Yongzhao Li’s research while affiliated with Xidian University and other places

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Publications (116)


A Transformer based Self-supervised Learning Framework for Robust Time-frequency Localization in Concurrent Cognitive Scenario
  • Article

January 2025

IEEE Transactions on Wireless Communications

Runyi Zhao

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Yuhan Ruan

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Yongzhao Li

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[...]

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Pei Xiao

Time-frequency localization (TFL) based intelligent wideband spectrum sensing is capable of achieving precise dynamic spectrum management. Recent studies demonstrate that object detectors can achieve excellent TFL performance in simple electromagnetic scenarios when trained with massive and labeled datasets. However, in real-world concurrent cognitive scenarios that allow users to reuse the same frequency band under a certain interference constraint, the phenomenon of signal overlapping in the time-frequency domain will seriously degrade the performance of object detector. To the best of our knowledge, no comprehensive analysis has been conducted to assess the impact of overlapping in TFL. To fill this research gap, we analyze the impact of overlapping and identify three challenges: variety of overlapping , hard to label , and feature destruction . To enhance the robustness of the detector, we first adopt a self-supervised learning (SSL) framework based on a masked autoencoder. This framework aims to pre-train a backbone with excellent feature extraction ability using unlabeled dataset to overcome variety of overlapping and labeling difficulties. Subsequently, we develop a transformer based robust TFL (TRTFL) detector. This detector is designed to leverage both time-frequency correlation and fine-grained features, effectively addressing issues related to feature destruction. Finally, simulation results demonstrate the superiority of the proposed method and the effectiveness of SSL framework and TRTFL. Compared to existing detectors, the TRTFL achieves superior feature extraction, yielding a mean average precision (mAP) of 90.70% in overlapping signal scenarios. Moreover, the TRTFL with SSL can achieve an mAP of up to 95.08% outperforming the state-of-the-art.


Design of Relay Selection Schemes for Uplink Cooperative NOMA Over Nakagami-m Fading

January 2025

IEEE Transactions on Vehicular Technology

This paper investigates relay selection (RS) schemes for uplink cooperative non-orthogonal multiple access (NOMA) systems, where two users with different priorities would like to transmit their information to a base station by means of multiple relays. Based on the degree of the use for the channel state information (CSI), five RS schemes with different user ordering and power allocation (PA) strategies are proposed, in which different tradeoffs between the performance and system overhead or complexity are considered. To evaluate the performance, we derive the closed-form expressions of the outage probability along with the diversity orders for all these RS schemes. The analytical results show that non-zero diversity orders can be harvested if the instantaneous CSI is employed when designing the user ordering or PA strategies. Furthermore, the provided simulation results demonstrate that better outage performance gain can be obtained if the collected CSI is used more fully when designing the RS schemes. Also, the improvement due to the use of dynamic PA strategies is more prominent in contrast to the situation where only user ordering strategies are enhanced, since lower outage probability can be always observed and there is no extra constraint for the PA coefficients.


Hybrid CJT-NCJT Serving Mode Empowered Energy-Efficient Cell-Free Massive MIMO Systems With Limited Fronthauls

January 2025

IEEE Wireless Communications Letters

Existing studies show that the non-coherent joint transmission (NCJT) serving mode can outperform the coherent joint transmission (CJT) serving mode in terms of energy efficiency (EE) in cell-free massive multi-input multi-output (CF-mMIMO) systems with limited fronthauls, although the CJT can achieve prominent coherent cooperative gain. However, relying solely on NCJT serving mode results in the underutilization of available fronthaul resources, failing to harvest the potential coherent gain of the system. Thus, to further improve the EE, we investigate energy-efficient resource allocation in CF-mMIMO systems under a hybrid CJT-NCJT serving framework, where users’ equipment (UEs) are categorized to operate in either CJT serving mode or NCJT serving mode. The emphasis of maximizing EE in the CJT-NCJT hybrid serving mode enabled CF-mMIMO system lies in fully utilizing fronthaul resources through UE serving mode allocation. To address this issue, we propose a matching-based serving mode allocation algorithm, embedded with a Dinkelbach-based power allocation algorithm for CF-mMIMO systems with limited fronthauls.


