Jianping Du

This letter provides a cross-layer perspective for the de-aggregation of aggregated packets. First, we analyze the natural redundancy in protocols that can be leveraged for delimitation. Second, we propose a new path metric, focusing on maximizing the likelihood of all potential redundant protocol fields. Subsequently, a Viterbi-based path search algorithm is developed to achieve the theoretical optimum. When repeated fields are available in the protocol stack, we introduce a decision feedback mechanism to further enhance performance. Tests conducted on aggregated High-Level Data Link Control (HDLC) protocol packets demonstrate that our method can approach the approximate performance upper limit with significantly reduced complexity and improved operability

In the realm of aviation, trajectory data play a crucial role in determining the target’s flight intentions and guaranteeing flight safety. However, the data collection process can be hindered by noise or signal interruptions, thus diminishing the precision of the data. This paper uses the bidirectional encoder representations from transformers (BERT) model to solve the problem by masking the high-precision automatic dependent survey broadcast (ADS-B) trajectory data and estimating the mask position value based on the front and rear trajectory points during BERT model training. Through this process, the model acquires knowledge of intricate motion patterns within the trajectory data and acquires the BERT pre-training Model. Afterwards, a refined particle filter algorithm is utilized to generate alternative trajectory sets for observation trajectory data that is prone to noise. Ultimately, the BERT trajectory pre-training model is supplied with the alternative trajectory set, and the optimal trajectory is determined by computing the maximum posterior probability. The results of the experiment show that the model has good performance and is stronger than traditional algorithms.

Mainstream wireless communication networks mostly adopt the layered protocol encapsulation mechanism, which inevitably introduces natural redundancy into protocols. To fully exploit the abundant natural redundancy in protocols (NRP), this paper provides a novel cross-frame and cross-layer perspective for channel coding and decoding (CCD). First, a generalized CCD architecture is developed for the first time to describe the NRP and artificial redundancy in channel codes (ARC) uniformly. The concept of equivalent code rate is proposed correspondingly to evaluate the potential gain of NRP. Second, we summarize the types of NRP, and for each of them, factor graphs and sum-product algorithms (SPAs) are adopted to model and solve the correlation between it and ARC. On this basis, the joint decoding of multi-class NRP and ARC is further considered, and a general SPA-based joint protocol-channel decoding (JPCD) scheme is presented. Finally, tests on the 802.11n TCP/IP/MAC protocol stack demonstrate that the JPCD scheme can significantly improve the decoding performance of both the packet header and payload with a relatively small computational cost. Our work shows great promise in reducing error retransmission, bandwidth overhead, and the difficulty of source recovery.

In this paper, we consider exploiting the natural redundancy (NR) that widely exists in the transmitted sources to improve the performance of communication signal detection. To exploit the abundant NR in the sources, we introduce the pre-trained models (PTMs) with a powerful capability to capture knowledge from massive labeled and unlabeled data into sequence detection. Especially for uncompressed Chinese text sources, we propose an effective sequence detection scheme aided by the widely used Bidirectional Encoder Representation from Transformers (BERT) deep learning model and combined with the beam search (BS) algorithm, which can achieve approximate sequence MAP estimation with an acceptable complexity. Simulation results demonstrate that the bit error rate (BER) performance of our proposed BERT-aided BS scheme for sequence detection significantly outperforms the classical detector.

The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been proposed for different application scenarios; currently, there is no unified mobility model that can be adapted to all scenarios. The mobility of nodes is an essential characteristic of mobile ad hoc networks (MANETs), and the motion state of nodes significantly impacts the network’s performance. Currently, most related studies focus on the establishment of mathematical models that describe the motion and connectivity characteristics of the mobility models with limited universality. In this study, we use a backpropagation neural network (BPNN) to explore the relationship between the motion characteristics of mobile nodes and the performance of routing protocols. The neural network is trained by extracting five indicators that describe the relationship between nodes and the global features of nodes. Our model shows good performance and accuracy of classification on new datasets with different motion features, verifying the correctness of the proposed idea, which can help the selection of mobility models and routing protocols in different application scenarios having the ability to avoid repeated experiments to obtain relevant network performance. This will help in the selection of mobility models for drone networks and the setting and optimization of routing protocols in future practical application scenarios.

