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Abstract Background Esophageal cancer is currently one of the high-risk malignant tumors worldwide, posing a serious threat to human health. This study aimed to analyse the causes of postoperative mortality and intrathoracic anastomotic leakage(IAL) after esophagectomy. Methods A retrospective analysis was conducted on 172 patients with esophageal cancer resection and focused on the preoperative and postoperative indicators. Cox regression analysis was performed to identify factors affected IAL and evaluated the potential factors on postoperative mortality. The Kaplan-Meier curve was applied to evaluate the effect of leakage on postoperative mortality after propensity score matching. Results Univariable and multivariable Cox regression analysis showed that infection and high BMI were significant risk factors for IAL, patients with BMI over 24 kg/m2 in IAL group was two times higher than that of the group without IAL (95% CI = 1.01–6.38; P = 0.048). When patients were infected, the hazard ratios(HRs) of anastomotic leakage was twice that of patients without infection (95% CI = 1.22–4.70; P = 0.011). On the other hand, IAL was a significant cause of postoperative mortality, the 40-day postoperative mortality rate in the leakage group was significantly higher than the non leakage group (28.95% in leakage group vs. 7.46% in non leakage group, P

Machine learning has started to be used in engine research to optimize combustion and predict fuel spray characteristics. This paper presents the development of a machine learning model using a Genetic Algorithm-Backpropagation (GA-BP) neural network to predict spray penetration. The GA-BP neural network was selected for its ability to optimize neural network weights and thresholds, thereby improving model convergence and avoiding local minima, which are common challenges in complex, non-linear problems such as spray prediction. The model was trained using experimental data from diesel injector spray tests, and its accuracy was evaluated through parametric sensitivity analysis, examining the influence of various input factors. A comparison between the machine learning model and the traditional empirical formulas of spray penetration revealed that the machine learning model achieved greater accuracy. In terms of the sensitivity to inputs, it is interesting to find that the cognition of machines is different from that of humans. When an input parameter does not have any functional relationship with other input parameters, the absence of this input parameter will lead to a significant decrease in the accuracy of the output result. The results demonstrate that the machine learning approach offers higher accuracy and better generalizability compared to traditional empirical methods. This study recommends the ways to get better results of penetration prediction with BP neural networks, which is efficient in training and utilizing Artificial Neural Networks (ANNs).

To deal with the spectrum demand of new low earth orbit satellite (LEO) communication component and the aggregate interference generated by incumbent terrestrial component in future integrated satellite-terrestrial system, a novel space-borne spectrum-sensing (SS) and spectrum-database assisted spectrum-sharing scheme with interweave model for LEO cognitive system was proposed.Firstly, considering the adverse impact of the sparse distribution of LEO cognitive node (CN) on SS and interference cartography (IC), the concept of cognitive node position was put forward.Secondly, the spectrum-DB was built based on dual-threshold hybrid SS and IC with LEO cognitive system.Finally, a spectrum-sharing scheme with multi-state virtual network (MSVN) was designed, which could make the coexistence between terrestrial component and uplink of LEO component available.Simulation results indicate that the spectrum-database approach based on the proposed dual-threshold hybrid SS and IC can support the spectrum awareness of high reliability with relatively low-density LEO CN, and that the proposed cooperative access scheme based on spectrum-database and interweave model for LEO component can share spectrum with terrestrial one, but not significantly increase the end-to-end delay of LEO component.

IntroductionSoil fungal communities play a crucial role in maintaining the ecological functions of alpine forest soil ecosystems. However, it is currently unclear how the distribution patterns of fungal communities in different soil layers of alpine forests will change along the elevational gradients.Material and methodsTherefore, Illumina MiSeq sequencing technology was employed to investigate fungal communities in three soil layers (0–10, 10–20, and 20–30 cm) along an elevational gradient (3500 m to 4300 m) at Sygera Mountains, located in Bayi District, Nyingchi City, Tibet.Results and discussionThe results indicated that: 1) Soil depth had a greater impact on fungal diversity than elevation, demonstrating a significant reduction in fungal diversity with increased soil depth but showing no significant difference with elevation changes in all soil layers. Within the 0–10 cm soil layer, both Basidiomycota and Ascomycota co-dominate the microbial community. However, as the soil depth increases to 10–20 and 20–30 cm soil layers, the Basidiomycota predominantly dominates. 2) Deterministic processes were dominant in the assembly mechanism of the 0–10 cm fungal community and remained unchanged with increasing elevation. By contrast, the assembly mechanisms of the 10–20 and 20–30 cm fungal communities shifted from deterministic to stochastic processes as elevation increased. 3) The network complexity of the 0–10 cm fungal community gradually increased with elevation, while that of the 10–20 and 20–30 cm fungal communities exhibited a decreasing trend. Compared to the 0–10 cm soil layer, more changes in the relative abundance of fungal biomarkers occurred in the 10–20 and 20–30 cm soil layers, indicating that the fungal communities at these depths are more sensitive to climate changes. Among the key factors driving these alterations, soil temperature and moisture soil water content stood out as pivotal in shaping the assembly mechanisms and network complexity of fungal communities. This study contributes to the understanding of soil fungal community patterns and drivers along elevational gradients in alpine ecosystems and provides important scientific evidence for predicting the functional responses of soil microbial ecosystems in alpine forests.

