Low Autocorrelation Binary Sequences: Best-Known Peak Sidelobe Level Values

Brest, Janez; Bošković, Borko

doi:10.1109/access.2021.3077541

Cited by 7 publications

(8 citation statements)

References 23 publications

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“…In contrast to exhaustive search, stochastic neighborhood search is also useful for many problems [6,17,18], including locating sequences with low PSL value. Recent approaches [8], [9], and [4] were able to reach sequences that have new best-known PSL values, for 105 < L ≤ 10 6 . The fitness function (see Eq.…”

Section: Related Workmentioning

confidence: 99%

“…Here we can again observe that, as the instance size increased, the difference between the old and new best-known PSL values (∆PSL) increased, and the best improvement by 135 was achieved for the largest instance size. The old best-known results were obtained in [4] and [7]. The first paper is our previous work, which was also based on a stochastic algorithm, while the exhaustive search with restriction of m-sequences was used in the second paper.…”

Section: It Can Be Observed That New Best-known Psl Valuesmentioning

confidence: 99%

“…The PSL values achieved by q q q q q q q q q q q q q q q q q q q 0.4 q q q q q q q q q q q q q q q q q q q q q q q M(Y) ( Fig. 4: Comparison of the PSL value trends according to [7], [4], [11], and the best-known values. [7] 889 135 the proposed algorithm were significantly less than √ L (value 1.0 in the graph), significantly less in comparison with other works, the trend of these values is now slightly flatter, and it is not possible to predict the growth rate for longer sequences.…”

Section: It Can Be Observed That New Best-known Psl Valuesmentioning

confidence: 99%

“…The exhaustive search was also applied under the restriction of m-sequence [7]. However, stochastic algorithms are useful for longer sequences [4,8]. To obtain good results, stochastic algorithms need a fitness function that guides the search process throughout the search space.…”

Section: Introductionmentioning

confidence: 99%

“…To obtain good results, stochastic algorithms need a fitness function that guides the search process throughout the search space. Different fitness functions have already been used for different values of L in recent works [4,7]. According to this observation, we propose an algorithm that uses two fitness functions within the optimization process.…”

Section: Introductionmentioning

confidence: 99%

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Two-phase Optimization of Binary Sequences with Low Peak Sidelobe Level Value

Bošković¹,

Brest²

2021

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The search for binary sequences with low peak sidelobe level value represents a formidable computational problem. To locate better sequences for this problem, we designed a stochastic algorithm that uses two fitness functions. In these fitness functions, the value of the autocorrelation function has a different impact on the final fitness value. It is defined with the value of the exponent over the autocorrelation function values. Each function is used in the corresponding optimization phase, and the optimization process switches between these two phases until the stopping condition is satisfied. The proposed algorithm was implemented using the compute unified device architecture and therefore allowed us to exploit the computational power of graphics processing units. This algorithm was tested on sequences with lengths L = 2 m − 1, for 14 ≤ m ≤ 20. From the obtained results it is evident that the usage of two fitness functions improved the efficiency of the algorithm significantly, new-best known solutions were achieved, and the achieved PSL values were significantly less than √ L.

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