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14 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

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ENHANCING THE PERFORMANCE OF ULTRA MOBILE BROADBAND 

NETWORK USING ADAPTIVE INTERFERENCE AND NOISE 

CANCELLING TECHNIQUE 

 
1Ezema D. C, 2Eke James and 3Ifeagwu E.N. 
1&2Department of Electrical & Electronic Engineering, Enugu State University of Science and Technology 

(ESUT), Enugu State, Nigeria. 
3Federal University Otueke (FUO), Bayelsa State, Nigeria. 

DOI: https://doi.org/10.5281/zenodo.13991006 

 

Abstract: This paper focused on enhancing the performance of Ultra mobile broadband networks using Adaptive 

interference and noise cancelling technique. The test van used during the measurement was driven in the direction of one 

antenna sector, though there were overlaps from two sectors at some points. In addition, the Global Positioning System 

(GPS) was used to measure the distance between the base station and the mobile station, i.e., the transmitter–receiver (T-R) 

separation distances starting from 100m to several multiples of 100m. During the drive test, four numbers of functional base 

stations were seen and the ray tracer helped to pick the signal of the network of choice amongst several signals from various 

networks. This is achieved by setting the appropriate frequency on which the desired network operates. As the investigative 

base station operates at a frequency of 878.87MHz, the ray tracer helped to locate the base station that transmits at that 

particular frequency. However, the signal strengths of the signals were recorded at various points and the distance of these 

points from the reference point of the base station also recorded. Results obtained showed that using adaptive interference 

and cancelling technique yielded a bit error rate in the range of 10-2 to 10-3. 

Keywords: broadband, noise cancelling, Compensating, Global Positioning System (GPS). 

 

1.0 INTRODUCTION 

In ultra-mobile broadband communication network, the system throughput as well as the throughput at a cell edge 

is among the most important evaluation measures for the requirements on the system performance (Lee W., 2011). 

Among many candidates on the multiple access schemes, frequency hopping orthogonal frequency division 

multiple access (FH-OFDMA) has been considered as one of the promising packet-based transmission techniques 

because FH-OFDMA can easily deal with the frequency selective fading channel and provide not only inner-cell 

orthogonality but also inter-cell interference (ICI) averaging effect. Thus, it enables us to construct a cellular 

network with a frequency reuse factor equal to one (Cox D.C, 2012). However, the ICI averaging effect would 

not work properly in case that there exist interferers with differently allocated power loading in adjacent cells, 

which makes the ICI pattern highly non-uniform (Ellingson, S.,2001). Therefore, it is very important to manage 

such non-uniform ICI, especially at cell edge. Several ICI mitigation techniques have been proposed to provide 

reliable communication to cell-edge users. Traditional frequency reuse schemes, such as a reuse factor 3 

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deployment, can significantly reduce the average ICI. However, it sacrifices accessible frequency resource in each 

cell in order to manage the interference levels so that the overall system capacity is quite limited. Another method 

proposed to handle the ICI is the coordinated symbol-repetition scheme (Bells, 2006). In this method, a repeated 

symbol mapping in the frequency domain is coordinated to be identical among adjacent cells. Then a user 

equipment can perform interference cancellation by using a minimum mean square error (MMSE) receiver in the 

frequency domain. This method can mitigate the ICI and increase the number of allowable interferers. But this 

scheme also needs additional resource due to the repetition, which ends up with limiting the system performance 

as well. As an advanced version of the traditional frequency reuse scheme, two frequency reuse schemes have 

been proposed. One is the partial frequency reuse (PFR) scheme, and the other is the soft frequency reuse (SFR) 

scheme (Rappaport, 2010). The PFR scheme partitions the whole frequency into two parts, a part with reuse factor 

1 and the other with reuse factor less than 1. The reuse factor 1 part is used only by inner cell users and the other 

part can be used by cell edge users. This scheme greatly solves the limitation of the traditional frequency reuse 

scheme. However, there still exists inefficiency due to the part for the cell-edge users. In the SFR scheme, a part 

of the whole frequency is reserved for the cell edge users and is kept being orthogonal among adjacent cells. The 

remaining frequency band can be used only by inner cell users for each cell. The transmission power can be 

amplified on the reserved band for the cell-edge users and inner cell users can also use this reserved frequency 

band. Therefore, the SFR scheme can achieve the reuse factor of 1. But, both the PFR and the SFR schemes need 