To solve the problem of the weak spectrum-cognitive ability caused by monitoring angle, direction resolution, limited processing ability and peak power for a low-earth-orbit (LEO) satellite, a multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning was proposed.Firstly, considering the available computing resource, cognitive performance, processing and transmission delay of each spectrum cognitive satellite, a cooperative-satellite selection and computing-resource allocation algorithm was built for multiple spectrum-cognitive tasks.Secondly, based on the selected satellites and the allocated computing resources, a low-complexity multi-satellite cooperative spectrum cognitive strategy was further designed, which could automatically sense the spectrum holes, and detect interference as well as identify the modulation mode.Simulation results demonstrate that compared to the single-node cognitive method, the designed multi-satellite cooperative spectrum cognitive strategy can obtain a better cognitive performance.Moreover, comparing with the existing model, the model utilized in the designed strategy can effectively achieve 96.69% and 93.32% lower number of parameters and required floating point operations per second, whilst maintaining the performance.

In the process of the vigorous development of satellite internet of things, random access protocol is regarded as the most suitable communication protocol due to its characteristics of low signaling overhead, high transmission efficiency, high flexibility and easy implementation.However, due to drastic load changes, conventional random access mode only has advantages in the specific load range.Dynamic allocation of random access resources requires the accurate number of time slot access applications.In view of the above problems, a random access adaptive scheme was proposed, which could adaptively selected the appropriate random access mode according to the future load size, so as to improved the system throughput.The load size could be obtained through the proposed load estimation and prediction scheme.When narrowband interference exists in the transmission environment, the spread-spectrum communication means was introduced to made the adaptive scheme of random access run successfully.Moreover, the proposed adaptive scheme also improved the system throughput.

The development of immunoprophylactic products against respiratory syncytial virus (RSV) has resulted in notable advancements, leading to an increased demand for preclinical experiments and placing greater demands on animal models. Nevertheless, the field of RSV research continues to face the challenge of a lack of ideal animal models. Despite the demonstration of efficacy in animal studies, numerous RSV vaccine candidates have been unsuccessful in clinical trials, primarily due to the lack of suitable animal models. The most commonly utilized animal models for RSV research are cotton rats, mice, lambs, and non-human primates. These animals have been extensively employed in mechanistic studies and in the development and evaluation of vaccines and therapeutics. However, each model only exemplifies some, but not all, aspects of human RSV disease. The aim of this study was to provide a comprehensive summary of the disease symptoms, viral replication, pathological damage, and enhanced RSV disease (ERD) conditions across different RSV animal models. Furthermore, the advantages and disadvantages of each model are discussed, with the intention of providing a valuable reference for related RSV research.

Alpine wetlands are an important source of methane (CH4) and play a key role in the global carbon cycle. Their CH4 emissions largely depend on microbial CH4 production and oxidation processes that involve external electron acceptors. Seasonal precipitation drives redox cycles of humic acids (HAs), iron oxide and sulfur species, which will in turn affect CH4 production and oxidation. To investigate the effects of electron acceptors on CH4 emissions, soil samples from a typical alpine wetland on the Tibetan Plateau were incubated with the addition of ferrihydrite (HFO), HAs, sodium sulfate (SO42-) or combinations (HAs-HFO, HAs-SO42- and HAs-HFO-SO42-). During long-term anaerobic incubation, CH4 concentrations showed similar trends, increasing rapidly from 0 to 60 days, decreasing from 60 to 240 days, and finally slowly increasing again after 240 days, in all treatments except the sterilised control. Thus, the incubation period was divided into the production, consumption and reproduction phases. The addition of HFO, HAs or HAs-containing electron acceptors promoted the rates of both production and consumption of CH4, increasing the production potential of CH4 by 65–100 % and the oxidation potential of CH4 by 58–115 %. On the other hand, SO42- inhibited the production and consumption of CH4, reducing the production potential by 35 % and the oxidation potential by 50 %. Electron acceptors such as HFO, HAs and SO42- play important roles in CH4 emissions. HAs are the dominant factor affecting CH4 emissions in alpine wetlands. From a broader ecological perspective, organic and inorganic electron acceptors play a key role in CH4 production and oxidation under anaerobic conditions, influencing CH4 emissions from alpine wetlands.

