Particle swarm optimization algorithm based decision feedback equalizer for underwater acoustic communication

Mahmutoğlu, Yiğit; Türk, Kadir; Tugcu, Emin

doi:10.1109/tsp.2016.7760848

Cited by 11 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of artificial intelligence technology in underwater acoustic communication mainly focuses on the dynamic changes in the marine environment and the physical characteristics of underwater acoustic channels [8,9]. Mahmutoglu et al [10] proposed the particle swarm optimization (PSO) algorithmbased adaptive decision feedback equalizer (DFE) for UWAC, in which PSO is independent from channel characteristics and has faster convergence. Although PSO has the highest computational complexity, our simulation results show that the PSO-DFE outperforms other algorithms.…”

Section: Related Workmentioning

confidence: 99%

Deep reinforcement learning-based adaptive modulation for OFDM underwater acoustic communication system

Cui

Yan

et al. 2023

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Due to the time-varying and space-varying characteristics of the underwater acoustic channel, the communication process may be seriously disturbed. Thus, the underwater acoustic communication system is facing the challenges of alleviating interference and improving communication quality and communication efficiency through adaptive modulation. In order to select the optimal modulation mode adaptively and maximize the system throughput ensuring that the bit error rate (BER) meets the transmission requirements, this paper introduces deep reinforcement learning (DRL) into orthogonal frequency division multiplexing acoustic communication system. The adaptive modulation is mapped into a Markov decision process with unknown state transition probability. Thereby, the underwater communication channel environment is regarded as the state of DRL, and the modulation mode is regarded as action. The system returns channel state information (CSI) and signal–noise ratio in every time slot through the feedback link. Because the Deep Q-Network optimizes in the changing state space of each time slot, it is suitable for a variety of different CSI. Finally, simulations in different underwater environments (SWellEx-96) show that the proposed adaptive modulation scheme can obtain lower BER and improve the system throughput effectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Deep reinforcement learning-based adaptive modulation for OFDM underwater acoustic communication system

Cui

Yan

et al. 2023

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…One resemblance between PSO, LMS, and RLS is their learning mechanism. In PSO, particles adjust their positions and velocities based on their individual experiences and the collective knowledge of the swarm [ 69 ]. This learning process allows particles to explore the search space and exploit promising regions.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Khan,

Shafi,

Khawaja

et al. 2023

Sensors

View full text Add to dashboard Cite

Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO.

show abstract

“…To improve the reliability and accuracy of the positioning, early positioning work mainly focused on the clock asynchronization problem for accurate underwater positioning [6][7][8][9][10]. With the development of urban positioning and indoor wireless positioning technology, many advanced signal processing technologies have emerged to weaken multipath signals or enhance the main path signals, such as sparse channel estimation [11,12], turbo equalization [13,14], decision feedback equalization [15], time reversal mirror in [16], etc. As the scale of underwater networks expands, large-scale positioning has received widespread attention and various methods have been proposed to tackle this problem [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Fast Ray-Tracing-Based Precise Localization for Internet of Underwater Things without Prior Acknowledgment of Target Depth

Huang,

Meng,

Sun

et al. 2024

JMSE

View full text Add to dashboard Cite

Underwater localization is one of the key techniques for positioning, navigation, timing (PNT) services that could be widely applied in disaster warning, underwater rescues and resource exploration. One of the reasons why it is difficult to achieve accurate positioning for underwater targets is due to the influence of uneven distribution of underwater sound velocity. The current sound-line correction positioning method mainly aims at scenarios with known target depth. However, for nodes that are non-cooperative nodes or lack depth information, sound-line tracking strategies cannot work well due to non-unique positional solutions. To solve this problem, we propose an iterative ray tracing 3D underwater localization (IRTUL) method for stratification compensation. To demonstrate the feasibility of fast stratification compensation, we first derive the signal path as a function of initial |grazing angle, and then prove that the signal propagation time and horizontal propagation distance are monotonic functions of the initial grazing angle, which guarantees the fast achievement of ray tracing. Simulation results indicate that IRTUL has the most significant correction effect in the depth direction, and the average accuracy has been improved by about 3 m compared to a localization model with constant sound velocity.

show abstract

Particle swarm optimization algorithm based decision feedback equalizer for underwater acoustic communication

Cited by 11 publications

References 12 publications

Deep reinforcement learning-based adaptive modulation for OFDM underwater acoustic communication system

Deep reinforcement learning-based adaptive modulation for OFDM underwater acoustic communication system

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Fast Ray-Tracing-Based Precise Localization for Internet of Underwater Things without Prior Acknowledgment of Target Depth

Contact Info

Product

Resources

About