RiSi: Spectro-temporal RAN-agnostic Modulation Identification for OFDMA Signals

Blind modulation identification is essential for 6G's RAN-agnostic communications, which identifies the modulation type of an incompatible wireless signal without any prior knowledge. Nowadays, research on blind modulation identification relies on deep convolutional networks that deal with a received signal's raw I/Q samples, but they mostly are limited to single-carrier signal recognition thus not pragmatic for identifying spectro-temporal OFDM/OFDMA signals whose modulation varies with time and frequency. Therefore, this paper proposes RiSi, a semantic segmentation neural network designed to work on OFDMA's spectrograms, by replacing vanilla DeepLabV3+'s 2D convolutions with 'flattened' convolutions to enforce the time-frequency orthogonality constraint and to achieve the grid-like pattern of OFDMA's resource blocks, and by introducing three-channel inputs consisting of I/Q/amplitude. Then, we synthesized a realistic and effective dataset consisting of OFDMA signals with various channel impairments to train the proposed network. Moreover, we treated varying communication parameters as different domains to apply domain generalization methods, to enhance our model's adaptability to diverse communication environments. Extensive evaluation shows that RiSi's modulation identification accuracy reaches 86% averaged over four modulation types (BPSK, QPSK, 16-QAM, 64-QAM), while its domain generalization performance for unseen data has been also shown to be reliable.Comment: 10 pages, 10 figure

User Recognition Based on Human Body Impulse Response: A Feasibility Study

Human recognition technologies for security systems require high reliability and easy accessibility in the advent of the internet of things (IoT). While several biometric approaches have been studied for user recognition, there are demands for more convenient techniques suitable for the IoT devices. Recently, electrical frequency responses of the human body have been unveiled as one of promising biometric signals, but the pilot studies are inconclusive about the characteristics of human body as a transmission medium for electric signals. This paper provides a multi-domain analysis of human body impulse responses (HBIR) measured at the receiver when customized impulse signals are passed through the human body. We analyzed the impulse responses in the time, frequency, and wavelet domains and extracted representative feature vectors using a proposed accumulated difference metric in each domain. The classification performance was tested using the k-nearest neighbors (KNN) algorithm and the support vector machine (SVM) algorithm on 10-day data acquired from five subjects. The average classification accuracies of the simple classifier KNN for the time, frequency, and wavelet features reached 92.99%, 77.01%, and 94.55%, respectively. In addition, the kernel-based SVM slightly improved the accuracies of three features by 0.58%, 2.34%, and 0.42%, respectively. The result shows potential of the proposed approach for user recognition based on HBIR

Analysis and optimization for non-orthogonal pilot sequence sets in massive MIMO systems

In modern wireless communication systems, orthogonal pilot signals has been generally employed in estimation of the channel state information. However, orthogonal pilot signals are inadequate for supporting the rapidly increasing requirements of communication throughput for 5G-and-beyond wireless environments, owing to the pilot contamination and short coherence times in high-mobility situations. To address these concerns, we present a new strategy for making use of non-orthogonal pilot sequences in channel estimation for multi-cell massive multiple-input multiple-output systems. First, we extend prior pilot assignment strategies based on the orthogonality of pilots to the general case of non-orthogonal pilot signals. Based on the proposed non-orthogonal pilot assignment strategy, we establish the minimal pilot length that fulfills a requirement for the channel estimate error, under a given degree of non-orthogonality. Then, we demonstrate validity of the pilot assignment strategy with the minimal length, which maximizes the entire network throughput. Simulation results show that our proposed method gives a significantly enhanced performance in terms of the net throughput compared to that with orthogonal pilot sequences. The performance gain becomes particularly significant with a higher density of users or shorter coherence time intervals