This paper investigates a wireless-powered cooperative communication network consisting of a source, a destination and a multi-antenna decode-and-forward relay. We consider the relay as a wireless-powered node that has no external power supply; but it is equipped with an energy harvesting (EH) unit and a rechargeable battery such that it can harvest and accumulate energy from radio-frequency signals broadcast by the source. By fully incorporating the EH feature of the relay, we develop an opportunistic relaying protocol, termed accumulate-then-forward (ATF), for the considered WPCCN. We then adopt the discrete Markov chain to model the dynamic charging and discharging behaviors of the relay battery. Based on this, we derive a closed-form expression for the exact outage probability of the proposed ATF protocol. Numerical results show that the ATF scheme can outperform the direct transmission one, especially when the amount of energy consumed by relay for information forwarding is optimized.
This paper investigates a wireless-powered communication network (WPCN) setup with one multi- antenna access point (AP) and one single-antenna source. It is assumed that the AP is connected to an external power supply, while the source does not have an embedded energy supply. But the source could harvest energy from radio frequency (RF) signals sent by the AP and store it for future information transmission. We develop a discrete time-switching (DTS) protocol for the considered WPCN. In the proposed protocol, either energy harvesting (EH) or information transmission (IT) operation is performed during each transmission block. Specifically, based on the channel state information (CSI) between source and AP, the source can determine the minimum energy required for an outage-free IT operation. If the residual energy of the source is sufficient, the source will start the IT phase. Otherwise, EH phase is invoked and the source accumulates the harvested energy. To characterize the performance of the proposed protocol, we adopt a discrete Markov chain (MC) to model the energy accumulation process at the source battery. A closed-form expression for the average throughput of the DTS protocol is derived. Numerical results validate our theoretical analysis and show that the proposed DTS protocol considerably outperforms the existing harvest-then-transmit protocol when the battery capacity at the source is large.
Enhancing information freshness in wireless networks has gained significant attention in recent years. To optimize or analyze information freshness, which is often characterized by the age of information (AoI) metric, extensive theoretical studies have been conducted on various wireless networks. Early research has demonstrated the significance of last-come-first-served (LCFS) packet scheduling and controlled status sampling (i.e., packet generation) in improving information freshness. These mechanisms have been widely adopted in subsequent studies. However, the effective implementation of these mechanisms in commercial off-the-shelf (COTS) wireless devices has not been thoroughly investigated, which could limit the practical application of information freshness-oriented protocols in real-world systems. Our work aims to address the gap by exploring the effective implementation of the information freshness-oriented mechanisms mentioned above in COTS WiFi devices that use the Linux operating systems. Our attempts reveal that the physical layer queue of WiFi devices operates on a first-come-first-served (FCFS) basis, and the packet generation process cannot be precisely controlled by default. To overcome these challenges, we develop Fresh-Fi, an information freshness-oriented protocol stack that involves careful customizations to the Linux networking protocol stack. Fresh-Fi mainly incorporates a mac80211 subsystem-based LCFS queue and a real-time kernel-based cross-layer tunnel between the mac80211 subsystem and the application layer for triggered packet generation. Our experiments show that implementing Fresh-Fi can significantly improve AoI performance. Specifically, we observed that Fresh-Fi improved AoI performance by over 13 times when compared to a baseline design that relies on an LCFS queue implemented in the application layer in standard Linux.
Aiming at the current problem of the lack of automatic real-time monitoring and management of drop-out fuses in the distribution station area, this paper proposes a station-level intelligent monitoring and management software for drop-out fuses based on a new energy intelligent monitoring platform. The software establishes intelligent monitoring and management software through RS485 serial ports The connection between the monitoring platform and the drop-type fuse status information collector, the intelligent monitoring platform can automatically call and test the collector information in a timed cycle through the algorithm, and fuse the station area through the 4G/5G network after analyzing the fault information actively reported by the collector. The fault information of the fuse is reported to the master station to realize the intelligent real-time monitoring of the drop-type fuse equipment in the station area, help the equipment maintenance personnel in the distribution station area to locate the fault fuse location and fault type at the first time, and greatly improve the fault response of the power distribution equipment in the station area. Speed and robustness of power distribution system, realize automatic intelligent monitoring and management of power distribution equipment in Taiwan area.
