A Matlab Based Cellular Mobile Communication Laboratory

Published Date: 02 Nov 2017

Abstract – Cellular mobile communication (CMC) is one of the fastest growing sectors in telecommunication industry worldwide. The users of CMC increased rapidly in the last two decades globally. To address the high demand of CMC services, hands-on experienced workforce is a prerequisite that are primarily supposed to be provided by the universities. However, because most universities lack heavily from sufficient funding, resources, and facilities, it is difficult to setup physical laboratory of CMC to provide students with hands-on experiences. This huge investment for physical setup can be saved by developing a virtual environment using MATLAB software tools. With a soft CMC laboratory, students can model many features of CMC, analyze and evaluate the performance in both link and system level. In this paper, we present a novel MATLAB based CMC laboratory course that comprises a total of ten experiments. The laboratory was offered in fall 2012 at American International University-Bangladesh (AIUB) for three sections each with a group of 40 students. The student’s comments on understanding, achieved knowledge, overall satisfaction, importance of the laboratory course, and any changes on the existing course content are documented and assessed.

Keywords: cellular mobile communication, laboratory, MATLAB, software tools.

I. INTRODUCTION

CMC is one of the rapid evolutionary technologies in the 21st century. User demands for rich multimedia services at high data rates are ever increasing. Telecommunication vendors and operators have been consistently putting significant efforts to fulfill the user needs. Standardization bodies such as third generation partnership project (3GPP), Institute of Electrical and Electronic Engineers (IEEE) have been working towards developing next generation mobile communication standards.

As mentioned in the table I of reference [1], a number of universities have already offered laboratory courses related to the mobile wireless communication with emphasizing on hardware, software, or a combination of hardware and software. Several publications, for example [1], addressed the importance of wireless communications laboratory to complement the course while few others addressed with specific areas such as cellular network planning [2], wireless networks and mobile systems [3], wireless and mobile embedded systems [4]. However, a CMC laboratory based on MATLAB tool is not obvious. In this paper, we present a novel MATLAB based CMC laboratory.

The paper is organized as follows. In section II all experiments of the laboratory are briefly described. The laboratory assessment is incorporated in section III. We finish the paper with a conclusion in section IV.

II. LABORATORY EXPERIMENTS

In this section, we describe all the experiments of CMC laboratory concisely in terms of major objectives and significance, necessary mathematical expressions and illustrations, and relevant performance analysis and evaluations. A detailed description of each experiment can be found emailing at either [email protected] or [email protected].

A. Experiment 1: Introduction to MATLAB.

Because the CMC laboratory is MATLAB simulator based, it is essential for the students to get familiarized with the use of this tool. In line with so, this experiment is designed to give students an overview on how to define and use operators, functions, variables, etc. in MATLAB. More specifically, we emphasize considerably on common mathematical functions, complex numbers, elementary matrices, vector and matrix calculations, numerical operations and transformations of matrices, operator precedence, general and logical functions, data manipulation commands, and graphics. However, because of MATLAB’s wide spread applications, we limit our focus on using those instructions that are more relevant to simulate the objectives of this laboratory experiments only.

B. Experiment 2: Propagation Models and Path Loss Estimation in Cellular Mobile Communication.

In this experiment, we primarily carry out the impact of carrier frequency (f) and distance (d) on path loss. In addition, a sensitivity analysis is carried out that provides critical parameters such as base station (BS) antenna height (hb), mobile station (MS) antenna height (hm) is incorporated for the system design and planning purpose.

We consider the very optimistic Free-space model, the very pessimistic ITU-R model, and the more realistic Hata model. All models are conceptually and analytically described, followed by respective simulation performance evaluation. We finish this experiment with a comparison of these path loss model simulation results as shown in fig.1.

Fig.1 Path loss versus distance estimation (f(MHz)=1800; hm (m)=3, hb (m)=50, d (km)=1 to 10).