A Hierarchical Game Framework for Win-Win Resource Trading in Cognitive Satellite Terrestrial Networks

October 2024

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10 Reads

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1 Citation

IEEE Transactions on Wireless Communications

With the increasing security concerns of the satellite network due to the broadcasting nature and the inherent openness of satellite-terrestrial communications, the satellite spectrum and terrestrial node resource trading based cooperation in cognitive satellite terrestrial networks (CSTNs) has gained a lot attention. However, the existing literature has not well considered the fairness issue in resource trading, which may cause cooperation failure between the satellite and terrestrial networks when their own benefits are impaired. To tackle this issue, in this paper we propose a two-layer hierarchical game framework for a multi-terrestrial base stations (BSs) CSTN scenario to guarantee the fairness of resource trading between the satellite and terrestrial networks and thus achieve a win-win situation for both networks. Specifically, a coalition formation game is adopted to study the cooperative behaviors among the terrestrial BSs. Herein, we propose a distributed merge-and-split based coalition formation algorithm to determine the coalition structure, of which the stability, convergence, and complexity are theoretically investigated. Moreover, a Stackelberg game is introduced to model the competition between the satellite and terrestrial BSs, where the satellite acts as the leader and the terrestrial BSs act as the followers. The Stackelberg equilibrium (SE) for the Stackelberg game is derived based on the backward induction method. We then design a distributed algorithm to obtain the coalition structure and SE for the proposed two-layer hierarchical game framework. Finally, simulations are presented to validate our theoretical results.


Power Control and Random Serving Mode Allocation for CJT-NCJT Hybrid Mode Enabled Cell-Free Massive MIMO With Limited Fronthauls

September 2024

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6 Reads

With a great potential of improving the service fairness and quality for user equipments (UEs), cell-free massive multiple-input multiple-output (mMIMO) has been regarded as an emerging candidate for 6G network architectures. Under ideal assumptions, the coherent joint transmission (CJT) serving mode has been considered as an optimal option for cell-free mMIMO systems, since it can achieve coherent cooperation gain among the access points. However, when considering the limited fronthaul constraint in practice, the non-coherent joint transmission (NCJT) serving mode is likely to outperform CJT, since the former requires much lower fronthaul resources. In other words, the performance excellence and worseness of single serving mode (CJT or NCJT) depends on the fronthaul capacity, and any single transmission mode cannot perfectly adapt the capacity limited fronthaul. To explore the performance potential of the cell-free mMIMO system with limited fronthauls by harnessing the merits of CJT and NCJT, we propose a CJT-NCJT hybrid serving mode framework, in which UEs are allocated to operate on CJT or NCJT serving mode. To improve the sum-rate of the system with low complexity, we first propose a probability-based random serving mode allocation scheme. With a given serving mode, a successive convex approximation-based power allocation algorithm is proposed to maximize the system's sum-rate. Simulation results demonstrate the superiority of the proposed scheme.


QoE-Based Semantic-Aware Resource Allocation for Multi-Task Networks

September 2024

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11 Reads

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7 Citations

IEEE Transactions on Wireless Communications

By transmitting task-related information only, semantic communications yield significant performance gains over conventional communications. However, the lack of mature semantic theory about semantic information quantification and performance evaluation makes it challenging to perform resource allocation for semantic communications, especially when multiple tasks coexist in the network. To cope with this challenge, we propose a quality-of-experience (QoE) based semantic-aware resource allocation method for multi-task networks in this paper. First, semantic entropy is defined to quantify the semantic information for different tasks, and the relationship between semantic entropy and Shannon entropy is analyzed. Then, we develop a novel QoE model to formulate the semantic-aware resource allocation in terms of semantic compression, channel assignment, and transmit power. The compatibility of the formulated problem with conventional communications is further demonstrated. To solve this problem, we decouple it into two subproblems and solved them by a developed deep Q-network (DQN) based method and a proposed low-complexity matching algorithm, respectively. Finally, simulation results validate the effectiveness and superiority of the proposed method, as well as its compatibility with conventional communications.