The integration of terrestrial communication and satellite communication for internet of things (IoT) is the inevitable trend of the development of wireless communication network, to achieve the vision of the connection of everything.As a supplement to terrestrial IoT, satellite IoT can make up the fl aws of terrestrial IoT.However, with the expansion of IoT scale, it will be diffi cult for satellites to meet the communication needs of increasing number of IoT terminals, because the problem of limited satellite load will become increasingly prominent.Multiple-input multiple-output (MIMO) technology is the key technology of terrestrial 5G, and also an important research direction of satellite communication in the future 6G.The beamforming(BF) technology was introduced in MIMO into the low earth orbit (LEO) satellite based IoT, to made full use of spatial resources and signifi cantly improved system capacity.In view of the implementation conditions of BF technology and the characteristics of satellite IoT, several related issues were discussed, included channel model, BF criterions, load balancing access technology of one satellite and cooperative BF access technology of several satellites.

With the development of science and technology and the continuous improvement of related technologies, low earth orbit (LEO) satellite communication has become a hot technology that countries all over the world are chasing.The advantages of orthogonal time frequency spacetechnology can well solve the signal transmission problem of broadband satellite ground link in high moving speed scenarios, which is conducive to the unifi cation of modulation waveforms between satellites and the ground, and the realization of space-based integrated communication.However, its transmission performance is aff ected by the accuracy of channel estimation, and the special modulation makes its channel estimation method diff erent from the traditional technology, so it needs to be studied.The existing mainstream OTFS channel estimation schemes are introduced, and the advantages and disadvantages of each scheme were analyzed, which laied a foundation for subsequent research.

Hand, foot, and mouth disease (HFMD) is a contagious viral infection predominantly affecting infants and young children, caused by multiple enteroviruses, including Enterovirus 71 (EV71), Coxsackievirus A16 (CA16), Coxsackievirus A10 (CA10), and Coxsackievirus A6 (CA6). The high pathogenicity of HFMD has garnered significant attention. Currently, there is no specific treatment or broad-spectrum preventive measure available for HFMD, and existing monovalent vaccines have limited impact on the overall incidence or prevalence of the disease. Consequently, with the emergence of new viral strains driven by vaccine pressure, there is an urgent need to develop strategies for the rapid response and control of new outbreaks. In this study, we demonstrated the broad protective effect of maternal antibodies against three types of HFMD by immunizing mother mice with a trivalent inactivated vaccine targeting EV71, CA16, and CA10, using a neonatal mouse challenge model. Based on the feasibility of maternal antibodies as a form of passive immunization to prevent HFMD, we prepared a multivalent antiviral milk by immunizing dairy cows with the trivalent inactivated vaccine to target multiple HFMD viruses. In the neonatal mouse challenge model, this immunized milk exhibited extensive passive protection against oral infections caused by the three HFMD viruses. Compared to vaccines, this strategy may offer a rapid and broadly applicable approach to providing passive immunity for the prevention of HFMD, particularly in response to the swift emergence and spread of new variants.

With the development of IoT technology and 5G massive machine-type communication, the 3GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks.

With the construction of spatial information network, the available spectrum resources are increasingly scarce, and the electromagnetic environment is becoming more and more complex, resulting a more prominent challenge of realizing the safe control of the global electromagnetic.Therefore, the sensing method of electromagnetic spectrum occupation state was studied, a time convolution neural network to sense the spectrum occupation state was constructed.To mine abnormal spectrum data, neural network was used to automatically detect abnormal spectrum, such as fl uctuation noise fl oor, spectrum grass and large bandwidth.The spectrum parameter extraction method was explored.Considering the large propagation delay between the spectrum-sensing satellite and its gateway, a neural network was constructed to predicted the spectrum occupation state.The perception, anomaly, parameters and prediction results of satellite spectrum signal were visualized.The preliminary evaluation results showed that the designed satellite-spectrum intelligent mining technologies could not only improved the sensing ability of spectrum situation, but also eff ectively improved the utilization of spectrum resources.

