The application of inverse synthetic aperture radar (ISAR) is investigated in orthogonal frequency-division multiplexing (OFDM) integrated sensing and communication (ISAC) systems. In contrast to velocity sensing of a point target of most ISAC works, ISAR enables rotational velocity sensing to obtain the cross-range values of different scatterers on an extended target. To utilize this characteristic, we initially derive the ISAR OFDM received signal reconstruction in the frequency domain, which demonstrates that the ISAR OFDM echo signal can be equivalent to the signal received by an array, including the decoupled radial range and cross-range parameters. According to the derived signal model, a supporting parameter estimation algorithm based on the equivalent array form is proposed to estimate the range and cross-range parameters for resolvable scatterers on the extended target. Finally, numerical results confirm the effectiveness of utilizing ISAR sensing in wideband ISAC systems.

Automatic modulation classification (AMC) is to identify the modulation type of a received signal, which plays a vital role to ensure the physical-layer security for Internet of Things (IoT) networks. Inspired by the great success of deep learning in pattern recognition, the convolutional neural network (CNN) and recurrent neural network (RNN) are introduced into the AMC. In general, there are two popular data formats used by AMC, which are the in-phase/quadrature (I/Q) representation and amplitude/phase (A/P) representation, respectively. However, most of AMC algorithms aim at structure innovations, while the differences and characteristics of I/Q and A/P are ignored to analyze. In this article, lots of popular AMC algorithms are reproduced and evaluated on the same data set, where the I/Q and A/P are used, respectively, for comparison. Based on the experimental results, it is found that: 1) CNN-RNN-like algorithms using A/P as input data are superior to those using I/Q at high signal-to-noise ratio (SNR), while it has an opposite result in low SNR and 2) the features extracted from I/Q and A/P are complementary to each other. Motivated by the aforementioned findings, a multitask learning-based deep neural network (MLDNN) is proposed, which effectively fuses I/Q and A/P. In addition, the MLDNN also has a novel backbone, which is made up of three blocks to extract discriminative features, and they are CNN block, bidirectional gated recurrent unit (BiGRU) block, and a step attention fusion network (SAFN) block. Different from most of CNN-RNN-like algorithms (i.e., they only use the last step outputs of RNN), all step outputs of BiGRU can be effectively utilized by MLDNN with the help of SAFN. Extensive simulations are conducted to verify that the proposed MLDNN achieves superior performance in the public benchmark.

The Internet of Things (IoT) permeates every aspect of our daily lives as billions of interconnected devices are deployed in the physical world. However, IoT networks operate in an untrusted environment and often suffer from many malicious active attacks. Automatic modulation classification (AMC), which can identify the modulation format of intercepted signals without prior knowledge, is a vital technology in countering physical-layer threats of IoT. However, most of the existing algorithms assume the channel is time invariant, and the AMC in time-varying channels is not been well studied. To deal with this dilemma, a novel AMC algorithm MCBLDN consisting of multiple convolutional neural networks (CNNs), a bidirectional long short-term memory network (BLSTM), and a deep neural network (DNN) is proposed. In MCBLDN, a multislot constellation diagram (CD) method is proposed to extract time-evolution characteristics for generating more discriminative features. Specifically, different grayscale subimages generated by slotted CDs are processed serially by their respective CNNs. Therefore, MCBLDN is overparameterized and time consuming. In addition, the frequency offset and phase offset caused by time-varying channels are neglected in MCBLDN, which is detrimental to the performance of AMC. To address the mentioned disadvantages, a lightweight MCBLDN with a spatial transformer network (SLCBDN) is proposed. First, the multiple CNNs in MCBLDN are pruned into a lightweight classification model, and the input data are rearranged to facilitate parallel processing by the lightweight CNN. Additionally, the spatial transformer network (STN) is utilized to reduce the influence of frequency offset and phase offset. Numerical results verify that the proposed method achieves superior performance and higher speed compared to the baseline algorithm MCBLDN.

Automatic modulation classification (AMC) identifies a received signal’s modulation scheme without prior knowledge of the intercepted signal, which enables significant applications in both the military and civilian domains. Inspired by the great success of deep learning (DL), lots of neural networks are introduced into AMC. To further improve classification performance, various complementary cues including in-phase/quadrature (I/Q), amplitude/phase (A/P), constellation, and other formats are used together to enhance the discrimination of the DL model, where only outputs of the last layer are used. In this paper, we find that different layers’ outputs in the DL model are also complementary to each other. As a result, a hierarchical classification head based convolutional gated deep neural network (HCGDNN) is proposed by utilizing different layers’ output, which only uses the I/Q cue. The proposed HCGDNN consists of three groups of convolutional neural networks (CNN) blocks, two groups of bidirectional gated recurrent units (BiGRU), and a hierarchical classification head. Compared to the long short-term memory (LSTM), the BiGRU has a smaller computational complexity and also releases the gradient dispersion and explosion in the training phase. With the help of the hierarchical classification head, three groups of modulation predictions are made for a received I/Q signal. After that, a novel nonlinear optimization fusion method is derived to generate fusion weights to fuse different groups, then a final classification decision is made. Compared to AMC methods using various cues, the proposed HCGDNN only uses I/Q cue and has low computational overhead. Numerical results suggest that the newly developed HCGDNN achieves superior performance on the public benchmark.To help other researchers, the source code will be uploaded to the github as long as the paper is published.