Current Time Slot Research Articles

An intelligent impulsive noise mitigation with deep learning method

AbstractTo enable message transmission among sensors and equipment, power line communication (PLC) is a widely adopted smart grid. However, due to the occurrence of impulsive noise (IN), reliable transmissions over PLC channels in the smart grid are challenging. Therefore, in this paper, we propose an adaptive noise mitigation scheme to clip the IN with the sliding window‐based method, where the altitude of the received signal in the current time slots is obtained by computing the average altitude of signals in the previous and next time slots. To detect the states of IN and dynamically estimate the power threshold of signals for the IN mitigation scheme, we develop an intelligent algorithm based on the long short‐term memory network. To prevent the useful signals from being eliminated as IN signals, we propose the accelerated proximal gradient method (APGM) based on tone reservation to reduce the peak‐to‐average power ratio (PAPR) for the transmitting signals with low computational complexity. In addition, the closed‐form expression of the bit error rate (BER) is derived for the proposed sliding window‐based IN mitigation scheme according to the probability density function of the IN. Simulation results demonstrate that the proposed IN mitigation scheme achieves a better BER performance than the conventional IN mitigation schemes. In addition, the APGM aided by IN mitigation can further improve BER performance due to the PAPR reduction.

Assessing the assimilation of Himawari-8 observations on aerosol forecasts and radiative effects during pollution transport from South Asia to the Tibetan Plateau

Abstract. Emissions from South Asia (SA) represent a critical source of aerosols on the Tibetan Plateau (TP), and aerosols can significantly reduce the surface solar energy. To enhance the precision of aerosol forecasting and its radiative effects in SA and the TP, we employed a four-dimensional local ensemble transform Kalman filter (4D-LETKF) aerosol data assimilation (DA) system. This system was utilized to assimilate Himawari-8 aerosol optical thickness (AOT) into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to depict one SA air pollution outbreak event in spring 2018. Sensitivity tests for the assimilation system were conducted first to tune temporal localization lengths. Comparisons with independent Moderate Resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET) observations demonstrate that the AOT analysis and forecast fields have more reasonable diurnal variations by assimilating all the observations within a 12 h window, which are both better than assimilating the hourly observations in the current assimilation time slot. Assimilation of the entire window of observations with aerosol radiative effect activation significantly improves the prediction of downward solar radiation compared to the free-run experiment. The AOT assimilation with aerosol radiative effect activation led to a reduction in aerosol concentrations over SA, resulting in increased surface radiation, temperature, boundary layer height, and atmospheric instability. These changes facilitated air uplift, promoting aerosol transport from SA to the southeastern TP and leading to an increase in AOT in this region.

Data-Driven Modeling of Vehicle-to-Grid Flexibility in Korea

With the widespread use of electric vehicles (EVs), the potential to utilize them as flexible resources has increased. However, the existing vehicle-to-grid (V2G) studies have focused on V2G operation methods. The operational performance is limited by the amount of availability resources, which represents the flexibility. This study proposes a data-driven modeling method to estimate the V2G flexibility. A charging station is a control point connected to a power grid for V2G operation. Therefore, the charging stations’ statuses were analyzed by applying the basic queuing model with a dataset of 1008 chargers (785 AC chargers and 223 DC chargers) from 500 charging stations recorded in Korea. The basic queuing model obtained the long-term average status values of the stations over the entire time period. To estimate the V2G flexibility over time, a charging station status modeling method was proposed within a time interval. In the proposed method, the arrival rate and service time were modified according to the time interval, and the station status was expressed in a propagated form that considered the current and previous time slots. The simulation results showed that the proposed method effectively estimated the actual value within a 10% mean absolute percentage error. Moreover, the determination of V2G flexibility based on the charging station status is discussed herein. According to the results, the charging station status in the next time slot, as well as that in the current time slot, is affected by the V2G. Therefore, to estimate the V2G flexibility, the propagation effect must be considered.

Combining Lyapunov Optimization With Evolutionary Transfer Optimization for Long-Term Energy Minimization in IRS-Aided Communications.

