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Wildfire risk prediction is a critical component of disaster prevention and mitigation, often closely associated with local human activities in most regions. Recent studies demonstrate that employing joint modeling techniques using diverse datasets alongside Convolutional Neural Networks-Long Short-Term Memory Networks (ConvLSTM) produces favorable predictive results. However, previous research inadequately explored the different impact of factors across different categories and spatial orientations, and neglected the impact of fuels and human activities inside the samples. This study focuses on the six eastern provinces of China, utilizing a multi-source dataset comprising satellite-monitored wildfire products from 2012 to 2022, along with various factors indicating terrestrial and human activities, simulated meteorological elements and high-resolution vegetation imagery. By introducing channel and spatial attention mechanisms and visual transformer mode, this research optimizes the ConvLSTM wildfire prediction model. Results indicate a noteworthy enhancement, elevating accuracy, Kappa coefficient, and AUC of ROC curves from 91.15%, 80.87%, and 97.01% to 92.79%, 84.48%, and 97.90%, respectively. Consequently, it reinforces the accuracy by increase of the structural features within samples and quantifying the differences in the importance of different factors, which is also validated by prediction application of the samples in the entire year of 2023. Sensitivity analysis reveals that the current model is still highly dependent on the meteorological factors. Notably, the impact of structural features significantly surpasses the influence of terrain and terrestrial ecology elements, which should be considered in further models. Thus, this study has developed a methodology integrating multiple attention mechanisms and sample structural features, which could furnish high-precision daily kilometer-level wildfire risk prediction products. This method could improve the efficiency of prevention and control by improving the accuracy and narrowing the high-risk areas.

In order to solve the problems of low transmission performance, inaccurate flip set construction and high complexity of existing polar codes successive cancellation list bit-flip (SCLF) algorithms, a SCLF algorithm for polar codes based on GRU network assistance and path metric exchange was proposed.First the decoding state was redivided, and a bit-flip strategy was proposed by combining the ideas of path metric exchange and reverse judgment.Secondly, a flip set construction method was proposed by training the GRU network to locate the first erroneous bit in the decoding.Finally, a multi-bit flipping rule was proposed by sorting the path metric for each low-order flip and fixing the low-order reliable bits before performing the high-order flip.Simulation results show that compared with the existing SCLF algorithms, the proposed algorithm improves the accuracy of identifying the first error bit by 18～24% at low signal noise ratio.Under single-bit and multi-bit flipping, the proposed algorithm has a performance gain of up to 0.3 dB and 0.2 dB, respectively, and the online decoding complexity is lower.

The precipitation characteristics in Hangzhou of Zhejiang Province, China under the background of global climate change are analyzed using the meteorological observation data obtained from the Hangzhou base station in this study. We investigate the climate characteristics of precipitation in Hangzhou from several aspects, such as centennial trend, seasonal change, periodicity and the variation of extreme precipitation. Our results show a linear decreasing trend and obvious interdecadal characteristics in the precipitation of Hangzhou on a centennial timescale. Significantly increased amplitude of precipitation fluctuation was observed since the beginning of the 21st century. For the interdecadal variation of seasonal precipitation on a centennial timescale, precipitation in autumn showed a decreasing trend of 8.1 mm/10a, whereas the trends for the other three seasons were statistically insignificant. The precipitation in Hangzhou showed a decreasing trend in spring and an increasing trend in winter over the past 30 years. Our analyses reveal distinct precipitation cycles, including a quasi-30-year cycle since the 1960s and a quasi-10-year cycle since the 1980s. However, the periodicity has weakened in the past 10 years. In addition, the occurrence of torrential rain has increased rapidly in the past 10 years. Furthermore, influenced by global climate change and regional processes, the variation of extreme precipitation in Hangzhou has changed, which shows strong correlations with the overall trend of annual precipitation. The annual maximum daily precipitation in Hangzhou was mainly in the range of 0–40 mm from 1951 to 1980 and in the range of 40–80 mm from 1981 to 2010 with the maximum daily precipitation occurrence rate of 4.7 times/10a and 6.3 times/10a, respectively. This study emphasizes the risk of urban waterlogging caused by short-term heavy rainfall and provides useful reference to the assessment of extreme meteorological and hydrological disaster risk in Hangzhou.