a strict cell planning, which is very hard for a practical system (Darren D. Chang and Olivier L. de Weck,). If an 

irregular cell pattern is given, such frequency reuse schemes requiring a strict cell planning would lead to an 

inefficient use of the spectrum. In a soft channel reuse (SCR) scheme using an erasure decoding (ED) method 

with downlink power control was proposed to handle;/the non-uniform ICI for downlink FHOFDMA systems. In 

this scheme, no strict cell planning is required due to its sub channel structure. It considered a multi cellular 

downlink OFDMA system where a power control is performed such that a base station (BS) can use full power 

for up to a pre-determined number of sub-channels allocated to cell-edge users while the rest of power is used for 

the sub channels allocated to inner-cell users. So all frequency resources can be utilized in each cell regardless of 

the cell shape. By erasing highly interfered symbols, it was shown that partially interfered signals can be 

effectively decoded without any prior knowledge on ICI. However, the ED method is only applicable to the case 

where a few sub-channels are allowed for the outer cell region such that ICI is concentrated to a small fraction of 

subcarriers in the sub-channel. As the number of cell edge users increases and demands more sub-channels, the 

performance of the ED scheme is deteriorated. In this work, we propose an adaptive interference and noise 

cancelling technique for ultra- mobile broadband network. The key operational features of the Ultra-Mobile Radio 

interface are support for high data transmission: 384kps with wide area coverage, 2Mbps with local coverage and 

high service flexibility: support of multiple parallel variable rate services on each connection. 

2.0 Interference on Ultra-Mobile Network (UMB) 

The type of interference experienced in ultra broadband network is the Multiple Access Interference or the Multi-

user Interference. Multiple Access Interference (MAI) is a factor, which limits the capacity and performance of 

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ultra band networks. In contrast to FDMA and TDMA techniques which are frequency bandwidth limited, all 

users transmit in the same frequency band and are distinguished at the receiver by the user specific spreading 

code. All other signals are not de-spread because they use different codes. These signals appear as interference to 

the desired user. As the number of users increase, the signal to interference ratio (SIR) decrease until the resulting 

performance is no longer acceptable. Thus, this multi–user interference must be reduced to achieve higher 

capacities. 

There are several ways of improving ultra broadband communication network. They are: 

Receiver beam forming, voice activation technology, power control, multiuser detection, using rake receivers and 

soft handoff. 

A simple equation for the uplink capacity U of a single Ultra broadband cell is given by (M. Viterbi, A. J. Viterbi 

(2008): 

   GNEWGU
Ob

2/1 
         (1)  

Where the value of 𝐸𝑏 𝑁0⁄  represents that required for adequate link performance. The scalar σ2 is the background 

noise power and S is the received signal power for each user. Finally, G is the ratio of the antenna gain for the 

desired user to that of interfering user in that cell. The value of G depends on the beam pattern for each user, but 

will roughly be proportional to the array size M. 

As a result, antenna arrays can improve the capacity in two ways: 

1. Increasing the antenna gain G and hence the array M. This reduces the average level of interference from each 

user in the cell, permitting a capacity increase.  

2. Reducing the required, 𝐸𝑏 𝑁0⁄  antenna array can provide increased space diversity at the base station, which 

can permit the receiver to operate at lower power signal. This increases the tolerance of the receiver to multiple 

access interference. 

3.0 MATERIALS AND METHODS 

In this section, we presented the characterization of the network under study. This was done by firstly carrying 

out measurement of the received power in dB. This is to ascertain the efficiency of the network. The measure data 

will now be used to develop the model for the network. 

3.1 Measurement Environment  

In this paper, field measurements were performed in Port Harcourt city, the capital of Rivers State of Nigeria 

using the existing wireless network of the Glo network which is a n ultraband base network. This helps to identify 

the nature of the typical propagation environment for the base station drive route. The drive test was intended to 

cover base station, using spectrum monitoring equipment such as ray tracers and spectrum analyzer.  

The Port Harcourt environment consisted of a sparsely built up environment and small houses with two to three 

floors and back yards. The houses were of clay bricks and metal roofs. The environment is made up of farm lands 

and few bushes around. Measurements of received signal strength will be made at intervals of 100m up to 700m 

from a reference point on the transmitting base station. The base station that was used for the research work is 

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shown below the base station belonging to ultra band network located at a location with Latitude 6˚ 45’N and 

Longitude: 4˚30’E of Port Harcourt city. The base station carrying sectorized antenna provides coverage for a 

large area such as suburban environment like Port Harcourt. The base station height is 36m and has a carrier 

frequency of 878.87MHz. 