C. Experiment 3: Estimation of Received Bit Energy for Data Rates in Wireless Communication.

In this experiment, students can understand how the received signal strength varies with the physical parameters of the environment. The received signal and noise strengths respectively are the function of energy per bit (Eb), and noise spectral density (N0). Hence, estimating the required received energy per bit over N0 () is inevitably a crucial need for designing reliable wireless communication systems. This estimation provides students with information regarding fundamental trade-offs between received power and channel bandwidth requirements, for example, for a given bandwidth (BW), the channel capacity depends directly on [5].

Denote data rate by R (bits/s), the bandwidth utilization of a radio channel link can be mathematically expressed as follows.

(1)

Hence, by plotting as a function of, students are able to define regions that are constrained either by received power or available channel bandwidth as given in [5].

D. Experiment 4: Multipath Fading in Cellular Mobile Communication.

In this experiment, students simulate the small-scale multipath fading effect on the transmitted signal. For non line-of-sight (NLOS) case, the received signal envelope follows Rayleigh distribution, and hence, the fading effect is simulated using Rayleigh fading function rayleighchan(). However, when at least one LOS component is present, the received signal envelope follows Rician distribution, and hence, the fading effect is simulated using Rician fading function ricianchan().

The fading effect is investigated under both frequency-flat and frequency-selective channel conditions in order to understand the effect of multipath propagation over the single path. The effect of change in Doppler spread, symbol duration, and the Rician K-factor on the channel response is also analyzed. Fig.2 shows an example of Rayleigh fading as well as Rician fading channel responses for frequency selective fading.

(a)

Fig.2 frequency selective (a) Rayleigh Fading channel response and (b) Rician Fading channel response.

E. Experiment 5: Power-Delay Profile and Doppler Spectrum for Channel Classification in Cellular Mobile Communication.

This experiment provides students with how to classify channels by evaluating power-delay profile and Doppler spectrum in cellular mobile communication. Power-delay profile and Doppler spectrum are major indicators for designing channel bandwidth (Bs) and transmitted symbol duration (Ts).

In multipath channels, the transmitted signal propagates over various paths, and each path is characterized with relative power and delay. The power-delay profile of the channel is an indication of the expected degree of dispersion of the transmitted signal that defines the maximum possible transmission bandwidth (Bc) by evaluating delay spread of the channel for proper reception.

Similarly, the spectral broadening or Doppler spread (fd) of the transmitted signal, caused by the degree of relative motion between BS and MS, defines the maximum possible transmitted symbol duration (Tc). Based on the relative magnitude of Bc over Bs and Tc over Ts, the channel can be classified as given in Table I.

Table I: Channel classification in mobile communication.

Channel classification

Parameter

Condition

Delay spread

Doppler spread

F. Experiment 6: Design of Cellular Mobile System.

This experiment gives the students an overview on cellular mobile system design. With changing system scenario, students are able to understand how the system design parameters and requirements change for optimal performance.

The design of cellular mobile system depends on several issues. Hence, we restrict the scope of system scenario considerations to carrier-to-interference level, cell structure, system and channel bandwidths, environmental profile, cell splitting and sectorization strategy, receiver filter characteristics, traffic distribution, trunked system nature, and coverage area.

Given such an explicit system scenario, students are able to estimate system design requirements and parameters such as frequency reuse factor, minimum co-channel cell reuse ratio, spectral efficiency, system capacity, minimum adjacent channel frequency separation, number of cells required for the coverage area, channel allocations with or without sectorization, new transmit power after cell splitting, traffic intensity, bit transfer capacity, and trunking efficiency [6].

Fig.3 shows an example scenario of the effect of sectorization on channel, a total of 125 channels, allocation for fixed channel assignment in the 7-cell cluster. Denote cells by A, B, C, D, E, F, and G, Fig.3 shows the channel allocation to each cell with no sectorization. However, when sectorization is considered, the channel allocation only for the sector 1 of each cell is shown. Similarly, the channel allocation to other sectors such as 2 and 3 for 3-sectored cell and sectors 2, 3, 4, 5, and 6 for 6-sectored cell can be estimated. Note that with sectorization, the capacity is improved by a factor of 3 and 6 respectively for 3-sectored cell and 6-sectored cell as compared with the capacity with no sectorization, however, results in an increase in call blocking rate.