In order to better implement the water environmental management policies, water quality evaluation is the basic step, that is to reasonably divide it into specific water quality category according to multiple water quality parameters in a certain water area.Aimed at this problem, an improved Naive Bayes classification method was proposed, which endowed different attributes with different weights, weakened the assumption of Naive Bayes conditional independence, and made the classification result closer to the actual category.Firstly, referred to the data released by the national surface water quality automatic monitoring station, 500 water quality data were selected as samples, and an evaluation system with four indicators was established, including dissolved oxygen, permanganate index, ammonia nitrogen and total phosphorus.And then, the improved Naive Bayes classification method was used to learn and evaluate the samples, and its classification performance by the five fold cross validation method was verified.The results show that the accuracy, precision, recall and F1 value of the improved Naive Bayes classification method reach 96.0%, 95.9%, 93.8% and 94.8% respectively, with higher performance index of water quality data classification compared with other Naive Bayes classification method, which can provide some reference for the problem of water quality data classification encountered in actual engineering.

In order to improve the quality and planting efficiency of barley seedlings in the growth chamber, the Kalman filter algorithm was firstly used to process the data collected by the sensor, which effectively reduced the influence of environmental factors and the error of the sensor itself, improved the accuracy of the collected data, and ensured the precise control in the growth chamber and accurate test data.Then multiple nonlinear regression, radial basis function and multilayer perceptron neural network were used to analyze the average growth height, seedling weight and seed weight of barley seeds about 160 hours after germination under different conditions.The drying ratio was analyzed and compared.The results show that the multi-layer perceptron network model fits the data best.Using this model to predict the average height of barley seedlings and the ratio of seedling weight of barley seedlings in the optimal environment is basically consistent with the actual planting effect, which provides a certain reference for the planting of barley seedlings in the growth chamber.

As one of the emerging technologies of low-power wide-area Internet of things, narrow band Internet of things (NB-IoT) faces the pressure and challenge of massive connections of terminals.Aiming at practical application scenarios, a preamble allocation scheme based on Beta distribution was proposed.In this scheme, the energy consumption of each time slot device was firstly calculated.The equipment was divided into high energy consumption according to two grouping methods: average energy consumption and cumulative energy consumption.The preamble was assigned to the high-energy-consuming device group first, and the remaining preamble and the conflicting preamble of the high-energy-consuming device group were assigned to the low-energy-consuming device group.The simulation results show that compared with the traditional random allocation scheme, the proposed scheme can effectively improve the average access success rate and average throughput of the NB-IoT system, and can effectively reduce the back off time.

With the increasing demand for tooth bleaching in esthetic dentistry, its safety has been the focus of a comprehensive body of literature. In this context, the aim of the present study was to evaluate the application effects of pentalysine β-carbonylphthalocyanine zinc (ZnPc(Lys)5)-mediated photodynamic therapy in dentin bleaching and its effects on dentin collagen. We first established a new and reproducible tooth staining model using dentin blocks stained by Orange II and then bleached with ZnPc(Lys)5 (25 μM) and hydrogen peroxide (10% or 30%). Data were analyzed with one- and two-way ANOVA and a significance level of p < 0.05. ZnPc(Lys)5 effectively bleached the dentin samples to an extent comparable to hydrogen peroxide at either 10% or 30% concentrations. Further studies on the dentin morphology, chemical element distribution, and protein constituents, using an electron microscope, energy dispersive spectroscopy, X-ray photoelectron spectroscopy, and SDS-PAGE, demonstrated that treatment with the photosensitizer preserved the dentin structure and, at the same time, the major organic component, collagen type I. For comparison, hydrogen peroxide (10% or 30%) treatment significantly degraded the collagen protein. This work indicated that the photosensitizer exerts potent bleaching effects on dentin staining; importantly, does not damage dentin and its collagen content; and opens up a new strategy to further explore various photosensitizers for the bleaching of both tooth enamel and dentin.

In order to improve the performance of digital modulation recognition under high dynamic signal-to-noise ratio, a joint modulation recognition method based on high-order cumulant and discriminative restricted Boltzmann machine was proposed, which extracted the high-order cumulant of digital signals as signal features, comprehensively utilized the generation ability and classification ability of the discriminative restricted Boltzmann machine, analyzed the recognition rate of digital signals in environments containing Gaussian noise, time-varying phase offset or Rayleigh fading.Experimental results show that compared with traditional classification methods, the recognition performance of the proposed method is obviously improved.In addition, the use of the model’s generation ability to reconstruct the input features can effectively improve the signal recognition rate under low signal-to-noise ratio.