3.2 Method of Data collection 

The measurement carried out on the first scene consist of a base station and a receiver antenna mounted on the 

roof top of the spectrum monitoring van housing the spectrum monitoring equipment. The base station antenna 

height is 36m while the mobile antenna was mounted on a 3m high test van. The carrier frequency of the network 

under consideration is 878.87MHz. 

 The test van used during the measurement was driven in the direction of one antenna sector, though there were 

overlaps from two sectors at some points. In addition, the Global Positioning System (GPS) was used to measure 

the distance between the base station and the mobile station, i.e., the transmitter–receiver (T-R) separation 

distances starting from 100m to several multiples of 100m.  

During the drive test, four numbers of functional base stations were seen and the ray tracer helped to pick the 

signal of the network of choice amongst several signals from various networks. This is achieved by setting the 

appropriate frequency on which the desired network operates.  

As base station operates at a frequency of 878.87MHz, the ray tracer helped to locate the base station that transmits 

at that particular frequency. However, the signal strengths of the signals were recorded at various points and the 

distance of these points from the reference point of the base station also recorded. These distances in kilometers 

were obtained using Global Positioning System (GPS). 

Generally speaking, the received signal strength from a base station decreases with distance when measured at 

various points along a radial path leading away from the base station. Ideally, signal-strength measurements would 

be made by monitoring and recording the signal received by a mobile unit in the van as it moves away from the 

base station. The measurements were carried out between 8.00am till 6.00pm each day for the whole period of 

measurement. 

3.3. Experimental Setup 

In other to provide good analysis of the network system that will ensure such good quality of service to users, 

quality of service parameters which will help in the system analysis are sought for. To achieve this, real time 

measurements were carried out and the data obtained were used to ascertain the efficiency of the existing 

infrastructure. It shows the base station transceiver (BTS) from where the transmitted signals emanate. The 

transmitted signal strengths are measured with the help of ray tracer. Ray tracer are software developed to indicate 

the presence of a signal and also its strength at a particular spot. The quality of the received signal in any channel 

can be ascertained by the ray tracer. While the ray tracer gives the level of signal strength, the global positioning 

system (GPS) shows the distance of the mobile unit or another transmitting base station from the base station 

under consideration. The GPS can also be used to measure the angular locations of the mobile unit with respect 

to the transmitting base station. The entire data that were measured were recorded and analyzed using spectrum 

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analyzer. Real time measurements of received signal strength, distance of mobile station (MS) from base station 

(BS), and angle of arrival (AOA) signal from the base station are carried out at a test bed. The results obtained 

were necessary in the simulation and performance analysis of adaptive antenna array in ultra mobile broadband 

network.  

 

 

 

 

 

 

 

 

 

 

 

3.4 Characterization and Development of the Model for the Network under Study  

Table 1: Technical Summary For Ultra-Mobile broadband network (UMB) 

Channel bandwidth 5MHZ 

Frequency band 1920MHz-1980MHz(uplink) 

2110MHz-2170MHz(downlink) 

Duplex mode  FDD and TDD 

Chip rate 3.84MCPS 

Frame Length 10ms 

Modulation QPSK 

Carrier Spacing 4.4MHz-5.2MHz 

Number of Chips/slot 2560chips 

Channel bit rate 5.7Mbps 

Channel coding Convolutional and Turbo codes 

 Physical layer Spreading Factors 4-256(uplink),4-512(downlink) 

Number of base station codes 512/frequency 

Power control period:Time slot 1500Hz rate 

Power control step size 0.5,1,1.5 and 2dB(variable) 

Power control range 80dB(uplink),30dB(downlink) 

Multirate Variable spreading and multicode 

Downlink RF channel Direct spread 

Pulse shaping  Root raised cosine, roll off=0.22 

Data types Packet and circuit switch 

Receiver  Rake 

Channel 

ID 

Rx Signal 

Distance  GPS 

Ray 

Tracer BTS 

Figure 3.3: Block diagram of the test bed 

setup 

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TABLE 2: AVERAGE OF THE RECEIVED SIGNAL STRENGTH MEASUREMENT CARRIED OUT 

(Cell site: 641, Frequency, f=878.87MHz, Transmitted Power, Tx =44.4dBm) 

Distance (m) RSS (dBm)  

100.00 -68.64 

200.00 -75.64 

300.00 -94.99 

400.00 -94.89 

500.00 -98.73 

600.00 -104.09 

700.00 -107.29 

TABLE 3: AVERAGE OF THE MEASUREMENT OF RECEIVED SIGNAL STRENGTH CARRIED 

OUT (Cell site:641, Frequency, f =878.87MHz, Transmitted Power, Tx =44.4dBm). 