A high-performance communication receiver desires a sufficient and accurate recognition of transmitted sources. In this paper, we propose a sequential decoding algorithm for the robust reception of sources with natural redundancy (NR) over the AWGN channel. To fully exploit the abundant NR in the exemplified English text sources, a causal language modeling (CLM) with a powerful Transformer decoder neural network (NN) structure is adopted for modeling the joint probability distribution. For versatility of byte-level tokenization, the UTF-8 encoding scheme is considered for the Wikipedia corpus, namely the English edition of the Wiki-40B dataset. The tree search-based M-algorithm (MA) is integrated with CLM, denoted as CLM-MA algorithm, to synthesize the a priori probability of information sequence and likelihood values of polluted symbols. Both simulation experiments and hardware platform evaluations are conducted to analyze the performance of the CLM-MA algorithm. With an adequately large M value, tremendous performance profit is achievable, especially for high-efficiency modulation levels and extremely noisy conditions. This mechanism of adopting the pre-trained NN model merely desires self-supervised learning architecture and eliminates the requirement of high-cost and accurate labels, which paves a new way for communication receiver design.

In this paper, we consider improving the performance of symbol detection by utilizing the natural redundancy (NR) that widely exists in the transmission sources. As an initial study, the NR considered in this paper is the redundancy inherent in the data caused by the non-uniform distribution of the memoryless transmission sources. We first theoretically analyze the utilization of NR to improve the performance of symbol detection from the perspective of information theory, and then propose a deep learning (DL) based iterative symbol detection algorithm with the source’s prior distribution unknown. Simulation results demonstrate that the performance of symbol detection can be improved by utilizing NR and our proposed DL-based iterative symbol detection algorithm can obtain the theoretically optimal performance even when the source distribution is blind to the detector.

High-speed information transmission system demands more powerful communication receivers. In this paper, we integrate deep learning algorithms into constructing waveform level receiver, which is capable of harnessing natural redundancy (NR) in raw message sources. Owing to end-to-end learning paradigm of neural network (NN), the proposed NN receiver can accomplish joint symbol timing synchronization and demodulation based on oversampled waveform sequences directly. The prevalent bidirectional LSTM (BiLSTM) and Transformer structures, suitable for excavating sequential correlation of complex sources and waveform sequences, are considered as the backbone of NN receivers. Under the cases of clean label and noisy label, we conceive two novel training algorithms and interpret their principles in theory. The clean label bits are accessible from original source encoding schemes, while noisy label bits are available from the decision results of canonical receivers with independently noised waveform sequences. When noisy labels possess sufficiently small error rates, optimized NN receiver is competitive with that adopting the accurate labels. As for Markov sources and natural language text sources, simulation results show that the proposed NN receivers’ performances are superior to the classical receivers without a priori knowledge, especially when high spectral efficiency modulation schemes are encountered and random noise is significant.

The maneuvering characteristics and range of motion of real aircraft are highly uncertain, which significantly increases the difficulty of trajectory prediction. To solve the problem that high-speed maneuvers and excessive trajectories in airspace cause a decrease in prediction accuracy and to find out the laws of motion hidden in a large number of real trajectories, this paper proposes a deep learning algorithm based on trajectory clustering and spatiotemporal feature extraction, which aims to better describe the regularity of aircraft movement for higher prediction accuracy. First, the abnormal trajectories in the public dataset of automatic dependent surveillance–broadcast (ADS-B) were analyzed, and to ensure the uniform sampling of trajectory data, the cleaning and interpolation of the trajectory data were performed. Then, the Hausdorff distance was used to measure the similarity between the trajectories, K-Medoids was used for clustering, and the corresponding prediction model was established according to the clustering results. Finally, a trajectory spatiotemporal feature extraction network was constructed based on a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network, and a joint attention mechanism was used to obtain the important features of the trajectory points. A large number of actual trajectory prediction experiments showed that the proposed method is more accurate than existing algorithms based on BP, LSTM, and CNN–LSTM models.

Dragonfly algorithm (DA) is a recently proposed optimization algorithm based on swarm intelligence, which has been successfully applied in function optimization, feature selection, parameter adjustment, etc. However, it fails to take individual optimal position into consideration but only relies on population optimal position and 5 behaviours to update individual position, leading to low accuracy, slow convergence, and local optima. To overcome these drawbacks, Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm (TPDA) is proposed. Tent chaotic map is used to initialize the population, making individuals distributed more uniformly in search space to improve population diversity and search efficiency. Population is classified according to individual fitness value, and different position update methods are adopted for different types of individuals to guide the search process and improve the ability of TPDA to jump out of local optima, thus realizing a balance between exploration and exploitation. The efficiency of TPDA has been validated by tests on 18 basic unconstrained benchmark functions. A comparative performance analysis between TPDA, Particle Swarm Optimization (PSO), DA, and Adaptive Learning Factor and Differential Evolution-Based Dragonfly Algorithm (ADDA) has been carried out. Experimental and statistical results demonstrate that TPDA gives significantly better performances compared with PSO, DA, and ADDA on the average and standard deviation in all 18 functions. The global optimization capability of TPDA on high-dimensional functions and the comparison of the time complexity of TPDA and other swarm intelligence algorithms is also verified in the paper. The results indicate that TPDA is able to perform better on optimizing functions without consuming more computational time.