Herein, we investigate the system spectral efficiency (SSE) and system energy efficiency (SEE) for massive two-way multiple-input-multiple-output (MIMO) relaying networks while considering imperfect channel state information (CSI) with $K$ -pair users in the presence of the hybrid relaying processing. First, we obtain the general expression of the signal-to-interference-and-noise-ratio (SINR) for the considered system when the antenna number of the relay station (RS) $M$ tends to be significantly large. Next, we consider the power scaling scheme (PSS) and achieve asymptotic SSE and SEE. We set the pilot sequence transmit power $P_{P}$ to be fixed $P_{P}$ and variable $P_{P}$ . For a nonvanishing SINR, we achieve different power scaling cases under fixed and variable $P_{P}$ . For a fixed $P_{P}$ value, the optimal PSS of the SSE can be obtained when the transmit power of each user can be scaled by $M$ , whereas that of the SEE can be achieved when the transmit power of both the RS and each user terminal can be reduced by $M$ . Based on the variable value of $P_{P}$ , the optimal PSS’ of both the SSE and the SEE can be used when the transmit power of both RS and each user can be scaled by $M^{1/3}$ . Thus, we conclude that the required SSE and SEE performances can be attained in multi-pair massive MIMO two-way relaying networks with a considerably low transmit power at RS and/or the transmit power of each user. The analytical results for the same are validated via simulation.

In vehicular edge computing (VEC), some tasks can be processed either locally or on the mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact, tasks are offloaded or not, based on the status of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. In this paper, device-to-device (D2D)-based V2V communication and multiple-input multiple-output and nonorthogonal multiple access (MIMO-NOMA)-based V2I communication are considered. In actual communication scenarios, the channel conditions for MIMO-NOMA-based V2I communication are uncertain, and the task arrival is random, leading to a highly complex environment for VEC systems. To solve this problem, we propose a power allocation scheme based on decentralized deep reinforcement learning (DRL). Since the action space is continuous, we employ the deep deterministic policy gradient (DDPG) algorithm to obtain the optimal policy. Extensive experiments demonstrate that our proposed approach with DRL and DDPG outperforms existing greedy strategies in terms of power consumption and reward.

The oceanic absorption of atmospheric CO _2 acidifies seawater, which accelerates CaCO _3 dissolution of calcifying organisms, a process termed dissolution effect. Promoted CaCO _3 dissolution increases seawater ALK (alkalinity), enhancing ocean carbon sink and mitigating ocean acidification. We incorporate different parameterizations of the link between CaCO _3 dissolution and ocean acidification into an Earth System Model, to quantify the feedback of the dissolution effect on the global carbon cycle. Under SRES A2 CO _2 emission scenario and its extension with emissions of 5,000 PgC in ∼400 years, in the absence of the dissolution effect, accumulated ocean CO _2 uptake between year 1800 and 3500 is 2,041 PgC. The consideration of the dissolution effect increases ocean carbon sink by 195–858 PgC (10%–42%), and mitigates the decrease in surface pH by 0.04–0.17 (a decrease of 10%–48% in [H ^+ ] (hydrogen ion concentration)), depending on the prescribed parameterization scheme. In the epipelagic zone, relative to the Arc-Atlantic Ocean, the Pacific-Indian Ocean experiences greater acidification, leading to greater dissolution effects and the resultant stronger feedbacks on ocean carbon sink and acidification in the Pacific-Indian Ocean. Noteworthy, the feedback of dissolution effect on ocean carbon sink can be comparable with or stronger than the feedback from CO _2 -induced radiative warming. Our study highlights the potentially critical role played by CaCO _3 dissolution effect in the ocean carbon sink, global carbon cycle and climate system.

Vehicular fog and cloud computing (VFCC) system, which provides huge computing power for processing numerous computation-intensive and delay sensitive tasks, is envisioned as an enabler for intelligent connected vehicles (ICVs). Although previous works have studied the optimal offloading scheme in the VFCC system, no existing work has considered the departure of vehicles that are processing tasks, i.e., the occupied vehicles. However, vehicles leaving the system with uncompleted tasks will affect the overall performance of the system. To solve the problem, in this paper, we study the optimal offloading scheme that considers the departure of occupied vehicles. We first formulate the task offloading problem as an semi-Markov decision process (SMDP). Then we design the value iteration algorithm for the SMDP to maximize the total long-term reward of the VFCC system. Finally, the numerical results demenstrate that the proposed offloading scheme can achieve higher system reward than the greedy scheme.

Abstract Many different encoding algorithms for systematic polar codes (SPC) have been introduced since SPC was proposed in 2011. However, the number of the computing units of exclusive OR (XOR) has not been optimized yet. According to an iterative property of the generator matrix and particular lower triangular structure of the matrix, we propose an optimized encoding algorithm (OEA) of SPC that can reduce the number of XOR computing units compared with existing non-recursive algorithms. We also prove that this property of the generator matrix could extend to different code lengths and rates of the polar codes. Through the matrix segmentation and transformation, we obtain a submatrix with all zero elements to save computation resources. The proportion of zero elements in the matrix can reach up to 58.5% from the OEA for SPC when the code length and code rate are 2048 and 0.5, respectively. Furthermore, the proposed OEA is beneficial to hardware implementation compared with the existing recursive algorithms in which signals are transmitted bidirectionally.

