚𝑎𝑥
denotes the maximal power transmit of FCU and the data-rate should fulfil the minimal
requirement of 𝑑 FCU QoS that written as;
∑ 𝑔 𝑑,𝑒
𝐸
𝑒=1 ≥ 𝐺 𝑑
𝑚𝑖𝑛
, ∀𝑑 ∈ 𝔇 (5)

2597
where, 𝐺 𝑑
𝑚𝑖𝑛
shows the minimal requirement rate of 𝑑 FCU and the interference constraint of total cross-tier
under femtocell networks to the MCU receiver part can be described as;
∑ ∑ ℎ 𝑑,𝑒
𝐸
𝑒=1
𝐷
𝑑=1 𝐼 𝑑,𝑒 𝑁𝑑,𝑒 ≤ 𝑀 𝑖𝑙
(6)
where, the interference level at MCU receiver is denote by 𝑀 𝑖𝑙
and the maximization of sum rate via power
controller at HetNets can be given as;
𝑚𝑎𝑥
ℎ 𝑑,𝑒 𝐼 𝑑,𝑒
∑ ∑ 𝑔 𝑑,𝑒
𝐸
𝑒=1
𝐷
𝑑=1
∑ ℎ 𝑑,𝑒
𝐸
𝑒=1 = 1, ∀𝑑 ∈ 𝔇, 𝑍1
∑ ℎ 𝑑,𝑒 𝐼 𝑑,𝑒
𝐾
𝑘=1 ≤ 𝐼 𝑑
𝑚𝑎𝑥
, ∀𝑑 ∈ 𝔇 , 𝑍2 (7)
where, 𝑍1 shows the individual 𝑒 subcarrier that assigned to each FCU, 𝐼 𝑑,𝑒 = 1 signify the 𝑒th-subcarrier
that used by 𝑑 FCU, and 𝑍2 shows the power transmission constraint of 𝑑 FCU over the subcarrier.
∑ 𝐺 𝑑,𝑒
𝐸
𝑒=1 ≥ 𝐺 𝑑
𝑚𝑖𝑛
, ∀𝑑 ∈ 𝔇 , 𝑍3 (8)
Equation (8) ensure the QoS for individual FCU,
∑ ∑ ℎ 𝑑,𝑒 𝐼 𝑑,𝑒 𝑁𝑑,𝑒
𝐸
𝑒=1
𝐷
𝑑=1 ≤ 𝑀 𝑖𝑙
, 𝑍4
ℎ 𝑑,𝑒 ∈ {0,1}, ∀𝑑 ∈ 𝔇, 𝑒 ∈ ℰ, 𝑍5 (9)
Where, 𝑍4 shows the total power interference at MCU receiver side, the major difficulty is
ℎ 𝑑,𝑒 = 1 is mixed integer and non-convex programming difficulty and 𝑁𝑑,𝑒 shows the channel gains feedback
that provided by MCU to FCU. In current development, mostly of the researchers has focused on power
allocation strategy in HetNets [34] that focus on enhancement power with considering perfect CSI [35].
In particle, the present of quantization errors and estimation error causes the channel uncertainty that is
harmful for MCUs and, in order to decrease that, we should consider some advancement technique, which
can deal with these uncertainties. Therefore, here we use robust user quality based power controller and,
the (8) and (9) can be rewritten in the probability form such as;
𝑚𝑎𝑥
∑ ∑ 𝑔 𝑑,𝑒
𝐸
𝑒=1
𝐷
𝑑=1 𝑠. 𝑡. 𝑍1, 𝑍2, 𝑍5
P{∑ 𝑔 𝑑,𝑒 ≤ 𝐺 𝑑
𝑚𝑖𝑛𝐸
𝑒=1 } ≤ 𝑄 𝑑, ∀𝑑 ∈ 𝔇, 𝑍6 (10)
P{∑ ∑ ℎ 𝑑,𝑒 𝐼 𝑑,𝑒 𝑁𝑑,𝑒
𝐸
𝑒=1
𝐷
𝑑=1 > 𝑀 𝑖𝑙
} ≤ 𝔷 (11)
where, both (10) and (11) ensure the MCU and FCU QoS via using the probability function
and 𝔷 and 𝑄 𝑑 shows the threshold value of outage probability. Here, OFDM feature technique has consider, so
there the subcarrier are independent from each other and each FCU data are mutually independent from all
subcarrier and the set of data-rate is defined as;
𝑆 𝑒
= {𝑔 𝑑,𝑒 ≤ 𝐺 𝑑
𝑚𝑖𝑛
}, (12)
𝑆 = {∑ 𝑔 𝑑,𝑒
𝐸
𝑒=1 ≤ 𝐺 𝑑
𝑚𝑖𝑛
} (13)
where, 𝑆 set is an intersection subset of 𝑆 𝑒
such as;
𝑆̅ ⊂ 𝑆 = 𝑆1
⋂ 𝑆2
… 𝑆 𝑒
. (14)
After applying the probability analysis, we got following relationship;
{ 𝑆̅} ≤ P{𝑆} = ∏ P𝐸
𝑒=1 {𝑆 𝑒} (15)

