The reconfigurable intelligent surface (RIS)-aided cognitive radio (CR) system holds significant promise for enhancing spectrum utilization. However, its practical implementation hinges critically on accurate channel state information (CSI). Obtaining full cascaded CSI in RIS-aided CR systems with the cross interference between multiple cascaded channels is challenging. To fill this gap, we propose the deep learning-based channel acquisition schemes for the users in static scenario and mobile scenario, respectively. In static scenario, we propose a novel deep neural network (DNN)-based channel estimation scheme named dual output parameter estimation (DOPE). This scheme achieves remarkable normalized mean square error (NMSE) performance in CSI estimation while significantly reducing the required pilot overhead. In mobile scenario, we propose a channel prediction scheme with hybrid recurrent neural network (RNN) and Transformer (HRT-CP). This scheme utilizes RNN to extract dynamic and static features of cascaded channels, and introduces Transformer’s powerful parallel processing capability to efficiently predict dynamic features. By combining static and dynamic features appropriately, the HRT-CP scheme has predicted the future cascaded CSI accurately, and mitigated the error accumulation phenomenon effectively. The simulation results under both near-field and far-field channel models demonstrate that our proposed schemes provide significant gains on NMSE performance compared to other benchmarks.

In reconfigurable intelligent surfaces (RIS)-aided cognitive radio (CR) systems, the estimation of high-dimensional channel state information (CSI) with the interferences for all the cascaded channels between the RIS and any pair of the transceivers is challenging. To solve this problem, we propose a three-stage scheme of the full cascaded channel estimation based on the deep neural network (DNN). At the beginning, the protocol of the two-step pilot transmissions is designed for generating the labeled channel dataset in data preparation stage. Specifically, we propose dual-output parameter estimation (DOPE) architecture to establish and train the DNN model for full cascaded channel estimation simultaneously with a low pilot overhead in model construction and training stage. Notably, our proposed DNNbased DOPE architecture can accomplish the estimation of both the near-field and far-field cascaded channels in the RIS-aided CR system. Besides, the protocol of only one pilot transmission is introduced for online channel estimation in the model deployment stage. The simulation results show that our proposed scheme outperforms the compared benchmark estimation algorithms at the aspects of both the normalized mean square error (NMSE) performance and the cost of the pilot overhead.

Using multiple sensors to update the status process of interest is promising in improving the information freshness. The unordered arrival of status updates at the monitor end poses a significant challenge in analyzing the timeliness performance of parallel updating systems. This work investigates the age of information (AoI) of a discrete-time dual-sensor status updating system. Specifically, the status update is generated following the zero-waiting policy. The two sensors are modeled as a geometrically distributed service time queue and a deterministic service time queue in parallel. We derive the analytical expressions for the average AoI and peak AoI using the graphical analysis method. Moreover, the connection of average AoI between discrete-time and continuous-time systems is also explored. It reveals that in dual-queue systems, the AoI results of continuous-time systems with exponential time distribution can be extended from the limit cases of discrete-time systems with geometric distribution. Numerical results validate the effectiveness of our analysis and further show that randomness of service time contributes more AoI reduction than determinacy of service time in dual-queue systems in most cases, which is different from what is known about the single-queue system.

Emerging applications in industrial Internet of things (IIoT) pursue ultra-reliability, low-latency, and high data rate. While majority of the industries are in the remote areas, capability of distant ultra-reliable and low-latency communication (uRLLC) has become one of the key performance indices of IIoT which we need to make a breakthrough. While relaying provides intuitive solution for improving communication distance, the introduction of relay in distant uRLLC intensifies the conflict between reliability and low-latency which further deteriorates the throughput of the relaying-based IIoT. In this work, we adopt full-duplex relaying to enhance the performance of distant uRLLC in IIoT. Specifically, we improve the reliability at MAC layer and throughput at physical layer in full-duplex relaying-based IIoTs, through jointly optimizing the coding rate and the power allocation between the source and relay node. In particular, a low-complexity algorithm is developed to find the exact optimal coding rate and relay power under total system power constraint. Extensive numerical results validate the conclusion that the reliability and throughput of distant uRLLC in IIoT are enhanced through full-duplex relaying, power allocation, and rate adaptation.

Blokchain is a promising technology to enable distributed and reliable data sharing at the network edge. The high security in blockchain is undoubtedly a critical factor for the network to handle important data item. On the other hand, according to the dilemma in blockchain, an overemphasis on distributed security will lead to poor transaction-processing capability, which limits the application of blockchain in data sharing scenarios with high-throughput and low-latency requirements. To enable demand-oriented distributed services, this paper investigates the relationship between capability and security in blockchain from the perspective of block propagation and forking problem. First, a Markov chain is introduced to analyze the gossiping-based block propagation among edge servers, which aims to derive block propagation delay and forking probability. Then, we study the impact of forking on blockchain capability and security metrics, in terms of transaction throughput, confirmation delay, fault tolerance, and the probability of malicious modification. The analytical results show that with the adjustment of block generation time or block size, transaction throughput improves at the sacrifice of fault tolerance, and vice versa. Meanwhile, the decline in security can be offset by adjusting confirmation threshold, at the cost of increasing confirmation delay. The analysis of capability-security trade-off can provide a theoretical guideline to manage blockchain networks based on the requirements of data sharing scenarios.

This brief presents an adaptive zero-current detector (ZCD) for the single-inductor multiple-output (SIMO) DC-DC converter with a full-wave current sensor. The innovative adaptive ZCD, which can be applied to the order power distribution control (OPDC) SIMO DC-DC converter, is designed, and it can accurately turn off the low-side power switch when the SIMO DC-DC converter operates in the discontinuous conduction mode. Besides, a new full-wave current sensor which contains only one sensing transistor is presented, and it can precisely sense the inductor current with a small delay. The SIMO DC-DC converter is designed and fabricated in a standard 65 nm CMOS process with output power ranges from 3.7 to 925 mW. The measured reverse current is reduced by up to 78.2%, and the measured light-load power efficiency is improved by up to 10%.

Due to the characteristics of the DCF model in 802.11 and P2P users' requirements of both downloading and uploading, P2P traffic frequently causes congestion in WLAN. However, traditional P2P cache cannot alleviates the congestion caused by P2P traffic in WLAN effectively. In this paper, we propose a novel solution called bidirectional cache for this problem. A bidirectional cache which contains a reverse cache as well as a forward cache can be deployed at the AC of a WLAN. Being a novel design, the reverse cache can cache release the uplink bandwidth resource at each AP can be and the congestion can be alleviated effectively. Meanwhile, the forward cache can still act as the traditional P2P cache to reduce the cross domain traffic. Simulation results show that, compared with traditional P2P cache, our bidirectional cache can shorten the file transfer time of P2P applications by at least 42% and improve the throughput of the other Internet applications by at least 28%.