TIME OF MEASUREMENT:8.00AM -6.00PM 

 

Distance(m) RSS (dBm)  

100.00 -67.94 

200.00 -74.16 

300.00 -86.84 

400.00 -87.04 

500.00 -91.64 

600.00 -99.33 

700.00 -103.13 

3.2.1 Characterization of the Ultraband Mobile network Radio Under Study. 

In order to characterize the mobile radio propagation environment under study, we made use of equation of 

pathloss to obtain the pathloss exponent, of the test bed environment and empirical propagation pathloss model 

for Portharcout. 

Therefore, in order to completely characterize a propagation environment under study, the following parameters 

must be known: 

1. Received signal strength obtained from field measurement 

2. The pathloss exponent, n,of the characterized environment 

3. An empirical propagation path loss model for the test bed environment. 

(I) Determination of Pathloss Exponent (n) of UltraTest Bed Environment  

Equation (1) predicts that the mean path loss PL(di) [dB] at a transmitter receiver separation di is (Feher,2006):  

PL(di) [dB] = PL(d0) [dB] + 10n log10(
𝑑𝑖

𝑑0
)  (2) 

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Where n = pathloss exponent 

PL(d0) = pathloss at known reference distance d0. That is: 

n = 
{𝑃𝐿(𝑑𝑖)− 𝑃𝐿(𝑑0)}

10𝑙𝑜𝑔10(
𝑑𝑖
𝑑0

)
   (3) 

(II) Empirical Path Loss Model For Mobile Radio Propagation Environment. 

The path loss model for free space is given by equation (3) (Gupta, et al, 2009) LPfs (dB) = 32.44 + 20 log10 (fc) 

+ 20log10 (di)  (4) 

Where LPfs is the free space path loss 

 fc is the carrier frequency in (MHz) 

 di is the distance between the base station (BS) and mobile station (MS) in (Km). 

The Hata path loss model for urban and suburban environment is given in equation (5) and (6) respectively. 

LPu (dB) = 69.55 + 26.16log10(fc )+ (44.9-6.55log10hb) log(di)-13.82log10(hb) - α (hm)  (5)  

LPs = Lpu – 2[log10(
𝑓𝑐

28
)]2 – 5.4  (6) 

Where; LPu is the pathloss prediction for urban area in dB, Lps is the pathloss prediction for suburban area in dB, 

α (hm) is the correlation factor for mobile station antenna height in dB, hb is the height of BS (km). hm is the height 

of MS.The correlation factor α (hm) for suburban is given as:  

α (hm) = [1.1log10fc – 0.7]hm – [1.56logfc – 0.8]       (7) 

(III) Computation of Pathloss Exponent  

Using equation (3) to obtain the pathloss, n, of the test bed environment where, Pr ,is the received signal strength 

values in dBm , Pt is the transmitting power in dB, Pathloss PL is the difference between Pt and Pr. 

 Let N, denote the numerator values and D denote denominator values, we computed the pathloss exponent of the 

test bed area as follows:  

d=[100,200,300,400,500,600,700] 

Pt=44.4dB 

Pr=[-67.94 -74.16 -86.84 -87.04 -91.64 -99.33 -103.13] 

PL(d)=Pt-Pr=44.4 -[-67.94 -74.16 -86.84 -87.04 -91.64 -99.33 -103.13] 

PL(d)=[112.34 118.56 131.25 131.44 136.04 143.73 147.53] 

 =[112.34 118.56 131.25 131.44 136.04 143.73 147.53]-[112.34] 

 =[0 6.22 18.91 19.10 23.70 31.39 35.19 ] 

N =134.51 

D=∑ [10log10 [1 2 3 4 5 6 7 ]] 

D=0+3.01+4.77+6.02+6.99+7.78+8.45 

 =37.02 

Therefore, n= N/D 

Pathloss exponent, n =3.63  

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The pathloss exponent, n, which characterizes the propagation environment under study (test bed 

area/environment) is obtained from the measured data and is 3.63. 

(IV) Developed Ultramobile broadband Propagation Pathloss Model For Test bed area. 