 ISSN: 2088-8708
2598
Further, it can be written as;
P{∑ 𝑔 𝑑,𝑒
𝐸
𝑒=1 ≤ 𝐺 𝑑
𝑚𝑖𝑛
} ≤ ∏ P𝐸
𝑒=1 {𝑔 𝑑,𝑒 ≤ 𝐺 𝑑
𝑚𝑖𝑛
} (16)
The probabilistic rate constraint for upper bound should satisfies the required outage probability
during the worst scenario, therefore the (10) can be written as;
Max P{∑ 𝑔 𝑑,𝑒
𝐸
𝑒=1 ≤ 𝐺 𝑑
𝑚𝑖𝑛
} ≤ ∏ P𝐸
𝑒=1 {𝑔 𝑑,𝑒 ≤ 𝐺 𝑑
𝑚𝑖𝑛
} ≤ 𝑄 𝑑 (17)
In order to provide deterministic outage probability the above (17) can be written as;
𝐺 𝑑
𝑚𝑖𝑛
≤ 𝐹ℎ 𝑑,𝑒log2 (1 +
𝐼 𝑑,𝑒
𝐾 𝑑,𝑒
J 𝐽 𝑑,𝑒
−1
(𝑄 𝑑 /𝐸)) , ∀𝑑 ∈ 𝔇. (18)
The satisfaction of above (18) ensure the power transmission with the considered outage probability,
similarly, the probabilistic interference (11) can be modified as;
ℎ 𝑑,𝑒 𝐼 𝑑,𝑒 ≤
𝑀 𝑖𝑙
𝐸N 𝑁 𝑑,𝑒
−1 ( √1−𝔷𝐷𝐸
)
, , ∀𝑑 ∈ 𝔇, ∀𝑒 ∈ ℰ. (19)
Therefore, the (19) equation said to be deterministic and it is require to keep it as presentable, moreover,
the power controller difficulty without any information can be represented as;
max
∑ ∑ 𝑔 𝑑,𝑒
𝐸
𝑒=1
𝐷
𝑑=1 𝑠. 𝑡. 𝑍1, 𝑍2, 𝑍5 (20)
𝐹ℎ 𝑑,𝑒log2 (1 +
ℎ 𝑑,𝑒
𝐾 𝑑,𝑒
J 𝐽 𝑑,𝑒
−1
(𝑄 𝑑 /𝐸)) ≥ 𝐺 𝑑
𝑚𝑖𝑛
, 𝑑 ∈ 𝔇. (21)
Here, we have applied the inverse collective distribution function at variable such as 𝐽 𝑑,𝑒 and 𝑁𝑑,𝑒, and those
can be written as J 𝐽 𝑑,𝑒
−1
and N 𝑁 𝑑,𝑒
−1
.
𝐸ℎ 𝑑,𝑒 𝐼 𝑑,𝑒N 𝑁 𝑑,𝑒
−1
( √1 − 𝔷𝐷𝐸
) ≤ 𝑀 𝑖𝑙
. (22)
Generally, the FCUs can acquire the CSI through the channel estimation in between FCUs and
MCUs, so these can cause some difficulty at CSI acquisition. Therefore, here we consider the independent
model of Gaussian distribution to handle the uncertainty parameters. Moreover, the channel gain from the
FCUs transmitter to BS is acquire via a robust user-𝑞𝑢𝑎𝑛𝑡𝑖𝑧𝑒𝑟 and the feedback is given back to
corresponding FCUs transmitter.
4. RESULT ANALYSIS
In this section, we presented the simulated results that is simulated in Matlab 2016b environment
and the system configuration; Intel i5 processor, 2GB NVidia graphics-card, 8GB RAM and Windows 10 OS
(Operating System). Moreover, we consider the several necessary parameters that generally used in traffic
condition scenarios; gain of antenna 14dBi, maximum and minimum transmit power are 20dBm and 0dBm,
transmit power of BS 43dBm, speed of users 3Km/h, Urban type channel model, correlation distance 40m,
radius of cell 1Km, carrier and subcarrier bandwidth 2000Mhz and 375KHz, system bandwidth 10MHz
and etc.
With considering these traffic parameters, we have taken 1 macro-cell, 10 femto-cell, 15 number of
MCUs, 60 subcarrier and 100 number of FCUs, and the location of femto-cell, MCUs and FCUs are
generated randomly. Here, Figure 2 represents the proposed network prototype and further we will focus on
4, 7, 8 and 10. The inputs of femto cells were selected arbitrary under real-time scenario such as video
data [36] and audio [37] to provide realistic multimedia transmission. The increment of mobile users will
trigger additional signal interference at FCUs and MCUs in small cells scenario.

