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Multi-objective Optimization with FEC Polar Code for Bandwidth Efficient Mobile Network Using Response Surface Methodology

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Abstract

Scheduling and resource allocation are vital elements of wireless systems. This paper presents a bandwidth-efficient polar code-based Orthogonal Frequency Division Multiplexing system to allocate optimized bandwidth, and power resources amongst the users. However, the available network bandwidth is limited to facilitate and deliver an array of services to the legions. Therefore, there is a necessity to increase the network capacity within the same accessible bandwidth to meet such demanding service requirements. The entire experimentation is carried on 48,000-bit data and adopts M-ary Phase Shift Keying modulation techniques. Here, a fixed Peak to Average Power Ratio of 14 dB and zero carrier frequency offset is considered. The intrinsic second-order mathematical regression model is developed for the entire system. The number of users is doubled keeping available bandwidth at a Bit Error Rate of 10–4. Latency of less than 3 ms is observed. The best possible result has shown Effective Isotropic Radiated Power of 26.99 dB with 5.78 dB Signal to Noise Ratio which is well acceptable for human body radiation hazards. The reduction of 33% in the cell area is the only acceptable penalty paid. This paper uses a Response Surface Methodology optimization method for the first time in mobile communication network design. The final model was found to be 98.30% reasonably accurate.

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Data Availability

Data/Code sharing not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

ANOVA:

Analysis of variance

BER:

Bit error rate

BIPCM:

Bit-interleaved polar coded modulation

BLER:

Block error rate

BP:

Belief propagation

CRC:

Cyclic redundancy check

D2D:

Device to device

DOE:

Design of experiments

EIRP:

Effective isotropic radiated power

FEC:

Forward error correction

FER:

Frame error rate

FFT:

Fast Fourier Transform

LCLSC:

Low complexity LSC

LDPC:

Low density parity check code

LLR:

Log likelihood ratio

LSC:

List successive cancellation

LTE:

Long term evolution

MPP:

Maximum partial polarization

MPSK:

M-ary phase shift keying

OFDM:

Orthogonal frequency division multiplexing

PAPR :

Peak to average power ratio

PSN:

Partial sum network

QAM:

Quadrature amplitude modulation

QoS:

Quality of service

RSM:

Response surface methodology

SCL:

Successive cancellation list

SMPE:

Simplified merged processing element

SNR:

Signal to noise ratio

SSC:

Simplified successive cancelation

PM:

Probability of symbol error rate

Pb:

BER

M:

No. of phases for PSK

K:

No. of bits per Hz

R:

Bit energy to noise power ratio

Q:

Probability of standard deviation

erfc:

Error function

Fc:

Carrier frequency

ht, hr:

Height of transmitting and receiving antenna

Ár:

Reflection coefficients

R:

Radius of cell

B:

Input noise bandwidth

B:

Coded bits per subcarrier

NS :

Subcarriers

k1:

Code rate

||xi-yi||:

Euclidian distance between two code-words

Z:

Bhattacharya parameter

(k, N) :

Block length of code words

L:

Length of PCM bits

ß:

Tuning parameters

TS :

Symbol time

Tb:

Bit time

Lp:

Path loss

NF:

Noise figure

GR :

Gain of receiving antenna

DF:

Degrees of freedom

SS :

Sum of squares

MS:

Mean squares

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Funding

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Authors and Affiliations

  1. Department of Electronics and Telecommunication, NBN Sinhgad School of Engineering, Pune, India

    Makarand Jadhav

  2. Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune, India

    Ganesh Dongre

  3. Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Information Technology, Pune, India

    Parikshit Mahalle

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  1. Makarand Jadhav
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  2. Ganesh Dongre
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  3. Parikshit Mahalle
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors.

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Correspondence to Makarand Jadhav.

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Jadhav, M., Dongre, G. & Mahalle, P. Multi-objective Optimization with FEC Polar Code for Bandwidth Efficient Mobile Network Using Response Surface Methodology. Wireless Pers Commun 125, 2833–2863 (2022). https://doi.org/10.1007/s11277-022-09688-w

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