A Recursion-Based SNR Determination Method for Short Packet Transmission: Analysis and Applications

August 2024

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9 Reads

The short packet transmission (SPT) has gained much attention in recent years. In SPT, the most significant characteristic is that the finite blocklength code (FBC) is adopted. With FBC, the signal-to-noise ratio (SNR) cannot be expressed as an explicit function with respect to the other transmission parameters. This raises the following two problems for the resource allocation in SPTs: (i) The exact value of the SNR is hard to determine, and (ii) The property of SNR w.r.t. the other parameters is hard to analyze, which hinders the efficient optimization of them. To simultaneously tackle these problems, we have developed a recursion method in our prior work. To emphasize the significance of this method, we further analyze the convergence rate of the recursion method and investigate the property of the recursion function in this paper. Specifically, we first analyze the convergence rate of the recursion method, which indicates it can determine the SNR with low complexity. Then, we analyze the property of the recursion function, which facilitates the optimization of the other parameters during the recursion. Finally, we also enumerate some applications for the recursion method. Simulation results indicate that the recursion method converges faster than the other SNR determination methods. Besides, the results also show that the recursion-based methods can almost achieve the optimal solution of the application cases.


Drone Identification Based on Normalized Cyclic Prefix Correlation Spectrum

August 2024

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18 Reads

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1 Citation

IEEE Transactions on Cognitive Communications and Networking

Utilizing deep learning (DL) to identify drones through radio signals has been proven to be a promising approach. However, two significant challenges remain to be solved. The first is how to identify drones effectively at the low signal-to-noise ratio (SNR) regime, and the second is how to identify drones stably among numerous unknown interferences. In theory, sufficient data can alleviate the above problems, but the costs of signal acquisition and labeling are usually unacceptable. In this work, we aim to improve the robustness of feature representation by introducing stable prior knowledge of drone signals to alleviate the two problems. Since drones commonly adopt orthogonal frequency division multiplexing (OFDM) modulation with particular cyclic prefix (CP) structures for video transmission, we propose a drone identification algorithm using a convolutional neural network (CNN) and normalized CP correlation spectrum (NCPCS). The NCPCS is strongly correlated with two invariant parameters, i.e., OFDM symbol duration and CP duration, and is mutually exclusive with other signals. Thus, NCPCS natively improves the robustness of the drone identification system to unknown interference signals. Besides, to keep the characteristic of NCPCS clear at the low SNR regime, we calculate the improved NCPCS by accumulating multiple consecutive OFDM symbols. The increase in correlation length effectively sharpens the peaks at low SNRs. Finally, a suitable CNN and a data augmentation (DA) method for NCPCS are proposed to precisely and generically extract these characteristics from NCPCS to identify drones. In this work, a universal software radio peripheral (USRP) X310 is utilized to collect the radio signals of five drones to construct the experimental dataset. We test the proposed algorithm under two different conditions: Gaussian noise condition and co-frequency interference condition. Experimental results show that the proposed algorithm outperforms waveform-based, spectrum-based and spectrogram-based algorithms under the two conditons. Besides, the proposed algorithm remains good robustness to unknown interference signals.


Elimination of Index Ambiguity for Overlapped Signals in Spectrum Sensing
  • Conference Paper
  • Full-text available

June 2024

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7 Reads

Download


Citations (64)


... When applying DL models to the 6G landscape, they encounter diverse data environments, from small-scale IoT networks to STN and SAGIN [24], [81], which offer wide coverage, high data transmission, and improved service availability and resilience [652], [653]. ...

Reference:

Advanced Deep Learning Models for 6G: Overview, Opportunities and Challenges
A Hierarchical Game Framework for Win-Win Resource Trading in Cognitive Satellite Terrestrial Networks
  • Citing Article
  • October 2024

IEEE Transactions on Wireless Communications

... These features were then fed to classifiers for subsequent classification. However, it is not easy to intercept the transient remote control signal of a drone because the duration of this signal is very short [7]. Zhang et al. [8] proposed a UAV recognition algorithm that relies on an artificial neural network utilizing the slope, kurtosis, and skewness of UAV RF signals as time domain features. ...