As naturally derived material, cellulose aerogels have excellent thermal insulation properties due to their unique high porosity and three-dimensional mesoporous structure. However, its hydrophilic properties limit its application in the field of building insulation. Here, we propose a method to prepare high hydrophobicity by adopting the sol-gel method and chemical vapor reaction strategy using cellulose acetate type II as raw material and 2,4-toluene diisocyanate as the cross-linking agent. Thermal properties of cellulose acetate aerogels (CAAs) were measured, where pyridine was the catalyst, acetone was the solvent, and perfluorodecyltriethoxysilane (PFDS), hexamethyldisilazane (HMDS), and methyltriethoxysilane (MTES) were used as hydrophobic agents (by process hydrophobic test). Compared with MTES-modified cellulose acetate aerogels (M-CAAs) and HMDS (H-CAAs)-modified cellulose acetate aerogels, PFDS-modified (P-CAAs) cellulose acetate aerogels are the most hydrophobic. By implementing hydrophobic modification of PFDS both inside and outside the structure of cellulose acetate aerogels, the water contact angle can reach up to 136°, strongly demonstrating the potential of PFDS as a hydrophobic agent. The results show that the thermal conductivity and compressive strength of cellulose acetate aerogel with the best hydrophobic properties are 0.035 W m−1 K−1 at normal pressure and 0.39 MPa at 3% strain, respectively. This work shows that the highly hydrophobic cellulose acetate aerogel has potential as a waterproof material in the field of building thermal-insulation materials.

Understanding the response of tropospheric ozone (O3) to nitrogen dioxide (NO2) change is important for local O3 control. The relationship between O3 and NO2 at county scale in China has been extensively studied using models, but there is a lack of results from direct measurements. In this study, we used measurements of O3, NO2 and meteorological conditions from a dense network in the Yangtze River Delta (YRD), and satellite observed formaldehyde (HCHO) and NO2 column densities for the analysis of O3 variabilities and its relationship to NO2. As a result, severe O3 pollution occurred mainly in Shanghai city, southern Jiangsu and northern Zhejiang provinces in YRD during April–September. In addition, meteorological conditions could explain 54% the diurnal O3 variation over YRD. During April–September 2015–2021, O3 showed a significant positive relationship (r = 0.61 ± 0.10) with NO2 after removing the impact from meteorological conditions. However, the relationship could be reversed with NO2 concentration change. Our result suggested that the controllable O3 related to NO2 change is up to 100 μg·m−3 in megacities over Shanghai and northern Zhejiang province. The O3 is much more sensitive to the NO2 reduction in megacities than surrounding areas. Our results evaluate the different impacts of NO2 changes on O3 formation, which provides explanation for the simultaneously alleviated O3 pollution and reduced NO2 in 2020 in Shanghai and northern Zhejiang, as well as the increased O3 in most counties before 2019 with reduced NO2 during October–March. The driving mechanism as revealed from this study for O3 and NO2 will be valuable for the O3 abatement through NO2 reduction at sub-county scale over YRD in China.

In the context of global warming, how to measure summer climate suitability at a local scale is important for meteorological services. Considering meteorological and ecological conditions, body comfort, and the atmospheric environment, an assessment method for summer climate suitability for Zhejiang Province is proposed. In this paper, a summer suitable index (SSI) for Zhejiang is calculated, including four secondary indices: a summer cool index (SCI), a comfort days index (CDI), a good air days index (GADI) and a vegetation cover index (VCI). Using a local evaluation criterion, summer climate suitable areas are distinguished objectively according to the SSI. The results show that especially suitable regions account for 4.97% of Zhejiang Province, very suitable regions account for 22.2%, suitable regions account for 39.58%, and general regions account for 33.25%. The summer climate suitable areas are located mainly in high mountains and hills and coastal island areas while plain areas cannot be considered a suitable destination for summer tourism. By comparison and discussion, the SSI is demonstrated to capture summer climate suitability well. In contrast to a fixed evaluation index, benchmark values obtained for the SSI depend on the local climate and the index is straightforward to apply.

Dimming control is critical in practical visible light communication (VLC) systems to satisfy illumination demand. Upon exploiting multiple layers of asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) signals for simultaneous transmission, layered ACO-OFDM (LACO-OFDM) constitutes a promising spectral-efficient modulation technique for VLC. However, dimming by integrating the industry-preferred pulsewidth modulation (PWM) to the LACO-OFDM scheme induces severe clipping noise due to the nonlinearity of LEDs. In this paper, we conceive a reconstructed LACO-OFDM (RLACO-OFDM) scheme incorporating the PWM technique for VLC to accommodate the dimming demands for indoor illumination, while maintaining a spectral-efficient communication link. Moreover, superior to its counterparts, the transmitted symbols in the RLACO-OFDM based VLC system can be readily recovered by a standard LACO-OFDM receiver while no additional detection of the PWM signal is required, which reduces the receiver complexity. Numerical results demonstrate that the proposed RLACO-OFDM is capable of realizing high spectral efficiency over a broad dimming range.