Cell index

No sectorization

A

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120

B

2 9 16 23 30 37 44 51 58 65 72 79 86 93 100 107 114 121

C

3 10 17 24 31 38 45 52 59 66 73 80 87 94 101 108 115 122

D

4 11 18 25 32 39 46 53 60 67 74 81 88 95 102 109 116 123

E

5 12 19 26 33 40 47 54 61 68 75 82 89 96 103 110 117 124

F

6 13 20 27 34 41 48 55 62 69 76 83 90 97 104 111 118 125

G

7 14 21 28 35 42 49 56 63 70 77 84 91 98 105 112 119 0

Sector index

With sectorization

3-sectored cell

6-sectored cell

A1

1 22 43 64 85 106

1 43 85

B1

2 23 44 65 86 107

2 44 86

C1

3 24 45 66 87 108

3 45 87

D1

4 25 46 67 88 109

4 46 88

E1

5 26 47 68 89 110

5 47 89

F1

6 27 48 69 90 111

6 48 90

G1

7 28 49 70 91 112

7 49 91

Fig.3: Fixed channel assignment to cells in a 7-cell cluster with no sectorization, 3-sectored cell, and 6-sectored cell.

G. Experiment 7: Estimation of Bit Error Probability of Modulation Schemes.

In this experiment, we are primarily concerned with finding an appropriate modulation scheme at the transmitting side using bit error probability measurement. We consider modulation schemes such as M-PAM, M-PSK, M-QAM and M-point orthogonal signal sets. We then draw a conclusion, based upon the results obtained from these considered schemes, on selecting an appropiate modulation scheme that can be well suited in power-limited region and bandwidth-limited region.

The followings (Figs. 4 and 5) are example line graphs for bit error probability versus, and it can be found that irrespective of the degree of modulation, PAM is susceptible more to bit errors than PSK, QAM and orthogonal signal sets. In addition, increase in the degree of modulation results in more bit errors for PAM, PSK, and QAM.

Fig.4: Bit error probability for 4-PAM, QAM and 4-point orthogonal signal sets.

Fig.5: Bit error probability for 16-PAM, QAM and 16-point orthogonal signal sets.

However, the opposite is the case for orthogonal signal sets. Overall, PAM and QAM schemes are bandwidth-efficient, and orthogonal signal sets are power-efficient [7].

H. Experiment 8: Radio Resource Allocations and Scheduling in Cellular Mobile Communication.

Radio resource allocation and scheduling is one of the important areas in mobile communication that needs considerable attention to address several significant issues such as spectral efficiency, energy efficiency, and quality of service. Scheduler defines the specific usage of physical resources such as time, frequency, and power. In this experiment, students evaluate the performance of the generic schedulers such as Round Robin (RR), Max-SNR, and Proportional Fair (PF) in terms of average cell spectral efficiency and Jain’s fairness index in a macrocell of LTE-Advanced systems.

The system model considers a dense urban deployment. A single macrocell is considered. A number of macro users (per cell for a 3-sector site) are assumed within the macrocell coverage (Fig.6). Fig.6 shows the cell layout in the system architecture, where macro users are distributed randomly and uniformly throughout the macrocell.

The throughput in any arbitrary resource block (RB) can be expressed in terms of the mutual information (MI) by employing Shannon’s formula as given below [8] [9].

(2)

where is the SINR at any RB in a particular transmission time interval (TTI); is the implementation loss factor.

The average MI () over all RBs over all users in a component carrier (CC) is obtained, and then the effective

Fig.6: Cell layout in the system architecture [8].

SINR, per RB per TTI is estimated from the average MI by inverse mapping. The inverse mapping function can be mathematically expressed for as

(3)

Hence, the system level spectrum efficiency per RB per TTI can be expressed as

(4)

Jain’s fairness index is adopted for the evaluation of the user fairness performance to define the degree of fairness among users. Mathematically, the fairness index can be expressed as follows [10].

(5)

where K represents the total number of user and represents the total number of RB allocated to user k over the simulation runtime.