This article studies an intelligent reflecting surface (IRS)-aided communication system under the time-varying channels and stochastic data arrivals. In this system, we jointly optimize the phase-shift coefficient and the transmit power in sequential time slots to maximize the long-term energy consumption for all mobile devices while ensuring queue stability. Due to the dynamic environment, it is challenging to ensure queue stability. In addition, making real-time decisions in each short time slot also needs to be considered. To this end, we propose a method (called LETO) that combines Lyapunov optimization with evolutionary transfer optimization (ETO) to solve the above optimization problem. LETO first adopts Lyapunov optimization to decouple the long-term stochastic optimization problem into deterministic optimization problems in sequential time slots. As a result, it can ensure queue stability since the deterministic optimization problem in each time slot does not involve future information. After that, LETO develops an evolutionary transfer method to solve the optimization problem in each time slot. Specifically, we first define a metric to identify the optimization problems in past time slots similar to that in the current time slot, and then transfer their optimal solutions to construct a high-quality initial population in the current time slot. Since ETO effectively accelerates the search, we can make real-time decisions in each short time slot. Experimental studies verify the effectiveness of LETO by comparison with other algorithms.

Temporal Correlation Enhanced Multiuser Detection for Uplink Grant-Free NOMA

Compressed sensing has been identified as a good candidate for user detection in grant-free non-orthogonal multiple access (NOMA) by exploiting the inherent sparsity of user activity. However, most of the existing works do not fully utilize the temporal correlation in NOMA and rely heavily on the unrealistic assumption that the number of active users is known in advance. To address these issues, we propose a temporal correlation enhanced multiuser detection scheme to achieve efficient and practical multiuser detection. Firstly, using 1 bit memory to piggyback the information whether the active users still have data to transmit, the base station can realize whether the active users in current time slot will turn to be silent or remain active. Then, to make explicit use of the temporal correlation of active user sets, a cross validation based adaptive subspace pursuit (CVASP) algorithm, is developed by utilizing the reported information on prior active users. The proposed CVASP is a highly practical algorithm that does not require any prior knowledge of the user sparsity level, as the cross validation technique could properly determine the stopping condition. Extensive simulation results demonstrate that the proposed mechanism could achieve a superier performance without requiring any prior knowledge.

A Nowcasting Central Controller with Continuous RTP for Residential Device Scheduling using Swap-Based BFPSO.

Energy management plays an important role in the residential sector allowing consumers to take control over their energy consumption w.r.t. the market fluctuations. For a long time, forecasting model-based scheduling was thought as a way to mitigate the expected versus reality electricity pricing gap. However, it does not always present a working model owing to uncertainties involved around it. This paper presents a scheduling model having a Nowcasting Central Controller. This model is designed for residential devices using continuous RTP and targets on optimizing the device schedule in the current time slot as well as the subsequent time slots. It is dependent on the current input data and less on the past dataset, making it implemen at any situation. To solve the optimization problem, four variants of PSO in conjunction with swapping operation are implemented on the proposed model by considering a normalized objective function made up of two cost metrics. The results demonstrate a quickness and reduction in costs by BFPSO at each time slot. A comparison is carried out among different pricing schemes that clearly establish the effectiveness of CRTP over DAP and TOD. With CRTP performing the best of the lot, the NCC model is found to be highly adaptable and robust to sudden changes in pricing schemes.

Deep Q-Network Based Beam Tracking for Mobile Millimeter-Wave Communications

In this paper, we present a beam tracking algorithm based on the deep Q-network (DQN) for mobile millimeter-wave (mmWave) communications. The proposed algorithm determines the receive beam angle from the received signals without knowing the channel model and dynamics. It uses the received signals of the current and previous time slots to design the state and reward of the DQN. Our goal is to maximize the signal-to-noise ratio by the actions of the designed DQN. A significant computational complexity reduction is achieved since the receiver does not need to run complicated signal processing algorithms once the DQN is properly trained. Therefore a practical implementation of mmWave beam tracking with a very large number of antennas under harsh mobile environments becomes feasible. Through the extensive simulations, we verified the performance of the proposed algorithm and demonstrated robustness to the system uncertainty and low computational complexity in comparison with particle filter and the Q-learning.

Very Long-Length FFT Using Multi-Resolution Piecewise-Constant Windows for Hardware-Accelerated Time-Frequency Distribution Calculations in an Ultra-Wideband Digital Receiver.