Chitosan (CTS) aerogel is a new type of functional material that could be possibly applied in the thermal insulation field, especially in energy-saving buildings. However, the inhibition method for the very big shrinkage of CTS aerogels from the final gel to the aerogel is challenging, causing great difficulty in achieving a near-net shape of CTS aerogels. Here, this study explored a facile strategy for restraining CTS-based aerogels’ inherent shrinkage depending on the chemical crosslinking and the interpenetrated supramolecular interaction by introducing nanofibrillar cellulose (NFC) and polyvinyl alcohol (PVA) chains. The effects of different aspect ratios of NFC on the CTS-based aerogels were systematically analyzed. The results showed that the optimal aspect ratio for NFC introduction was 37.5 from the comprehensive property perspective. CTS/PVA/NFC hybrid aerogels with the aspect ratio of 37.5 for NFC gained a superior thermal conductivity of 0.0224 W/m• K at ambient atmosphere (the cold surface temperature was only 33.46 °C, despite contacting the hot surface of 80.46 °C), a low density of 0.09 g/cm3, and a relatively high compressive stress of 0.51 MPa at 10% strain.

Abstract As a large number of small base stations have been deployed in dense heterogeneous networks, the increasing cost of energy has become a challenging issue. In this paper, we propose an optimized clustering-based sleep strategy to release the power consumption and interference in the system. First, we group the small base stations with large interference as a cluster in which the binary particle swarm optimization (BPSO) algorithm is used to formulate a sleep strategy for small base stations in the clusters. Since the small base stations between clusters have less interference, the whole spectrum resources can be shared in all clusters and the orthogonal spectrum resources are allocated for the small base stations in the same cluster. The strategy effectively restrains the co-tier interference in the system. Furthermore, a separate sleep strategy is applied for each cluster to improve the sleep and activation efficiency of the small base stations. The simulation results show that the proposed sleep strategy can improve the satisfaction of user equipment and reduce the power consumption. As the increasing number of user equipment, the outage probability of the system can be reduced effectively.

Cellulose and its derivative aerogels have attracted much attention due to their renewable and biodegradable properties. However, the significant shrinkage in the supercritical drying process causes the relatively high thermal conductivity and low mechanical property of cellulose and its derivatives aerogels. Considering the pearl-necklace-like skeleton network of silica aerogels, which can improve thermal insulation property and mechanical property. Herein, we propose a new strategy for fabricating cellulose diacetate aerogels (CDAAs) with pearl-necklace-like skeletons by using tert-butanol (TBA) as exchange solvent after experiencing the freezing-drying course. CDAAs obtained have the low density of 0.09 g cm−3, the nanopore size in the range of 10–40 nm, the low thermal conductivity of 0.024 W m−1 K−1 at ambient conditions, and the excellent mechanical properties (0.18 MPa at 3% strain, 0.38 MPa at 5% strain). Ultimately, CDAAs with moderate mechanical property paralleled to cellulose-derived aerogels obtained from supercritical drying process are produced, only simultaneously owning the radial shrinkage of 6.2%. The facile method for fabricating CDAAs could provide a new reference for constructing cellulose/cellulose-derived aerogels and other biomass aerogels.

The central limit theorem (CLT)-based Gaussian approximation facilitates the probability analysis of cognitive radio (CR), but it incurs data rate loss due to the inaccurate configuration of the optimal sensing duration (OSD). To estimate the data rate loss, an approach to obtain a tight upper bound of the maximal achievable data rate over a Nakagami-m fading via-to-sense channel is proposed. Moreover, a direct formula is more desirable than an iteration scheme for a highly mobile CR that has to calculate the OSD more quickly. To meet this need, a strategy to achieve the OSD based on exponential interpolation is proposed. For Nakagami-Gamma shadowed fading via-to-sense channels, the proposed scheme is demonstrated to have advantages over the popularly utilized Hermite spline, such as: 1) a more tractable derivation of the probability density function (PDF) of the OSD and 2) a less complex calculation of the ergodicsensing capacity. These advantages are significantly favorable for a CR network to achieve low latency communication and swiftly perform quality of service management to adapt to the high mobility.