Interactions between users and videos are the major data source of performing video recommendation. Despite lots of existing recommendation methods, user behaviors on videos, which imply the complex relations between users and videos, are still far from being fully explored. In the paper, we present a model named Sagittarius. Sagittarius adopts a graph convolutional neural network to capture the influence between users and videos. In particular, Sagittarius differentiates between different user behaviors by weighting and fuses the semantics of user behaviors into the embeddings of users and videos. Moreover, Sagittarius combines multiple optimization objectives to learn user and video embeddings and then achieves the video recommendation by the learned user and video embeddings. The experimental results on multiple datasets show that Sagittarius outperforms several state-of-the-art models in terms of recall, unique recall and NDCG.
Achieving ultra-reliable, low-latency and secure communications is essential for realizing the industrial Internet of Things (IIoT). Non-coherent massive multiple-input multiple-output (MIMO) is one of promising techniques to fulfill ultra-reliable and low-latency requirements. In addition, physical layer authentication (PLA) technology is particularly suitable for secure IIoT communications thanks to its low-latency attribute. A PLA method for non-coherent massive single-input multiple-output (SIMO) IIoT communication systems is proposed in this paper. This method realizes PLA by embedding an authentication signal (tag) into a message signal, referred to as "message-based tag embedding". It is different from traditional PLA methods utilizing uniform power tags. We design the optimal tag embedding and optimize the power allocation between the message and tag signals to characterize the trade-off between the message and tag error performance. Numerical results show that the proposed message-based tag embedding PLA method is more accurate than the traditional uniform tag embedding method which has an unavoidable tag error floor close to 10%.
Enhancing information freshness in wireless networks has gained significant attention in recent years. To optimize or analyze information freshness, which is often characterized by the age of information (AoI) metric, extensive theoretical studies have been conducted on various wireless networks. Early research has demonstrated the significance of last-come-first-served (LCFS) packet scheduling and controlled status sampling (i.e., packet generation) in improving information freshness. These mechanisms have been widely adopted in subsequent studies. However, the effective implementation of these mechanisms in commercial off-the-shelf (COTS) wireless devices has not been thoroughly investigated, which could limit the practical application of information freshness-oriented protocols in real-world systems. Our work aims to address the gap by exploring the effective implementation of the information freshness-oriented mechanisms mentioned above in COTS WiFi devices that use the Linux operating system. Our attempts reveal that implementing these mechanisms in COTS systems is not a straightforward task. Specifically, we found that the physical layer queue of WiFi devices operates on a first-come-first-served (FCFS) basis, and the packet generation process cannot be precisely controlled by default. To overcome these challenges, we develop Fresh-Fi, an information freshness-oriented protocol stack that involves careful customization to the lower layers of the Linux networking protocol stack. Fresh-Fi mainly incorporates a mac80211 subsystem-based LCFS queue and a real-time kernel-based cross-layer tunnel between the mac80211 subsystem and the application layer for triggered packet generation.
The optical fiber communication network of city power system has the characteristics of dense network, complex laying environment, and the interruption of communication is high cost. Two modes optical fiber network monitoring solution with protective optical path and fault self-recovery is discussed to ensure the reliable work of the optical fiber network. For optical fiber line that lacks core resource, the monitoring mode with protective optical path and fault self-recovery is designed to monitor working core, and the problem of waste fiber core and interruption of working core can be solved. For optical line that plenty of core resource, the monitoring mode is designed to monitor non-working core. The monitoring mode switch method is proposed, and each optical fiber can select one mode independently to adapt different demands. An optical power prediction model is proposed based on an improved Elman neural network. As result, scientific maintenance plan can be made in terms of trend analysis of optical fiber line to reduce maintenance cost. Finally, using experimental core and real optical fiber line in power system, the testing result of the monitoring solution is analyzed to illustrate the effect of the solution.
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