The hardware-accelerated time-frequency distribution calculation is one of the commonly used methods to analyze and present the information from intercepted radio frequency signals in modern ultra-wideband digital receiver (DRX) designs. In this paper, we introduce the piecewise constant window blocking FFT (PCW-BFFT) method. The purpose of this work is to show the generation of spectrograms (formed by a number of spectrum lines) using a very large number of samples (N) in an FFT frame for each spectrum line calculation. In the PCW-BFFT, the N samples are grouped into K consecutive time slots, and each slot has M number of samples. As soon as the M samples in the current time slot are available from a high-speed analog-to-digital convertor (ADC), the frequency information will be obtained using K M-point FFTs. Since each time the FFT frame hops one time slot for the next spectrum line calculation, the frequency information obtained from a time slot will be reused in many spectrum line calculations, as long as these spectrum lines share those samples in the time slot. Although the use of the time domain PCW introduces spikes in the frequency spectrum of the window, the levels of those spikes are still much lower than the first side lobe level of a rectangular window. Using a Gaussian window as an example, the highest spike level can be lower than the main lobe level by at least 38 dB. The PCW-BFFT method allows a DRX to produce multiple spectrograms concurrently with different analysis window widths when the time domain samples become available continuously from the ADC. This paper presents the detailed derivation process of the PCW-BFFT method and demonstrates the use of the method with simulation results. The hardware implementation process will be reported in another paper. The computer simulation results show that long signals with slowly changing frequencies over time can be depicted on the spectrograms with wide analysis windows, and short pulses and signals with rapidly changing instantaneous frequencies can be captured in the narrow analysis window spectrograms.

A Subspace Approach to Sparse-Sampling-Based Multi-Attribute Data Aggregation in IoT

The emergence of the heterogeneous Internet of Things (IoT) has realized the demand for multi-attribute data collection in response to the increasing demand for information in diverse applications. Sparse sampling has been used to reduce network energy consumption in order to extend the life of energy-constrained networks. Real-time multi-attribute data aggregation under the sparse sensing framework has become a research focus. Therefore, we proposed a sparse-sampling-based IoT data aggregation approach to reduce network energy consumption and enable real-time multi-attribute reconstruction. For data collection, a sparse sampling data collection approach is proposed that can successfully collect and transmit the multi-attribute data to the sink even while certain IoT sensor nodes are in the sleep mode. For data reconstruction, a real-time multi-attribute data reconstruction method based on subspace is proposed. The proposed method arranges multi-attribute data in a tensor form in order to further utilize the correlation of multi-attribute data. Subspaces representing the spatial distributions of the multi-attribute data can be obtained from the previously reconstructed data. Incorporating total variation constraint, the proposed method reconstructs the current time slot multi-attribute data with high precision in real time. The experimental results demonstrate the effectiveness of the proposed method in real-time multi-attribute data reconstruction.

An Enhanced Reliable Access Scheme for Massive IoT Applications in Ubiquitous IoT Systems

As a key infrastructure for intelligent manufacturing, the Industrial Internet of Things (IIoT) can connect people, massive intelligent terminals, automated control equipment, and convergence nodes to achieve a high integration of industrial systems with advanced computing, sensor technology, fifth-generation technology (5G), and the Internet. How to realize the robust and reliable connection of massive IoT applications has become the research focus of industry and academia. However, the massive IoT industrial equipment, diverse business types, complex networking mode, and broadcast characteristics of wireless media have posed serious challenges to providing stable and reliable connections in industrial communication scenarios. In order to overcome this problem, the paper proposes an enhanced reliable access scheme (ERAS) for massive IoT applications in software-defined industrial heterogeneous networks. Based on the idea of software-defined networking (SDN), the ERAS is designed from two perspectives of the control plane and the data plane. Based on the idea of greedy algorithms, ERAS uses priority, interference set, deadline, and other information to find the local optimal solution and then obtain the global optimum. First, low-priority periodic data is accessed to the channel based on the earliest deadline. Then, high-priority data (such as control and interactive services) judges the channel occupancy status according to the access flow table, and if there is an idle channel in the current time slot, it is normally accessed. Otherwise, the subslots reserved with high priority in the control channel are used to preempt the low-priority channel. This preemption mechanism can ensure reliable and real-time connections for massive IIoT. Simulation results show that the ERAS performs better than traditional methods in terms of delay, deadline guarantee rate, and packet loss rate.