This paper assesses the performances of a downlink cooperative non-orthogonal multiple access (C-NOMA) network over Nakagami-m fading channels. The analytical outage probability expression for each user is derived and verified by Monte Carlo simulations. Based upon the outage probability, the maximal throughputs are obtained and analyzed under different channel conditions. Furthermore, the effects of channel fading on the probability and throughput are investigated. The results indicate that the performance of the C-NOMA system is excellent over the source-relay link with less fading. This analysis can be used for designing the C-NOMA systems in the future fifth-generation wireless communication.

Linear precoding algorithms with low complexity in massive multi-in multi-out system have always been a hot research topic to solve the problem of inter-cell interference. In this paper, we proposed a conjugate gradient-based regularized zero-forcing (CG-RZF) precoding algorithm, with which the base station can directly obtain the transmitted signal after RZF precoding and avoid directly solving the inverse matrix in RZF. Moreover, an RZF precoding algorithm based on a parallel conjugate gradient (Parallel-CGRZF) is also proposed, which can optimize initial values and iterative process of the aforementioned CG-RZF precoding algorithm. The simulation results have shown that the proposed CG-RZF and the optimized Parallel-CG-RZF precoding algorithm can significantly improve the performance of bit error rate with fast convergence speed compared with other precoding algorithms and can reduce the number of global communications. Meanwhile, the calculation complexity of the proposed CG-RZF and Parallel-CG-RZF precoding algorithm is much lower than the optimized Chebyshev iteration algorithm.

Abstract This paper evaluates the performance of the vehicle-to-infrastructure (V2I) fair access with a finite retry limit through constructing an analytical model. Firstly, the process of the distributed coordination function (DCF) mechanism with a finite retry limit is described through developing a 2-D Markov chain; based on the Markov chain, the transmission probability with the access parameters including the minimum window and the retry limit is derived. Then, considering the V2I unfair access problem, the relationship between the velocity and the transmission probability of a vehicle is derived. Based on the proposed model, given a finite retry limit, the minimum window of a vehicle can be determined according to its velocity. In the simulation, the proposed model is justified to be effective and the V2I fair access performance with a finite retry limit is evaluated through comparison.

Conventional optical orthogonal frequency division multiplexing (OFDM) schemes, such as adaptively biased optical OFDM (ABO-OFDM) and hybrid asymmetrically clipped optical OFDM (HACO-OFDM), are unable to tap all the resources of the subcarriers and only achieve relatively high power efficiency. In this paper, a hybrid adaptive bias optical OFDM (HABO-OFDM) scheme for visible light communication (VLC) is proposed to improve spectral efficiency and power efficiency. In the proposed HABO-OFDM scheme, different optical OFDM components are combined for transmission at the same time, and the adaptive bias is designed to ensure the non-negativity, as well as obtaining significantly high power efficiency. Meanwhile, the implementation complexity of the HABO-OFDM receiver is notably lower than the conventional superimposed optical OFDM schemes. Simulation results show that the proposed HABO-OFDM scheme outperforms ABO-OFDM and HACO-OFDM in terms of both peak-to-average-power ratio (PAPR) and power efficiency. The PAPR performance of HABO-OFDM is about 3.2 dB lower than that of HACO-OFDM and 1.7 dB lower than that of ABO-OFDM. Moreover, we can see that the Eb(elec)/N0 required for HABO-OFDM to reach the BER target is lower than the other two schemes at the Bit rate/Normalized bandwidth range of 3.5 to 8.75, which means that the power efficiency of HABO-OFDM is higher in this range.

Precoding algorithm is used to transmit signals effectively and to reduce the interferences from other user terminals in the massive multiple-input-multiple-output (MIMO) systems. In order to decrease the computational complexity of the precoding matrix, this paper proposes a new precoding algorithm. We use Chebyshev iteration to estimate the matrix inversion in the regularized zero-forcing precoding (RZF) algorithm. It does not need to compute the matrix inversion directly but uses iterations to estimate the matrix inversion. Therefore, the computational complexity can be decreased in this way. Furthermore, Chebyshev iteration has lower convergence rate, and it can gain precoding matrix quickly. This paper analyzes the performance of the Chebyshev-RZF precoding algorithm using average achievable rate and computes the complexity of the algorithm. Then, this paper optimizes initial values of the Chebyshev iteration algorithm on the basis of the feature of massive MIMO systems and makes initial values easier to be obtained. Simulation results show that after two iterations, the Chebyshev-RZF precoding algorithm can get similar average achievable rate as the RZF precoding algorithm does. An optimized Chebyshev-RZF precoding algorithm gets similar performance to the Chebyshev-RZF precoding algorithm after one iteration.

