Blind spreading sequence estimation based on hill-climbing algorithm

Sha, Zhi-Chao; Huang, Zhitao; Zhou, Yiyu; Wang, Fenghua

doi:10.1109/icosp.2012.6491814

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Local ekstremum is searched in the neighborhood of the current state, where the first accepted value of the current state is the initial state value. Solution c is called local optimization, where the N(c) neighborhood does not have a better solution, and it is not the best solution in the whole set of solutions [50]. In optimizing with a hill-climbing algorithm it is not possible to determine whether the local extreme found is a global one.…”

Section: Hill-climbingmentioning

confidence: 99%

A New Approach to Identifying a Multi-Criteria Decision Model Based on Stochastic Optimization Techniques

Kizielewicz

Sałabun

2020

Symmetry

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Many scientific papers are devoted to solving multi-criteria problems. Researchers solve these problems, usually using methods that find discrete solutions and with the collaboration of domain experts. In both symmetrical and asymmetrical problems, the challenge is when new decision-making variants emerge. Unfortunately, discreet identification of preferences makes it impossible to determine the preferences for new alternatives. In this work, we propose a new approach to identifying a multi-criteria decision model to address this challenge. Our proposal is based on stochastic optimization techniques and the characteristic objects method (COMET). An extensive work comparing the use of hill-climbing, simulated annealing, and particle swarm optimization algorithms are presented in this paper. The paper also contains preliminary studies on initial conditions. Finally, our approach has been demonstrated using a simple numerical example.

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Section: Hill-climbingmentioning

confidence: 99%

A New Approach to Identifying a Multi-Criteria Decision Model Based on Stochastic Optimization Techniques

Kizielewicz

Sałabun

2020

Symmetry

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“…Neighborhood search have been applied triumphantly in many combinatorial optimization problems, such as binpacking (Ceschia et al 2013) and scheduling (Ceschia and Schaerf 2011). Several popular NS methods appear, such as hill climbing (HC) (Sha et al 2012), steepest descent (SD) (Meza 2010), simulating annealing (SA) (Kirkpatrick et al 1983), and tabu search (TS) (Glover 1998;Glover and Marti 2006).…”

Section: Introductionmentioning

confidence: 99%

Improving monarch butterfly optimization through simulated annealing strategy

Yang

Wang

Tian

et al. 2020

J Ambient Intell Human Comput

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Currently, a novel of meta-heuristic algorithm called monarch butterfly optimization (MBO) is presented for solving machine learning and continuous optimization problems. It has been proved experimentally that MBO is superior to artificial bee colony algorithm (ABC), ant colony optimization algorithm (ACO), Biogeography-based optimization (BBO), differential evolution algorithm (DE) and simple genetic algorithm (SGA) algorithms on most test functions. This paper presents a new version of MBO with simulated annealing (SA) strategy called SAMBO. The SA strategy is put in the migration operator and butterfly adjusting operator. So the newly proposed algorithm has two features: One is that the algorithm accepts all the butterfly individuals whose fitness are better than their parents. The other is that the algorithm randomly selects some individuals which are worse than their parents to disturbance the convergence of algorithm. In this way, the SAMBO algorithm can escape from local optima. Finally, the experiments are carried on 14 continuous nonlinear functions, and results show that SAMBO method exceeds the MBO algorithm on most test functions.

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“…For SC direct‐sequence spread spectrum (SC‐DSSS) signals, a subspace‐based method was proposed to recover the convolution of the spreading code and channel impulse response in multipath environment [6]. In [7], based on hill‐climbing (HC) algorithm, blind spreading sequence recovery was carried out. This technique provides faster convergence speed comparing with the genetic‐based algorithm proposed in [8].…”

Section: Introductionmentioning

confidence: 99%

Blind spreading sequence estimation algorithm for long‐code DS‐CDMA signals in asynchronous multi‐user systems

Liang

Wang

et al. 2017

IET signal process.

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Despreading signal at receiver side requires prior knowledge of the spreading sequences in direct-sequence code division multiple access (DS-CDMA) system. However, the knowledge of spreading sequences is always unknown to the receiver in non-cooperative communication. Therefore the receiver has to estimate the spreading sequences in a blind manner. This study presents a novel algorithm to estimate the spreading sequences for asynchronous long-code (LC) DS-CDMA signals. By investigating the projection characteristics of the signal subspace, the authors treat the spreading sequence estimation problem as a discrete optimisation search problem that can be solved efficiently by modern optimisation search methods. Moreover, they design a successive subspace deflation scheme to alleviate interference from other users. Numerical experiments demonstrate that the proposed algorithm provides good estimation of the spreading sequences for LC-DS-CDMA signals. Compared with the existing algorithms, the proposed algorithm exhibits much better performance in large system load scenario with low signal-to-noise ratio.

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Blind spreading sequence estimation based on hill-climbing algorithm

Cited by 6 publications

References 10 publications

A New Approach to Identifying a Multi-Criteria Decision Model Based on Stochastic Optimization Techniques

A New Approach to Identifying a Multi-Criteria Decision Model Based on Stochastic Optimization Techniques

Improving monarch butterfly optimization through simulated annealing strategy

Blind spreading sequence estimation algorithm for long‐code DS‐CDMA signals in asynchronous multi‐user systems

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