Real-time POI recommendation via modeling long- and short-term user preferences

Recently, Next Point-of-Interest (POI) Recommendation which proposes users for their next visiting locations, has gained increasing attention. A timely and accurate next POI recommendation can improve users’ efficient experiences. However, most existing methods typically focus on the sequential influence, but neglect the user’s real-time preference changing over time. In some scenarios, users may need a real-time POI recommendation, for example, when using Take-away Applications, users need recommending the appropriate restaurants at the specific moment. Hence, how to mine users’ patterns of life and their current preferences becomes an essential issue for the real-time POI recommendation. To address the issues above, we propose a real-time preference mining model (RTPM) which is based on LSTM to recommend the next POI with time restrictions. Specifically, RTPM mines users’ real-time preferences from long-term and short-term preferences in a uniform framework. For the long-term preferences, we mine the periodic trends of users’ behaviors between weeks to better reflect users’ patterns of life. While for the short-term preferences, trainable time transition vectors which represent the public preferences in corresponding time slots, are introduced to model users’ current time preferences influenced by the public. At the stage of recommendation, we design a category filter to filter out the POIs whose categories are unpopular in corresponding time slots to reduce the search space and make recommendation fit current time slot better. Note that RTPM does not utilize users’ attributes and their current locations for recommendation, which makes great contributions to users’ privacy protection. Extensive experiments on two real-world datasets demonstrate that RTPM outperforms the state-of-the-art models on Recall and NDCG.

Performance Limits of Visible Light-Based Positioning for Internet-of-Vehicles: Time-Domain Localization Cooperation Gain

In this paper, we aim to give a unified performance limit analysis of the visible light-based positioning (VLP) for a vehicular user equipment (UE), which will help to understand the essence of time-domain localization cooperation and gain insights into how to improve the performance limit of the vehicular VLP system. This is challenging due to the complex system models and the complex dependency between UE location performance and orientation performance. To achieve the above goal, we will first characterize the closed-form error bounds of the UE location and orientation at each time slot, respectively, in terms of Fisher information. Generally, the VLP error will propagate over time as the vehicular UE moves, and hence the VLP error at the current time slot is affected by the VLP performance at the previous time slot, the UE mobility and the channel quality. Based on the obtained VLP error bounds, we then reveal the impact of prior UE location knowledge, UE mobility and signal-to-noise-ratio on the VLP performance. Furthermore, the time-domain evolution of the VLP error is studied, where the convergence of the time-domain VLP error evolution is established and its closed-form stable state is quantified, which will shed light on the long-term performance of the vehicular VLP system.

Energy-Efficient Resource Optimization for Hybrid Energy Harvesting Massive MIMO Systems

In this article, we study resource optimization issues for hybrid energy harvesting massive multiple-input–multiple-output (MIMO) systems, where renewable energy harvested from surrounding environments is introduced as additional energy supply to the base station (BS) together with the grid power. Our purpose is to maximize system energy efficiency with the preferential use of renewable energy under several practical restrictions, such as limited battery capacity, energy causality, quality-of-service (QoS) requirement, and transmit power constraints. An offline policy is first presented under ideal assumptions, i.e., noncausal knowledge of the channel state information and energy harvesting dynamics are known in advance, which causes it to become a performance upper bound. In particular, the offline policy is designed in an iterative manner with the use of the fractional programming technique. Moreover, inspired by the offline policy, we exploit statistical information to present two practical online algorithms. Specifically, the Markov chain method predicts prospective information through use of the finite-state Markov model, whereas the timing detection scheme focuses only on the resource optimization of the current time slot. Numerical results illustrate that both online algorithms perform closely to the offline policy, which indicates that they can achieve a good tradeoff between performance and implementation complexity.

Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs.

Constantly changing electricity demand has made variability and uncertainty inherent characteristics of both electric generation and cellular communication systems. This paper develops an online learning algorithm as a prescheduling mechanism to manage the variability and uncertainty to maintain cost-aware and reliable operation in cloud radio access networks (Cloud-RANs). The proposed algorithm employs a combinatorial multi-armed bandit model and minimizes the long-term energy cost at remote radio heads. The algorithm preschedules a set of cost-efficient energy packages to be purchased from an ancillary energy market for the future time slots by learning both from cooperative energy trading at previous time slots and by exploring new energy scheduling strategies at the current time slot. The simulation results confirm a significant performance gain of the proposed scheme in controlling the available power budgets and minimizing the overall energy cost compared with recently proposed approaches for real-time energy resources and energy trading in Cloud-RANs.