In order to reduce the complexity of the massive MIMO channel estimation, a channel estimation method based on Kapteyn series expansion was proposed. By using Taylor series to expand the inversion, the complexity was greatly reduced. In order to improve the Taylor-MMSE estimation of the convergence rate, Kapteyn series was used to expand covariance matrix. The simulation result shows that the mean square error of Kapteyn-MMSE estimation converges faster than Taylor-MSME. It is insufficient that the complexity of Kapteyn-MMSE slightly higher than the complexity of Taylor-MMSE, but still far less than the complexity of classical MMSE algorithm.

In order to maximize energy efficiency in heterogeneous networks (HetNets), a turbo Q-Learning (TQL) combined with multistage decision process and tabular Q-Learning is proposed to optimize the resource configuration. For the large dimensions of action space, the problem of energy efficiency optimization is designed as a multistage decision process in this paper, according to the resource allocation of optimization objectives, the initial problem is divided into several subproblems which are solved by tabular Q-Learning, and the traditional exponential increasing size of action space is decomposed into linear increase. By iterating the solutions of subproblems, the initial problem is solved. The simple stability analysis of the algorithm is given in this paper. As to the large dimension of state space, we use a deep neural network (DNN) to classify states where the optimization policy of novel Q-Learning is set to label samples. Thus far, the dimensions of action and state space have been solved. The simulation results show that our approach is convergent, improves the convergence speed by 60% while maintaining almost the same energy efficiency and having the characteristics of system adjustment.

Massive multiple-input-multiple-output (MIMO) is one of the key technologies in the fifth generation (5G) cellular communication systems. For uplink massive MIMO systems, the typical linear detection such as minimum mean square error (MMSE) presents a near-optimal performance. Due to the required direct matrix inverse, however, the MMSE detection algorithm becomes computationally very expensive, especially when the number of users is large. For achieving the high detection accuracy as well as reducing the computational complexity in massive MIMO systems, we propose an improved Jacobi iterative algorithm by accelerating the convergence rate in the signal detection process.Specifically, the steepest descent (SD) method is utilized to achieve an efficient searching direction. Then, the whole-correction method is applied to update the iterative process. As the result, the fast convergence and the low computationally complexity of the proposed Jacobi-based algorithm are obtained and proved. Simulation results also demonstrate that the proposed algorithm performs better than the conventional algorithms in terms of the bit error rate (BER) and achieves a near-optimal detection accuracy as the typical MMSE detector, but utilizing a small number of iterations.

In order to reduce the computational complexity of the inverse matrix in the regularized zero-forcing (RZF) precoding algorithm, this paper expands and approximates the inverse matrix based on the truncated Kapteyn series expansion and the corresponding low-complexity RZF precoding algorithm is obtained. In addition, the expansion coefficients of the truncated Kapteyn series in our proposed algorithm are optimized, leading to further improvement of the convergence speed of the precoding algorithm under the premise of the same computational complexity as the traditional RZF precoding. Moreover, the computational complexity and the downlink channel performance in terms of the average achievable rate of the proposed RZF precoding algorithm and other RZF precoding algorithms with typical truncated series expansion approaches are analyzed, and further evaluated by numerical simulations in a large-scale single-cell multiple-input-multiple-output (MIMO) system. Simulation results show that the proposed improved RZF precoding algorithm based on the truncated Kapteyn series expansion performs better than other compared algorithms while keeping low computational complexity.

In multiuser visible light communication (VLC) systems, many transmit precoding (TPC) techniques have been investigated to suppress multiuser interference. However, these conventional works restrict their modulation to the special case of zero mean, which inherently limits their application to some popular modulations associated with the non-zero mean in VLC, such as pulse position modulation (PPM). Since the modulation with non-zero mean leads to more intricate optical power constraints and design objective functions than the case of zero mean, the TPC design that can support a general modulation is still an open problem. In the paper, we conceive of a general solution of the TPC scheme combined with dimming control for multiuser VLC systems, which is capable of mitigating multiuser interference, while at the same time, achieving the desired dimming level. The proposed scheme is applicable to a wide range of modulations in VLC, such as pulse amplitude modulation (PAM), PPM, and so on. Simulation results demonstrate that the proposed scheme outperforms the traditional pseudo-inverse-based zero-forcing TPC in terms of bit error rate (BER).