Simulation parameters and assumptions are adopted mainly from [11], which are based on the system architecture explained. The spectral efficiency performance and the fairness performance of these schedulers are shown in Fig.7. From Fig.7, it can be found that RR scheduler provides the best fairness but the least spectral efficiency performance. On the contrary, Max-SNR scheduler provides the best spectral efficiency but the worst fairness performances. However, the PF scheduler provides a trade-off between fairness and spectral efficiency performances by taking the user previous resource allocation history into account.

Fig.7: Spectral efficiency and fairness performances of generic resource schedulers.

I. Experiment 9: Design of Inland Digital Microwave Link.

In this experiment, students study the basic concepts of high frequency wave propagation, optical and radio horizon, ducting phenomenon, Fresnel zones, earth bulge, and factor k: a ratio of effective earth radius to the true earth radius and its effect on the link range. Fig.8 shows an example point-to-point microwave backhaul between base station transceivers (BTSs) A and B with gain and loss in different parts.

Fig.8: A typical single hop microwave communications link.

Given that transmit power is 0 dBW, total cable loss is 4 dB, carrier frequency is 6 GHz, intermediate frequency bandwidth of the receiver is 10 MHz, receiver is at room temperature, and 90% radio link reliability, students can estimate the effective isotropic radiated power (EIRP), free-space path loss of the hop, power flux density at the receiver front end, receiver noise threshold, antenna aperture diameter, and the received power. Fig.9 is a simulated result for a single hop microwave link. All gains and losses are shown along with the signal propagation from transmitter to receiver.

Fig.9: Typical link budget estimation of a LOS microwave link.

J. Experiment 10: Design of Satellite Link.

Satellite backhaul in mobile systems is a solution where other alternative backhaul technologies are not feasible such as islands, remote locations [12]. Typically, a long-distance network through satellite link is designed and established through geostationary earth orbit (GEO) satellites at frequencies in C band (6-4 GHz) and in Ku band (14-12 GHz). In this experiment, students study the fundamental issues and constraints in designing earth-satellite-earth communication link as given in. Fig.10.

Fig.10: Basic earth-satellite-earth communication link [13].

Refer to Fig.10, given the parameters such as transmit power, satellite antenna gain, uplink and downlink slant range, and satellite antenna gain-to-noise temperature ratio, we can estimate the earth station-satellite-earth station link budget. In addition, uplink carrier-to-noise ratio, (C/N)u, downlink carrier-to-noise ratio, (C/N)d, and the total link carrier-to-noise ratio, (C/N)total can also be estimated. If (C/N)u >>(C/N)d, then C/N ≈ (C/N)d. In this case the satellite link is said to be downlink-limited. When (C/N)u <<(C/N)d, then C/N ≈ (C/N)u, and the satellite link is said to be uplink-limited.

We consider both single carrier and multi-carrier per transponder for the link budget estimation and carryout an analysis of how multi-carrier scenario affects the link performance. Followings as given in Table II are parameters that are considered as an example scenario to estimate the satellite link performance for the operation of single carrier per transponder.

Table II: Default simulation parameters and scenarios for satellite link [13].

Parameter

Value

Transponder operation band

14/12-GHz

Noise bandwidth

46 MHz

Satellite antenna gain-to-noise ratio

-1.6dB/K

Satellite saturation EIRPs

44 dBW

Satellite TWTA BOi

11 dB

Satellite TWTA BOo

6 dB

Earth station transmit antenna gain

57.6 dB

Earth station receive antenna gain

56.3 dB

Earth station carrier power fed to antenna

174 W

Maximum uplink and downlink slant range

37,506 km

System noise temperature Ts

160 K

Uplink tracking loss

1.2 dB

Downlink tracking loss

0.9 dB

Interference into or from adjacent satellite, terrestrial interference, etc. and any influence by the atmosphere

Negligible

The simulation output gives the desired link budget along with other measures as given in Table III for single carrier per transponder operation.

Table III: Output simulation parameters and values for satellite link.