A Low-Latency Interference Coordinated Routing for Wireless Multi-Hop Networks

Recently, there has been an increasing interest in exploiting interference cancelation to support multiple adjacent concurrent transmissions instead of avoiding interference through scheduling. In line with these efforts, this paper propose an interference coordinated routing (ICR) scheme for wireless multi-hop networks to achieve more transmission concurrence, and thus lower the end-to-end delay. The proposed ICR scheme firstly constructs an initial path by the interference-aware routing algorithm, which captures the end-to-end latency and spatial resource cost as the routing metrics. Then, to analyze the feasibility of concurrent transmission for a given link set, we consider the interference coordination and formulate the concurrent transmission of multiple links as a linear programming (LP) problem. The solution to the LP problem indicates the power allocation. Finally, a distributed guard zone based selection (GBS) algorithm is further proposed to iteratively explore the maximum feasible link set for each time slot. The selected links are simultaneously active for packet transmission with the allocated power in the current time slot, and the remaining links will be put off to the next. Simulation results confirm that ICR reduces the end-to-end delay by 9.16% to 73.82%, and promotes better transmission concurrence compared with the existing schemes.

Uplink NOMA-based long-term throughput maximization scheme for cognitive radio networks: an actor–critic reinforcement learning approach

Non-orthogonal multiple access (NOMA) is one of the promising techniques for spectrum efficiency in wireless networks. In this paper, we consider an uplink NOMA cognitive system, where the secondary users (SUs) can jointly transmit data to the cognitive base station (CBS) over the same spectrum resources. Thereafter, successive interference cancellation is applied at the CBS to retrieve signals transmitted by the SUs. In addition, the energy-constrained problem in wireless networks is taken into account. Therefore, we assume that the SUs are powered by a wireless energy harvester to prolong their operations; meanwhile, the CBS is equipped with a traditional electrical supply. Herein, we propose an actor–critic reinforcement learning approach to maximize the long-term throughput of the cognitive network. In particular, by interacting and learning directly from the environment over several time slots, the CBS can optimally assign the amount of transmission energy for each SU according to the remaining energy of the SUs and the availability of the primary channel. As a consequence, the simulation results verify that the proposed scheme outperforms other conventional approaches (such as Myopic NOMA and OMA), so the system reward is always maximized in the current time slot, in terms of overall throughput and energy efficiency.

Request Scheduling in Quantum Networks

Quantumnetworking is emerging as a new research area to explore the opportunities of interconnecting quantum systems through end-to-end entanglement of qubits at geographical distance via quantum repeaters. A promising architecture has been proposed in the literature that decouples entanglement between adjacent quantum nodes/repeaters from establishing end-to-end paths by adopting a time slotted approach. Within this model, we destructure further end-to-end path establishment into two subproblems: path selection and scheduling. The former is set to determine the best repeaters to connect two end nodes, provided that all their local entanglements have succeeded. On the other hand, scheduling is concerned with deciding, which pairs of end nodes are served in the current time slot, while the others remain queued for later time slots. Unlike path selection, scheduling has not been investigated so far in the literature, particularly in presence of quantum noise, which makes both problems even more challenging. In this article, we propose to address it via a general framework of heuristic algorithms, for which we propose three illustrative instances with the objective of keeping the application delay small while achieving a good system utilization, in terms of high entanglement rate and fidelity of remotely entangled qubits. The system proposed is evaluated extensively via event-driven quantum network simulations, with noisy repeaters, in different node topologies under a Poisson arrival of requests from quantum applications. The results show the existence of a fundamental tradeoff between system- and application-level metrics, such as fairness versus entanglement and fidelity, which lays the foundations for further studies in this thriving research area.