2599
Figure 2. Proposed Network Prototype
Figure 3 shows the transmission power that used by different algorithm in cell 4, where the existing
algorithm HARQ-CC [38] and HARQ-T1 [39] has used average power of 16.52dBm and 20dBm, where our
propose model NQHN has used 14.33 dBm average power that is 28% lesser compare to HARQ-T1 [39] and
13.25% lesser compare to HARQ-CC [38].
Figure 4 shows the computed throughput by different algorithm in cell 4, where the existing
algorithm HARQ-CC [38] and HARQ-T1 [39] has obtained average throughput of 124 Mbps and 88.69
Mbps, where our propose model NQHN has got 135 Mbps average throughput that is 7.9% more compare to
HARQ-CC [38] and 34% more compare to HARQ-T1 [39].
Figure 5 shows the transmission power that used by different algorithm in cell 7, where the existing
algorithm HARQ-CC [38] and HARQ-T1 [39] has used average power of 12.2dBm and 17.16dBm, where
our propose model NQHN has used 10.35 dBm average power that is 39% lesser compare to HARQ-T1 [39]
and 15.14% lesser compare to HARQ-CC [38].
Figure 6 shows the computed throughput by different algorithm in cell 7, where the existing
algorithm HARQ-CC [38] and HARQ-T1 [39] has obtained average throughput of 120 Mbps and 99.7 Mbps,
where our propose model NQHN has got 142 Mbps average throughput that is 15.4% more compare to
HARQ-CC [38] and 29.8 % more compare to HARQ-T1 [39].
Figure 3. Power (dBm) in Cell 4 Figure 4. Throughput (bps) in Cell 4

 ISSN: 2088-8708
2600
The transmission power used in cell 8 by different algorithm has shown in Figure 7, where,
the average power used by proposed NQHN is 8.44 dBm, which is 1 % more compare to HARQ-CC [38] and
46.5% less compare to HARQ-T1 [39]. Moreover, the throughput in Mbps are obtained by different
algorithm in cell 8 has shown in Figure 8, where, the average throughput of our proposed approach is 142
Mbps that is 29 % more compare to HARQ-CC [38] and 27.6% more compare to HARQ-T1 [39].
Similarly, Figure 9 shows the transmission power that used by different algorithm in cell 10 where,
the average power used by proposed NQHN is 12.43 dBm, which is 12.3 % lesser compare to HARQ-CC
[38] and 32.66% less compare to HARQ-T1 [39]. Moreover, Figure 10 shows the computed throughput by
different algorithm in cell 10 where, the average throughput of our proposed approach is 134 Mbps, which is
12 % more compare to HARQ-CC [38] and 32.7% more compare to HARQ-T1 [39].
Figure 11 shows the average throughput of considered HetNets, where our proposed approach got
25 Mbps, HARQ-CC [38] got 22 Mbps and HARQ-T1 [39] got 19 Mbps throughput rate. Moreover,
Figure 12 shows the computed delay from different algorithm in end-to-end considered HetNets scenario,
where NQHN got 0.5 sec of delay, which is 61% less delay compare to HARQ-CC [38] and 90% less
compare to HARQ-T1 [39].
Figure 11. Average Throughput (bps) Figure 12. Computed Delay from
different Algorithm