Platooning strategy is an important component of autonomous driving technology. Autonomous vehicles in platoons are often equipped with a variety of on-board sensors to detect the surrounding environment. The abundant data collected by autonomous vehicles in platoons can be transmitted to the infrastructure through vehicle-to-infrastructure (V2I) communications using the IEEE 802.11 distributed coordination function (DCF) mechanism and then uploaded to the cloud platform through the Internet. The cloud platform extracts useful information and then sends it back to the autonomous vehicles respectively. In this way, autonomous vehicles in platoons can detect emergency conditions and make a decision in time. The characteristics of platoons would cause a fair-access problem in the V2I communications, i.e., vehicles in the platoons moving on different lanes with different velocities would have different resident time within the infrastructure’s coverage and thus successfully send different amounts of data to the infrastructure. In this case, the vehicles with different velocities will receive different amounts of useful information from the cloud. As a result, vehicles with a higher velocity are more likely to suffer from a traffic accident as compared to the vehicles with a lower velocity. Hence, this paper considers the fair-access problem and proposes a fair-access scheme to ensure that vehicles with different velocities successfully transmit the same amount of data by adaptively adjusting the minimum contention window of each vehicle according to its velocity. Moreover, the normalized throughput of the proposed scheme is derived. The validity of the fair-access scheme is demonstrated by simulation.

Multi-platooning is an important management strategy for autonomous driving technology. The backbone vehicles in a multi-platoon adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit vehicles’ kinematics information through inter-platoon communications, and then forward the information to the member vehicles through intra-platoon communications. In this case, each vehicle in a multi-platoon can acquire the kinematics information of other vehicles. The parameters of DCF, the hidden terminal problem and the number of neighbors may incur a long and unbalanced one-hop delay of inter-platoon communications, which would further prolong end-to-end delay of inter-platoon communications. In this case, some vehicles within a multi-platoon cannot acquire the emergency changes of other vehicles’ kinematics within a limited time duration and take prompt action accordingly to keep a multi-platoon formation. Unlike other related works, this paper proposes a swarming approach to optimize the one-hop delay of inter-platoon communications in a multi-platoon scenario. Specifically, the minimum contention window size of each backbone vehicle is adjusted to enable the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay. The simulation results indicate that, the one-hop delay of the proposed approach is reduced by 12% as compared to the DCF mechanism with the IEEE standard contention window size. Moreover, the end-to-end delay, one-hop throughput, end-to-end throughput and transmission probability have been significantly improved.

This study investigates the superiority of cooperative broadcast transmission over traditional orthogonal schemes when applied in a downlink relaying broadcast channel (RBC). Two proposed cooperative broadcast transmission protocols, one with an amplify-and-forward (AF) relay, and the other with a repetition-based decode-and-forward (DF) relay, are investigated. By utilizing superposition coding (SupC), the source and the relay transmit the private user messages simultaneously instead of sequentially as in traditional orthogonal schemes, which means the channel resources are reused and an increased channel degree of freedom is available to each user, hence the half-duplex penalty of relaying is alleviated. To facilitate a performance evaluation, theoretical outage probability expressions of the two broadcast transmission schemes are developed, based on which, we investigate the minimum total power consumption of each scheme for a given traffic requirement by numerical simulation. The results provide details on the overall system performance and fruitful insights on the essential characteristics of cooperative broadcast transmission in RBCs. It is observed that better overall outage performances and considerable power gains can be obtained by utilizing cooperative broadcast transmissions compared to traditional orthogonal schemes.

Wind energy, as a vital renewable energy source, also plays a significant role in reducing carbon emissions and mitigating climate change. It is therefore of utmost necessity to evaluate ocean wind energy resources for electricity generation and environmental management. Ocean wind distribution around the globe can be obtained from satellite observations to compensate for limited in situ measurements. However, previous studies have largely ignored uncertainties in ocean wind energy resources assessment with multiple satellite data. It is against this background that the current study compares mean wind speeds (MWS) and wind power densities (WPD) retrieved from scatterometers (QuikSCAT, ASCAT) and radiometers (WindSAT) and their different combinations with National Data Buoy Center (NDBC) buoy measurements at heights of 10 m and 100 m (wind turbine hub height) above sea level. Our results show an improvement in the accuracy of wind resources estimation with the use of multiple satellite observations. This has implications for the acquisition of reliable data on ocean wind energy in support of management policies.

Distinguishing the predictable 10–30-day stable components (STs) in the actual atmosphere has been important in atmospheric science research. In this study, a new method for extracting 10–30-day STs was developed with the use of historical observations. We extracted and analyzed 10–30-day STs via statistical extrapolation tests. The results show that the STs are maintained uniformly at the intraseasonal time scale; the overall trends in the atmospheric motion are revealed. Comparisons between pentad-by-pentad changes in the explained variances of the 10–30-day STs under ENSO phases show that the explained variance transmission attenuation trends for El Niño and La Niña years are weaker and more continuous than those of neutral years. Data for 10–30-day STs can remain continuous and stable from one month to the next. The proposed method and results present a new means of extracting predictable STs from the atmosphere using historical data.