Drone Identification Based on Normalized Cyclic Prefix Correlation Spectrum
  • Citing Article
  • August 2024

IEEE Transactions on Cognitive Communications and Networking

... Such an opinion corresponds to the results of this study, but it requires clarification, as universal education implies a harmonious combination of VR technologies with other methods of conducting classes. Zhang et al. (2023) considered a complex of problematic aspects related to expanding data based on modelling the virtual electromagnetic environment for drone signal identification when used in the modern education system. It was noted that the efficiency of using drones to obtain various types of data is due to the fact that drones operate in unlicensed frequency ranges, where there are many devices operating on the same frequency. ...

Virtual Electromagnetic Environment Modeling Based Data Augmentation for Drone Signal Identification
  • Citing Article
  • August 2023

Journal of Information and Intelligence

... However, with this growth comes the need for advanced detection and classification techniques to ensure safe and secure UAV operation. Xue et al. [102] propose a DL approach for UAV identification using RF signals, specifically addressing challenges in scenarios involving nonstandard waveforms, such as unknown operating channels and environmental variations. To address these challenges, the system includes a method to correct frequency mismatches in signals, known as carrier frequency offset (CFO) compensation, using a technique called morphological filtering, which helps align signals accurately under different conditions. ...

Radio Frequency Identification for Drones With Non-Standard Waveforms Using Deep Learning
  • Citing Article
  • January 2023

IEEE Transactions on Instrumentation and Measurement

... However, these methods need to consider inference efficiency further under more limited UAV computing resources with higher efficiency. Min et al. [33] and Zhao et al. [34] have proposed lightweight object detection networks with sub-bit level parameter sizes for real-time UAV applications, which also challenges the model's adaptability. ...

Anchor-Free Multi-UAV Detection and Classification Using Spectrogram
  • Citing Article
  • January 2023

IEEE Internet of Things Journal

... An accurate and reliable method for classifying and locating UAVs based on their RF emissions was presented in [85]. Specifically, a passive monitoring framework was presented consisting of numerous distributed receivers strategically situated across various locations, aimed at capturing RF signals emitted by UAVs during their activities. ...

Radio Frequency Based Distributed System for Noncooperative UAV Classification and Positioning
  • Citing Article
  • August 2023

Journal of Information and Intelligence

... The reason is that in some application scenarios, the center frequency and subchannel division of PUs change adaptively with service requirements and channel state, and it is difficult to obtain this prior knowledge. In view of this, many excellent DL-based time-frequency localization (TFL) methods have emerged [13]- [15], which can obtain the timefrequency information (TFI) of each time-frequency block (TF-Block) well in broadband SS. ...

CCD-GAN for Domain Adaptation in Time-Frequency Localization-Based Wideband Spectrum Sensing
  • Citing Article
  • September 2023

IEEE Communications Letters

... This approach regards the feature selection process as a search problem. Common means are Forward Selection [57,58], Backward Elimination [59,60], and Recursive Feature Elimination [61,62]. The wrapper method can consider the interaction and dependence between features [63]. ...

Recursive Feature Elimination Based Feature Selection in Modulation Classification for MIMO Systems

Chinese Journal of Electronics

... The authors proposed a direct and effective solution to maximize the achievable rates in the FSC structure based on minimum mean square error optimization [22]. Furthermore, some authors adopted FSC structures to design hybrid beamformers for the maximization of energy efficiency [23][24][25][26]. The authors considered a FSC structure, where the analog part is updated elementwise to minimize the interference-leakage between its subblocks and the digital part is then updated to maximize the EE based on the alternating optimization technique [23]. ...

Energy Efficient Hybrid Precoding for Adaptive Partially-Connected mmWave Massive MIMO: A Decomposition-Based Approach
  • Citing Article
  • December 2023

IEEE Transactions on Vehicular Technology