2601
5. CONCLUSION
The traffic and QoS related issues in WC networks are growing continuously. Therefore, it is
necessary build the small outdoor cells (i.e., macro-cell) by setup the access points (i.e., femto-cell). In this
paper, we proposed Novel QoS aware HetNets (NQHN), which contains OTA and robust user quality based
power controller in order to provide QoS of macro-cell HetNets and improve system capacity. The optimized
scheduling algorithm has used in order to handle the traffic occurrence effectively in a small-cell HetNets
that also provide acceptable capacity to a system. The acquired channel state and traffic information are
given input to OTS algorithm to make the scheduling result at a period. Moreover, the quantization errors and
estimation error causes the channel uncertainty that is harmful for MCUs and for that we consider the robust
user quality based power controller. In result section, we have shown sum rate maximization for a two-tier
HetNets with multiple femto-cells and one macro-cell, where our proposed approach has got 11% more
throughput compare to HARQ-CC [38] and 22% more throughput compare to HARQ-T1 [39],
which channel capacity enhancement by our proposed model.
REFERENCES
[1] B. Balavenkatesh, et al., “Enhancement of QoS of VOIP over Heterogeneous Networks by Improving Handoff
Speed and Throughput,” 2009 International Conference on Advances in Computing, Control, and
Telecommunication Technologies, Trivandrum, Kerala, pp. 840-844, 2009.
[2] A. Umer, et al., “Coverage and Rate Analysis for Massive MIMO-Enabled Heterogeneous Networks with
Millimeter Wave Small Cells,” 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, pp.
1-5, 2017.
[3] Z. Liu and Y. Ji, “Intercell Interference Coordination under Data Rate Requirement Constraint in LTE-Advanced
Heterogeneous Networks,” 2014 IEEE 79th Vehicular Technology Conference (VTC Spring), Seoul, pp. 1-5, 2014.
[4] W. Xia, et al., “Large System Analysis of Resource Allocation in Heterogeneous Networks with Wireless
Backhaul,” IEEE Transactions on Communications, vol/issue: 65(11), pp. 5040-5053, 2017.
[5] W. Xia, et al., “Energy-efficient task scheduling and resource allocation in downlink C-RAN,” 2018 IEEE Wireless
Communications and Networking Conference (WCNC), Barcelona, Spain, pp. 1-6, 2018.
[6] H. S. Jo, et al., “Interference mitigation using uplink power control for two-tier femtocell networks,” IEEE Trans.
Wirel. Commun, vol. 8, pp. 4906-4910, 2009.
[7] H. J. Zhang, et al., “Resource allocation in spectrum-sharing OFDMA femtocells with heterogeneous services,”
IEEE Trans. Commun, vol. 62, pp. 2366-2377, 2014.
[8] T. Mao, et al., “Distributed energy-efficient power control for macrofemto networks,” IEEE Trans. Veh. Technol,
vol. 65, pp. 718-731, 2016.
[9] A. M. Abdelhady, et al., “Energy-Efficient Resource Allocation for Phantom Cellular Networks with Imperfect
CSI,” IEEE Transactions on Wireless Communications, vol/issue: 16(6), pp. 3799-3813, 2017.
[10] A. Ben-Tal and A. Nemirovski, “Selected Topics in Robust Convex Optimization,” Math. Program, vol. 112,
pp. 125-158, 2007.
[11] F. Rezaei, et al., “LTE PHY performance analysis under 3GPP standards parameters,” 2011 IEEE 16th
International Workshop on Computer Aided Modeling and Design of Communication Links and Networks
(CAMAD), Kyoto, pp. 102-106, 2011.
[12] H. Fukudome, et al., “Measurement of 3.5 GHz Band Small Cell Indoor-Outdoor Propagation in Multiple
Environments,” European Wireless 2016; 22th European Wireless Conference, Oulu, Finland, pp. 1-6, 2016.
[13] H. Tabassum, et al., “Analysis of Massive MIMO-Enabled Downlink Wireless Backhauling for Full-Duplex Small
Cells,” IEEE Transactions on Communications, vol/issue: 64(6), pp. 2354-2369, 2016.
[14] E. Kurniawan and A. Goldsmith, “Optimizing cellular network architectures to minimize energy consumption,”
Proc. 2012 IEEE Int. Conf. Commun, 2012.
[15] E. Bj¨ornson and E. Jorswieck, “Optimal resource allocation in coordinated multi-cell systems,” Found. Trends
Commun. Inf. Theory, vol/issue: 9(2-3), pp. 113-381, 2013.
[16] H. Zhang, et al., “Downlink Energy Efficiency of Power Allocation and Wireless Backhaul Bandwidth Allocation
in Heterogeneous Small Cell Networks,” IEEE Transactions on Communications, vol/issue: 66(4), pp. 1705-1716,
2018.
[17] A. S. Thyagaturu, et al., “SDN-Based Smart Gateways (Sm-GWs) for Multi-Operator Small Cell Network
Management,” IEEE Transactions on Network and Service Management, vol/issue: 13(4), pp. 740-753, 2016.
[18] J. G. Andrews, et al., “A tractable approach to coverage and rate in cellular networks,” IEEE Trans. Commun,
vol/issue: 59(11), pp. 3122-3134, 2011.
[19] S. P. Weber, et al., “Transmission capacity of wireless ad hoc networks with outage constraints,” IEEE Trans. Inf.
Theory, vol/issue: 51(12), pp. 4091-4102, 2005.
[20] F. Baccelli, et al., “An Aloha protocol for multihop mobile wireless networks,” IEEE Trans. Inf. Theory, vol/issue:
52(2), pp. 421-436, 2006.
[21] A. M. Hunter, et al., “Transmission capacity of ad hoc networks with spatial diversity,” IEEE Trans. Wireless
Commun, vol/issue: 7(12), pp. 5058-5071, 2008.
[22] R. H. Y. Louie, et al., “Open-loop spatial multiplexing and diversity communications in ad hoc networks,” IEEE
Trans. Inf. Theory, vol/issue: 57(1), pp. 317-344, 2011.

