Enhanced Semi-Blind Uplink Interference Suppression on Multicell Massive MIMO Systems for Multi Modulus Signals

Maruta, Kazuki; Ahn, Chang-Jun

doi:10.1109/vtcfall.2019.8891412

Cited by 4 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [ 8 ] examines the model of an array antenna’s reception signal, utilizing the correlation of the steering vector to identify and mitigate various forms of spoofing jamming. Most existing jamming diagnosis methods rely on manually selected feature parameters based on the distribution law of specific type jamming signals.…”

Section: Introductionmentioning

confidence: 99%

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer

Zhang,

Deng,

Liu

et al. 2024

Sensors

View full text Add to dashboard Cite

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.

show abstract

Section: Introductionmentioning

confidence: 99%

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer

Zhang,

Deng,

Liu

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Semi-blind channel estimation based on modified CMA and unitary scrambling for massive MIMO systems

Sellami

Siala

2021

Telecommun Syst

View full text Add to dashboard Cite

Pilot contamination is one of the main impairments in multi-cell massive Multiple-Input Multiple-Output (MIMO) systems. In order to improve the channel estimation in this context, we propose to use a semiblind channel estimator based on the constant modulus algorithm (CMA). We consider an enhanced version of the CMA namely the Modi ed CMA (MCMA) which modi es the cost function of the CMA algorithm to the sum of cost functions for real and imaginary parts. Due to pilot contamination, the channel estimator may estimate the channel of a contaminating user instead of that of the user of interest (the user for which the Base Station wants to estimate the channel and then the data). To avoid this, we propose to scramble the users sequences before transmission. We consider different methods to perform unitary scrambling based on rotating the transmitted symbols (one Dimensional (1-D) scrambling) and using unitary matrices (two-Dimensional (2-D) scrambling). At the base station, the received sequence of the user of interest is descrambled leading to a better convergence of the channel estimator. We also consider the case where the Automatic Repeat reQuest (ARQ) protocol is used. In this case, using scrambling leads to a signi cant gain in terms of BLock Error Rate (BLER) due to the change of the contaminating users data from one transmission to another induced by scrambling.

show abstract

Improving Semi-Blind Interference Suppression on Multi-Cell Massive MIMO Systems by Multi-Antenna Users

Maruta

2023

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

View full text Add to dashboard Cite

Enhanced Semi-Blind Uplink Interference Suppression on Multicell Massive MIMO Systems for Multi Modulus Signals

Cited by 4 publications

References 23 publications

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer

Semi-blind channel estimation based on modified CMA and unitary scrambling for massive MIMO systems

Improving Semi-Blind Interference Suppression on Multi-Cell Massive MIMO Systems by Multi-Antenna Users

Contact Info

Product

Resources

About