Using equation (8) and equation (9) and substituting fc =878.87MHz, 

hb= 0.036,hm=0.0018, α (hm)=3.99,we obtain the value for the pathloss model for Portharcourt as: 

LPu(dB) = 69.55 + 26.16log10(878.87)+(44.9-6.55log10(0.036)) log(di)-13.82log10(0.0018) - 3.99 (8) 

LPu(dB) =104.63+54.36Log(di)  (9) 

From equation 6, we obtain: 

LPs = 104.63+54.36Log(di) – 2[log10(
878.87

28
)]2 – 5.4 (10) 

LPs (do)=94.74dB. 

But, re-arranging equation (3.1), so that: 

PL(di) [dB]=Lp(di)=empirical pathloss model for Portharcourt 

PL(d0) = LPs(do) = pathloss for Portharcourt at known reference distance d0 

n = 3.63 =pathloss exponent. 

Therefore, the empirical path loss model for Portharcourt is: 

Lp(di)=94.74 + 36.3Log(di) (11) 

3.4 The Propose Developed Mathematical Signal Model. 

A possible method of predicting the received signal power (RSS) for the test-bed environment is propose by 

(Chipcon,2007) as shown in equation  

RSSI = -10n log10(
𝑑𝑖

𝑑0
) + A  (12) 

Where; 

RSSI = the signal power at the receiver 

n= pathloss exponent 

di = distance between the transmitter and receiver 

do= reference distance from the transmitting base station i.e.100m 

A = the RSS at a hundred meter distancefrom transmitting base station 

The value for the pathloss ,n, exponent was computed to be 3.63. The value for A; which is the received signal 

power at 100 meter from the transmitter, and is found to be -67.94 dBm. Putting these values back to the model 

represented by equation (12).and re-arranging equation (12), so that y denotes RSSI and x denote
𝑑𝑖

𝑑𝑜
, our propose 

model which can be used in predicting received signal strength is: 

y = −36.3log10 𝑥 – 67.94  (13) 

Equation (13) was used in predicting the received signal strength of the test bed environment. 

 

 

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0 5 10 15 20 25 30 35 40
10

-2

10
-1

10
0

Antenna Element M=4 and Rake fingers=4

Eb/Nb[dB]

B
E

R

 

 

Conventional beamforming

LMS algorithm

4.0 RESULTS AND DISCUSSIONS 

Comparison of Bit Error Rate Performance of Ultra-Broadband Network Using Adaptive Interference and 

cancelling technique Algorithm and Conventional Beam forming Algorithm 

Figure 2 show the comparison of the receiver average BER performance of ultra-mobile broadband network with 

LMS adaptive beam forming algorithm and conventional beam forming algorithm. The mean BER is improved 

with the LMS adaptive beam forming compared to the conventional beam forming algorithm. The BER of the 

LMS is of order 10-2 at Eb/No≤ 10dB 

 

 

 

 

 

 

 

 

 

 

 

Figure 2; BER Vs. Eb/No Performance of ultra-mobile broadband network using LMS Adaptive Beam forming 

Algorithm compared with Conventional Beam forming Algorithm. 

5.0 CONCLUSION 

The improvement of ultra mobile broadband network using adaptive interference and noise cancellation technique 

was investigated for a conventional narrow band beam former by varying the array elements and the number of 

interferers which revealed significant improvement as these numbers increase for odd numbered arrays. Hence, 

interference is greatly suppressed as the odd numbered array elements increases. From the charts, it was observed 

that the signal-to-interference and noise ratio depends on the number of antenna element, the inter-element 

spacing between the arrays and the number of interferers. There was great improvement in the SINR when odd 

numbered elements were used with inter-element spacing of d=0.5 in the presence of large interferers. Adaptive 

antenna as observed from our analysis showed greater improvement in the SINR over sectorized antenna in the 

presence of large interferers. Finally, improving the interference suppression and noise reduction capabilities of 

any antenna system in the presence of large interferers, increases the capacity of that system deploying such an 

antenna. Therefore, adaptive antenna proposed here will increase capacity of the ultra mobile broadband , thus 

making the network to accommodate more subscribers per base station. 

 

 

 

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Rappaport, T.S., (2010), “Smart Antennas: Adaptive Arrays, Algorithms and Wireless Position Location”, IEEE 

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Wells, M.C., (2006), “Increasing the capacity of GSM cellular radio using adaptive antennas”, IEEE proceedings, 

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