 ISSN: 2088-8708
2602
[23] P. R. Li and K. T. Feng, “Energy-Efficient Channel Access for Dual-Band Small Cell Networks,” GLOBECOM
2017 - 2017 IEEE Global Communications Conference, Singapore, pp. 1-6, 2017.
[24] D. Stoyan, et al., “Stochastic Geometry and Its Applications,” Wiley, 1987.
[25] F. Baccelli and B. Błaszczyszyn, “Stochastic Geometry and Wireless Networks,” Now Publishers Inc., 2009.
[26] M. Haenggi, et al., “Stochastic geometry and random graphs for the analysis and design of wireless networks,”
IEEE J. Sel. Areas Commun, vol/issue: 27(7), pp. 1029-1046, 2009.
[27] J. Zhang and J. Andrews, “Distributed antenna systems with randomness,” IEEE Trans. Wireless Commun,
vol/issue: 7(9), pp. 3636-3646, 2008.
[28] V. Chandrasekhar and J. Andrews, “Spectrum allocation in tiered cellular networks,” IEEE Trans. Commun,
vol/issue: 57(10), pp. 3059-3068, 2009.
[29] W. C. Cheung, et al., “Throughput optimization, spectrum allocation, and access control in two-tier femtocell
networks,” IEEE J. Sel. Areas Commun, vol/issue: 30(3), pp. 561-574, 2012.
[30] D. Cao, et al., “Optimal base station density for energyefﬁcient heterogeneous cellular networks,” Proc. 2012 IEEE
Int. Conf. Commun, 2012.
[31] H. Dhillon, et al., “Modeling and analysis of K-tier downlink heterogeneous cellular networks,” IEEE J. Sel. Areas
Commun, vol/issue: 30(3), pp. 550-560, 2012.
[32] I. F. Chao and C. S. Chiou, “An enhanced proportional fair scheduling algorithm to maximize QoS traffic in
downlink OFDMA systems,” 2013 IEEE Wireless Communications and Networking Conference (WCNC),
Shanghai, 2013.
[33] O. Rioul and J. C. Magossi, “On Shannon’s Formula and Hartley’s Rule: Beyond the Mathematical Coincidence,”
Entropy, vol/issue: 16(9), pp. 4892-4910, 2014.
[34] A. H. Arani, et al., “Distributed learning for energy efficient resource management in selforganizing heterogeneous
networks,” IEEE Trans. Veh. Technol, vol. 66, pp. 9287-9303, 2017.
[35] H. J. Zhang, et al., “Downlink energy efficiency of power allocation and wireless backhaul bandwidth allocation in
heterogeneous small cell networks,” IEEE Trans. Commun, vol. 95, pp. 1-13, 2017.
[36] P. Seeling and M. Reisslein, “Video Transport Evaluation with H.264 Video Traces,” IEEE Communications
Surveys & Tutorials, vol/issue: 14(4), pp. 1142-1165, 2012.
[37] C. Bouras, et al., “A simulation framework for lte-a systems with femtocell overlays,” ACM New York, NY, USA,
2012.
[38] M. Sheng, et al., “Performance Analysis of Heterogeneous Cellular Networks with HARQ under Correlated
Interference,” IEEE Transactions on Wireless Communications, vol/issue: 16(12), pp. 8377-8389, 2017.
[39] Evolved Universal Terrestrial Radio Access (E-UTRA), “Radio Resource Control (RRC),” Protocol Specification,
document TS 36.331, Rev. 12.5.0, 3GPP, 2015.

Channel Capacity Maximization using NQHN Approach at Heterogeneous Network

More Related Content

What's hot (20)

Similar to Channel Capacity Maximization using NQHN Approach at Heterogeneous Network (20)

More from IJECEIAES (20)

Recently uploaded (20)

Channel Capacity Maximization using NQHN Approach at Heterogeneous Network