Atomic Network-Based DOA Estimation Using Low-Bit ADC

Sheng, Shuran; Chen, Peng; Yao, Yuxuan; Wu, Lenan; Chen, Zhimin

doi:10.3390/electronics10060738

Cited by 3 publications

(3 citation statements)

References 51 publications

(62 reference statements)

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“…Figure 8. shows the structure of TabNet model after enhancement, while the graph of ELU activation function shown in figure 9 [28].…”

Section: Split Block the Split Block Divides The Input Into Two Branc...mentioning

confidence: 99%

A Developed Multi-Level Deep Learning Model for Network Slicing Classification in 5G Network

Hamza,

Ebady Manaa

2024

J. Phys.: Conf. Ser.

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5G is considered as a key contributor and infrastructure supplier in the communication technology industry, capable of supporting a wide range of services such as virtual reality, driverless automobiles, e-health, and a variety of intelligent applications. Network slicing is designed to support the diversity of services applications with increased performance and flexibility needs by dividing the physical network into many logical networks. Service classification allows 5G service providers to accurately select the network slices for each service. We propose a Network Slicing classifier that uses a Multi-level Deep learning Model. First, we created a dataset of 5G network slicing that contain attributes connected with various network services. Next, we performed a multi-level model that consist of a set of Machine learning and deep learning model (such as deep Neural Network, Random Forest and Decision Tree) as a first level followed by next level that which is represent Attentive Interpretable Tabular Learning model. The outcomes of the experiment showed that the proposed model was able to exceed the normal models with high performance results.

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“…Figure 8. shows the structure of TabNet model after enhancement, while the graph of ELU activation function shown in figure 9 [28].…”

Section: Split Block the Split Block Divides The Input Into Two Branc...mentioning

confidence: 99%

A Developed Multi-Level Deep Learning Model for Network Slicing Classification in 5G Network

Hamza,

Ebady Manaa

2024

J. Phys.: Conf. Ser.

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“…A CS-based framework can reduce the complexity of the hardware required for DOA estimation, and it is one of the most attractive research areas in B5G/6G wireless communications. Since DOA estimation mainly relies on spatial-temporal measurements, CS-based DOA estimation frameworks can be divided into three categories, namely spatial CS frameworks [ 48 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ], temporal CS frameworks [ 60 , 61 , 62 , 63 ], and spatial-temporal CS frameworks [ 64 ].…”

Section: Spatial/temporal Compressive Samplingmentioning

confidence: 99%

“…With the exception of one-bit quantification, various quantification networks have been discussed. In [ 61 ], the DOA estimation issue is considered under the conditions of a low-bit ADC, and a deep-learning network is implemented to eliminate quantization noise. In [ 62 ], a linear additive quantization noise model is considered for low-bit sampling DOA estimation issues, and an improved MUSIC method is introduced.…”

Section: Spatial/temporal Compressive Samplingmentioning

confidence: 99%

DOA Estimation in B5G/6G: Trends and Challenges

Ruan

Wang

Wen

et al. 2022

Sensors

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Direction-of-arrival (DOA) estimation is the preliminary stage of communication, localization, and sensing. Hence, it is a canonical task for next-generation wireless communications, namely beyond 5G (B5G) or 6G communication networks. Both massive multiple-input multiple-output (MIMO) and millimeter wave (mmW) bands are emerging technologies that can be implemented to increase the spectral efficiency of an area, and a number of expectations have been placed on them for future-generation wireless communications. Meanwhile, they also create new challenges for DOA estimation, for instance, through extremely large-scale array data, the coexistence of far-field and near-field sources, mutual coupling effects, and complicated spatial-temporal signal sampling. This article discusses various open issues related to DOA estimation for B5G/6G communication networks. Moreover, some insights on current advances, including arrays, models, sampling, and algorithms, are provided. Finally, directions for future work on the development of DOA estimation are addressed.

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High-Resolution and Robust One-Bit Direct-of-Arrival Estimation via Reweighted Atomic Norm Estimation

Li,

Yang,

Dai

et al. 2024

Sensors

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In recent years, one-bit quantization has attracted widespread attention in the field of direction-of-arrival (DOA) estimation as a low-cost and low-power solution. Many researchers have proposed various estimation algorithms for one-bit DOA estimation, among which atomic norm minimization algorithms exhibit particularly attractive performance as gridless estimation algorithms. However, current one-bit DOA algorithms with atomic norm minimization typically rely on approximating the trace function, which is not the optimal approximation and introduces errors, along with resolution limitations. To date, there have been few studies on how to enhance resolution under the framework of one-bit DOA estimation. This paper aims to improve the resolution constraints of one-bit DOA estimation. The log-det heuristic is applied to approximate and solve the atomic norm minimization problem. In particular, a reweighted binary atomic norm minimization with noise assumption constraints is proposed to achieve high-resolution and robust one-bit DOA estimation. Finally, the alternating direction method of multipliers algorithm is employed to solve the established optimization problem. Simulations are conducted to demonstrate that the proposed algorithm can effectively enhance the resolution.

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Atomic Network-Based DOA Estimation Using Low-Bit ADC

Cited by 3 publications

References 51 publications

A Developed Multi-Level Deep Learning Model for Network Slicing Classification in 5G Network

A Developed Multi-Level Deep Learning Model for Network Slicing Classification in 5G Network

DOA Estimation in B5G/6G: Trends and Challenges

High-Resolution and Robust One-Bit Direct-of-Arrival Estimation via Reweighted Atomic Norm Estimation

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