RNA-protein interactions (RPIs) play an important role in several fundamental cellular physiological processes, including cell motility, chromosome replication, transcription and translation, and signaling. Predicting RPI can guide the exploration of cellular biological functions, intervening in diseases, and designing drugs. Given this, this study proposes the RPI-gated graph convolutional network (RPI-GGCN) method for predicting RPI based on the gated graph convolutional neural network (GGCN) and co-regularized variational autoencoder (Co-VAE). First, different types of feature information were extracted from RNA and protein sequences by nine feature extraction methods. Second, Co-VAEs are used to eliminate the redundancy of fused features and generate optimal features. Finally, this study introduces gated cyclic units into graph convolutional networks (GCNs) to construct a model for RPI prediction, which efficiently extracts topological information and improves the model's interpretable feature learning and expression capabilities. In the fivefold cross-validation test, the RPI-GGCN method achieved prediction accuracies of 97.27%, 97.32%, 96.54%, 95.76%, and 94.98% on the RPI369, RPI488, RPI1446, RPI1807, and RPI2241 datasets. To test the generalization performance of the model, we used the model trained on RPI369 to predict the independent NPInter v3.0 dataset and achieved excellent performance in all six independent validation sets. By visualizing the RPI network graph based on the prediction results, we aim to provide a new perspective and reference for studying RPI mechanisms and exploring new RPIs. Extensive experimental results demonstrate that RPI-GGCN can provide an efficient, accurate, and stable RPI prediction method.

The vision of backscatter communication always incorporates compatibility with active radios to enable low-cost and easy deployment. However, recent innovations lack the flexibility to communicate with heterogeneous wireless devices directly. In this paper, we design and implement a flexible backscatter system, i.e., Flexcatter, which can support various modulation schemes in a software-defined way, to be compatible with different kinds of active radios. The key technique is signal emulation, where the tag can vary the reflection coefficient in the time domain to emulate desired baseband signals. We first carefully design a cost-effective impedance network, which employs two radio frequency (RF) switches to provide up to 16 reflection coefficients. Next, we establish and model the emulation mapping between desired baseband signals and available reflection coefficients. Besides, the emulation frameworks for different modulation schemes are presented. After that, to face the emulation distortion for orthogonal frequency division multiplexing (OFDM), we introduce the oversampling method in the baseband modulation process. We further build the prototype hardware, and experiment results show that Flexcatter can flexibly generate various kinds of backscatter signals, including Wi-Fi, BLE, and LoRa. Especially the OFDM transmission generated by Flexcatter can achieve a throughput of 25.1 Mbps.

Ambient LoRa backscatter enables battery-free wireless communication with long-range connectivity for the Internet of Things. However, current efforts mainly focus on symbol-level modulation, which trades considerable data rates for long backscatter ranges. To enhance the data rate, this paper presents and prototypes Pacim, which fully explores the potential of long-period LoRa chirps and conveys additional information by varying symbol lengths. Specifically, we propose the chirp interval modulation scheme that modulates multiple data bits in each time interval between two chirp-based anchor symbols. Moreover, we design a twin-chirp cancellation method at the receiver that eliminates the frequency discontinuity within anchor symbols, and propose a fine-grained detection algorithm to measure the arrival time of anchor symbols in the frequency domain. We further propose three reliable methods to improve transmission reliability and analyze the symbol error rate (SER) performance. We also build a hardware prototype and perform comprehensive evaluations. Our experiment results show that Pacim can achieve up to$8.6 \times $throughput gain while keeping long-range, compared with the state-of-the-art ambient LoRa backscatter design.

In this paper, we design and implement a flexible backscatter system, i.e., Flexcatter, which can support various baseband modulation schemes to be compatible with active radios. The key technique employed by Flexcatter is signal emulation, where the tag can vary the reflection coefficient in the time domain to emulate desired baseband signals. To meet the low-power requirements of Flexcatter, we first carefully design a cost-effective impedance network, which can provide up to 16 reflection coefficients for different signal amplitudes and phases. Moreover, we present an effective emulation strategy for desired baseband signals. Next, we introduce the oversampling method in the baseband modulation pipeline to mitigate the effect of emulation errors caused by hardware deficiency. We further build the prototype hardware, including an FPGA platform and the radio frequency (RF) module. Experiment results show that OFDM transmissions from Flexcatter can achieve a throughput of 21.1 Mbps at the backscatter range of 12 m. To the best of our knowledge, we are the first to generate OFDM transmission with 64 subcarriers using low-power RF switches. Our design has the potential to accept other types of existing active radios as the receiver to reduce the deployment cost significantly.

In this paper, we propose a new framework for covert communication in hybrid active/passive networks, which explores the inherent uncertainty of the backscatter transmissions to achieve active and passive communication reciprocity in security. Under this framework, we first model the aggregate interference from the sporadic backscatter transmissions and derive the covert outage probability and the transmission success probability to capture the covertness and reliability. Then, we formulate a transmit power optimization problem to maximize the covert throughput subject to certain covertness and reliability requirements and derive a closed-form approximation of the maximum covert throughput. Particularly, we investigate the worst-case scenario of covert communication in which warden always uses the optimal detection thresholds. Finally, numerical results demonstrate that covert communication with the help of uninformed backscatters that are not coordinated with Alice in the hybrid network is feasible. Results also provide design guidelines for the optimal choice of interference parameters, which is capable of striking a balance between covertness and reliability.

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce up to 88% of the IoT's TX power when the AP sets 8× clock rate.

Recent years have seen major innovations in developing energy-efficient wireless technologies, such as the Bluetooth low energy (BLE) for Internet of Things (IoT). Despite demonstrating significant benefits in providing low power transmission and massive connectivity, very few of these technologies directly connect to the Internet. Recent advances demonstrate the viability of direct communication among heterogeneous IoT devices with incompatible physical layers. These techniques, however, require modifications in transmission power or time, which may affect the media access control layer behaviors in legacy networks. In this paper, we argue that the frequency domain can serve as a free side channel with minimal interruptions to legacy networks. To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying demodulator and channel state information. The artificial Doppler shifts can be compensated for by the inherent frequency synchronization module and thus have a negligible impact on legacy communications. Our evaluation using commercial off-the-shelf BLE chips and 802.11-compliant testbeds has demonstrated that DopplerFi can achieve a throughput of up to 6.5 Kb/s at the cost of merely less than 0.8% throughput loss.

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce the IoT's TX power by boosting the decoding capability at the receivers.