(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 14, No. 1, 2023 161 | Page www.ijacsa.thesai.org An Optimized Method for Polar Code Construction Issame El Kaime 1 , Reda Benkhouya 2 , Abdessalam Ait Madi 3 , Hassane Erguig 4 Advanced Systems Engineering Laboratory-National School of Applied Sciences, Ibn Tofail University Kenitra, Morocco 1 MISC Laboratory-Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco 2 Advanced Systems Engineering Laboratory-National School of Applied Sciences, Ibn Tofail University Kenitra, Morocco 3 Materials Physics and Subatomics Laboratory-Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco 4 Abstract—Polar codes are traditionally constructed by calculating the reliability of channels, then sorting them by intensive calculations to select the most reliable channels. However, these operations can be complicated especially when, the polar code length, N becomes great. This paper proposes a new low-complexity procedure for polar codes construction over binary erasure and additive white Gaussian noise (AWGN) channels. Using the proposed algorithm, the code construction complexity is reduced from O(Nlog N) to O(N), where N=2 n (n≥1). The proposed approach involves storing the classification of channels by reliabilities in a vector of length L, and then deriving the classification of M channels for every M where M<=L. The proposed method is consistent with Bhattacharya parameter based Construction and Density Evolution with Gaussian Approximation (DEGA) based construction. In this paper, the Successive Cancellation Decoding algorithm (SCDA) is used. Thanks to its low complexity and its high error-correction capability. Keywords—Polar codes; SNR; successive cancellation decoding; error correction; low-complexity; code construction; additive white Gaussian noise; Bhattacharya parameter; density evolution with Gaussian approximation I. INTRODUCTION It is usual for communication links to suffer from errors due to random noise, interference and malfunctioning devices, etc. To correct errors in channel coded data streams, a set of algorithmic operations is applied to the original data stream at the transmitter. A second set of algorithmic operations is applied to the received data stream at the receiver. Encoding and decoding operations at the transmitter and receiver are collectively called channel coding operations in channel coding terminology. Research in channel coding is focused on developing high performance channel codes that mitigate the effects of errors in communication links. A real challenge here is to accomplish this with sufficient simplicity to allow practical implementation in silicon technology. Everything depends on the complexity of a code, including its power consumption, memory requirements, computation power requirements, and latency, which determine whether or not a code is appropriate for any given scenario. Channel coding is somewhat revolutionized by polar codes. The polar codes proposed by Arikan in 2008 can achieve that capacity of any binary discrete memoryless channel [1], researchers from all over the world have been interested in polar codes ever since their introduction in [1], polar codes can be used in lot of applications like cryptgraphy [2], speech communication [3], data storage [4]. Polar codes are also used to develop new block [5]. Polar codes are composed of three main stages; namely the construction, the coding and finally the decoding. The construction of polar codes is a crucial step as they affect the performance of polar codes. In polar codes construction, synthetic channels are evaluated by reliability. Good channels are selected for information transmission and the bad channels are frozen. Where polar codes are capable to achieving channel capacity for any binary-input discret memoryless channel [1]. Polar codes construction step encounters the following problem: given N code lengths and K information bit length,what is the best way to select K channels out of all the available ones, knowing that the remaining N-K bit channels are frozen and provided to the transmitter and receiver. An indication of the quality of a virtual bit channel     can be determined by using a variety of metrics. Polar codes take effect when the length of the code is very large, which implies a large number of computes to know the capacity of each channel, As a result, building polar codes is extremely difficult and requires a lot of resources. This paper considers the construction of polar codes over symmetric binary discrete memory-less channels by using a new method to reduce the complexity of the construction. Many methods have been developed previously in literature to build polar codes, Monte- Carlo simulations are proposed in [1] with a high complexity of O(TNlogN) where T indicates the number of iterations of Monte-Carlo simulations. In [6] and [7], polar codes construction is based on density evolution, where convolutions of functions are performed and numerical calculation precision is limited by the complexity of the process. In [8] bit-channel approximations are proposed with a Complexity under controlled conditions of O(N.μ 2 log μ) (μ a user-defined parameter that limits the number of output alphabets at each step of the approximation process). Another type of algorithm can construct polar codes using Gaussian approximation (GA) of additive white Gaussian noise (AWGN) channels [9]–[11], this approximation function [11] inherently limits the GA method, with some restrictions on the length of blocks [11]. Bhattacharya paremeters are used to construct polar codes in[1], other constructions methods with variable performance and complexity are located in [12]–[14]. This paper describes an efficient method of constructing polar codes to reduce their computational complexity, if the method suggested in this paper is compared to other ones in the literature, the method presented here is characterised by a reduced complexity.