Real-Time Self-Dispatch of a Remote Wind-Storage Integrated Power Plant Without Predictions: Explicit Policy and Performance Guarantee

This paper investigates real-time self-dispatch of a remote wind-storage integrated power plant connecting to the main grid via a transmission line with a limited capacity. Because prediction is a complicated task and inevitably incurs errors, it is a better choice to make real-time decisions based on the information observed in the current time slot without predictions on the uncertain electricity price and wind generation in the future. To this end, the operation problem is formulated under the Lyapunov optimization framework to maximize the long-term time-average revenue of the wind-storage plant. Inter-temporal storage dynamics are represented by a virtual queue which is mean rate stable. An online method for real-time dispatch is proposed based on Lyapunov drift algorithm via a drift-minus-revenue function. The upper bound of such a function, which does not depend on future uncertainty, is minimized in each time slot. Explicit dispatch policies are obtained through multi-parametric programming technique so that no optimization problem is solved online. It is proved that the online algorithm can maintain all the constraints across the entire horizon and the expected optimality gap compared to the deterministic offline optimum with perfect uncertainty information is inversely proportional to the weight coefficient in the drift-minus-revenue function. Numerical tests using real wind and electricity price data validate the effectiveness and performance of the proposed method.

Voltage Control for a Polymer Electrolyte Fuel Cell System By Model Predictive Control Controller

Due to its high power density, fast start up ability, low operating temperature, noiseless and environmental friendliness, polymer electrolyte fuel cells (PEFCs) have been regarded as the most promising alternative power sources for a variety of applications, such as transportation system, domestic system, stationary system and so on. Stable and effective control strategy is essential for the efficient and safe operation of PEFC systems. Voltage control is always a research hotspot in PEFCs’ control study. When using a PEFC system to supply power in a real application, it is of great significance to maintain a stable output voltage to avoid an adverse voltage drop under sudden load change, which can reduce the maintenance costs of the PEFC generation system. The output voltage of the PEFC system can be easily affected by the inlet flow rate of the reactant gas, i.e., the reactions inside the fuel cell system will be more sufficient with higher input flow rate of the reactant gas to produce higher electrical voltage, otherwise, less electrical voltage is generated. Therefore, the focus of this study is to control the output voltage of a PEFC system by regulating its hydrogen and air flow rates, which stands for a multi input and single output control problem.Model predictive control (MPC) is an advanced method of process control with stronger robustness and faster response compared with conventional controllers. Its main advantage is that it allows the current timeslot to be optimized while keeping future timeslots in account. Moreover, the MPC is rather suitable for solving systems with constraints, which makes a good choice for the voltage control of the PEFC system since the fuel cell system is a complicated nonlinear and strongly coupled system, with many constraints that need to be considered to ensure the coordination and balance between every subsystem.The first part of this study is to develop a PEFC system dynamic model, which is based on the NedStack PS6 of 6kW rated power. Its polarization behavior based on different reactant flowrates are evaluated. Then based on the developed model, a novel MPC controller is designed to control the PEFC system’s output voltage at 48V, assuming that the PEFC system can be an alternative power source for an electric bicycle with 48 V by regulating the input hydrogen and air flow rates. The following figure gives an overview of the voltage control of the PEFC system. It can be seen the input of the MPC controller contains the reference stack voltage, the state vector, the actual stack voltage and the current. It should be noted that the current is the our test signal. Figure 1

Integration of a Multi-Stack Fuel Cell System in Microgrids: A Solution Based on Model Predictive Control

This paper proposes a multi-objective model predictive control (MPC) designed for the power management of a multi-stack fuel cell (FC) system integrated into a renewable sources-based microgrid. The main advantage of MPC is the fact that it allows the current timeslot to be optimized while taking future timeslots into account. The multi-objective function solves the problem related to the power dispatch at time that includes criteria to reduce the multi-stack FC degradation, operating and maintenance costs, as well as hydrogen consumption. Regarding the scientific literature, the novelty of this paper lies in the proposal of a generalized MPC controller for a multi-stack FC that can be used independently of the number of stacks that make it up. Although all the stacks that make up the modular FC system are identical, their levels of degradation, in general, will not be. Thus, over time, each stack can present a different behavior. Therefore, the power control strategy cannot be based on an equal distribution according to the nominal power of each stack. On the contrary, the control algorithm should take advantage of the characteristics of the multi-stack FC concept, distributing operation across all the stacks regarding their capacity to produce power/energy, and optimizing the overall performance.