Parameter

Value

Carrier EIRP

80.0055 dBW

Uplink free space loss

206.8463 dB

Uplink carrier-to-noise ratio

23.3591 dB

Satellite EIRP

44 dBW

Downlink free space loss

205.5074 dB

Downlink carrier-to-noise ratio

24.8926 dB

Total satellite link (earth-satellite-earth) carrier-to-noise ratio

127.2986 dB

Satellite characteristics

uplink limited

III. LABORATORY ASSESSMENT

In order to assess the significance of the CMC laboratory, we carried out a student survey within the registered students of the CMC laboratory of fall 2012 semester at AIUB. The total number of students participated in the survey was ninety one. The survey questions were categorized into two parts: part I incorporates general questions and part II incorporates query based questions, with five questions in each part (Table IV). Students were asked to put a tick on either Yes or No, with an option for making comments, explaining the reasons for their opinions.

Fig.11 shows the response of the students for each question of the survey (Table IV). From the Figs.11 (a) and (b), it can be found that overall most of the students

Table IV: Student survey questions of CMC laboratory assessment.

No

Question

Part I: General category

Q01

The laboratory experiments are relevant and helpful for understanding the contents of the course cellular mobile communication.

Q02

The experiments of the lab are easy to understand and have been written properly with sufficient details.

Q03

The lab provides sufficient realization of the practical aspects of cellular mobile communication using MATLAB software tool.

Q04

The lab is helpful for further research and professional career development in mobile communication.

Q05

The lab is equipped with sufficient resources for simulation.

Query based

Q06

Is the laboratory time of three hours duration sufficient for the experiments?

Q07

Is there any prerequisite the students should complete before they enroll for the lab?

Q08

Should there be any changes in the content, strategy, methodology, etc. of the lab?

Q09

Is there any deficiency in the lab experiments that should be addressed?

Q10

Is there any comment on improving the lab?

responded to Yes. Particularly, majority have agreed upon questions 01 through 07, i.e. the laboratory is relevant and helpful for understanding the CMC course, provides sufficient realization of the practical aspects of CMC, and is helpful for further research and professional career development in mobile communication. In addition, according to the majority of the students, the experiments have been written properly with easy to understand features, and the lab is equipped with sufficient resources for simulation.

From the Fig.11 (b), for query based questions, 08 to 10, majority of the students responded almost equally to Yes or No. More specifically, the laboratory time is sufficient, and the students should complete prerequisites such as digital signal processing, telecommunications engineering, and how to work with MATLAB. Moreover, in the final question, students were asked to make comments on improving the

(a)

(b)

Fig.11: Response of the students to the survey questions (a) general category (b) query based.

laboratory. Selected few comments made by a considerable number of students are as follows: "arrangement of few field visits to telecommunication industry may facilitate students gaining some real life experiences", "few practical types of equipment can be introduced", and "understanding of MATLAB coding should be a prerequisite".

IV. CONCLUSION

In this paper, we described a novel MATLAB based cellular mobile communication (CMC) laboratory course which had been offered in fall 2012 at AIUB, Bangladesh. The laboratory course comprises of ten experiments, covering the fundamental design parameters, considerations, and estimations of CMC both in the radio interface and core network levels. In the radio interface side, estimation of path loss, fading, power delay profile, and received bit error probability, while in the core network side, estimation of link budget of earth-satellite-earth communication, inland microwave communication, and radio resource allocation and scheduling have been included. In addition, a fundamental to the MATLAB was also introduced at the very beginning for allowing students to understand of how to code in MATLAB.

A survey over ninety one students was carried out to assess the importance and impact of the laboratory. Almost all students found the laboratory experiments relevant and helpful for understanding the CMC theory course and for further research and professional career development. Since the laboratory is just MATLAB software based, the laboratory can be offered at affordable cost to provide students to gain practical realization of the CMC theory course at the university. The authors would be pleased to share the laboratory course materials with any individual or institution interested it and can be reached at either [email protected] or [email protected].

ACKNOWLEDGEMENT

The authors are thankful to Abdur Rahman and Farhadur Arifin, AIUB, Bangladesh for their generous support to develop the laboratory.