Mineral-associated organic matter (MAOM) is the largest soil organic carbon (OC) pool with the longest turnover. MAOM is expected to have relatively little sensitivity to climate change due to mineral protection, but its persistence involves several organo-mineral fractions. The uncertainty in the response of specific organo-mineral fractions to climate change hampers the reliability of predictions of MAOM preservation in the future. Here, we applied a sequential chemical fractionation method integrated with network analysis to investigate MAOM stabilization mechanisms across five alpine ecosystems: alpine desert, alpine steppe, alpine meadow, alpine wetland, and alpine forest. Hierarchical cluster analysis revealed grouping of seven extractable OM fractions in MAOM into three OM clusters: a cluster with weak bondings consisting of water-soluble OM (WSOM) and weakly adsorbed fractions (2.1–21.3% of total OC); a cluster with metal-bound complexes comprising Ca-OM complexes and Fe/Al-OM complexes (3.8–12.2% of total OC); and a cluster with strong bonding composed of Al oxyhydroxides, carbonates and Fe oxyhydroxides (12.2–33.5% of total OC). The relative percentages of OM from soils of the five ecosystems in the three clusters exhibited distinct pH dependence patterns. With the increase in pH, the cluster with weak bondings decreased, and that with strong bondings increased, while the one with metal-bound complexes showed a maximum at weakly acidic pH. Organo-mineral fractions and metal cations in MAOM constructed a complex network with pH as the central node. Results suggest that precipitation does not only alter vegetation type and microbial biomass but also regulate soil pH, which is balanced by specific metal cations, thus resulting in particular pH preference of specific OM clusters. These findings demonstrate that soil pH plays a central role in unveiling MAOM dynamics and can serve as a good predictor of soil organo-mineral fractions across alpine ecosystems.

Beam hopping technology can provide enough fl exibility and high resource utilization for satellite communication system, and shows good performance in broadband satellite communication system.However, the existing beam planning scheme of beam hopping technology is relatively fi xed and can not change with the change of service demand characteristics.For the situation of uneven distribution of user demand and scattered location, the advantages of beam hopping technology are diffi cult to play.To solve the above problems, a beam optimization method was proposed, which used the agility characteristics of phased array antenna to adjusted the size of beam according to the characteristics of service distribution, so as to made the service demand in each beam more balanced.On this basis, from the perspective of matching the demand with the actual transmission capacity, the minimization n-order diff erence objective function was established, the time slot resources allocated in each beam were solved by convex optimization algorithm, and the overall satisfaction and system delay of the system before and after beam optimization were compared.The simulation results showed that compared with the traditional beam planning scheme, the algorithm could improved the overall satisfaction of the system, reduced the system delay and improved the access success rate of the users.

First, we analyzed the composition and demand characteristics of LEO satellite IoTs, and designed an architecture of LEO satellite IoTs.Second, to extracted characteristics of electromagnetic environment and channel, we studied electromagnetic environment and channel sensing methods through constructing neural network model.Then, we investigated an active terminal-identifi cation method for detecting active terminals.Finally, considered multiple-input multiple-output as the main approach to improved the capacity of terrestrial mobile communication systems, we explored a pair of algorithms of mining satellite spatial resources for realizing the collision mitigation and capacity improvement of LEO IoT terminals.Simulation results showed that the designed method sofmining spatial-domain resources could mitigated eff ectively signal collisions among IoT terminals, and improved signifi cantly the concurrent accessing number of LEO satellite IoT terminals.

With the vigorous development of space communication technology and the continuous advancement of space-integrated-ground information network, satellite communication networks resource management and control is becoming more and more complex.Due to the scarcity of satellite resources, the slowness of resource scheduling compared to the status refreshing, and uneven distribution of business, eff ciently managing resources has become one of urgent problems to be solved in the development of satellite communications.In view of the heterogeneous network system architecture of high and low orbit satellites, the challenges, its network resource management and control facing, were analyzed.Integrating on the basis of traditional management and control architecture, the collaborative management and control architecture based on group management was introduced.The management strategy of satellite network virtual resource pool was explained to relieved resources scarcity.Resource scheduling algorithms based on deep reinforcement learning(DRL) was introduced to solved the mismatch problem of traditional scheduling methods in complex environments.Beam-hopping technology was adopted to deal with the two-dimensional unevenness of service distribution in time and space.

Under the scenario of global warming, the response of greenhouse gas emissions from alpine wetlands remains unclear. In this study, fluxes of CO2 and CH4 were measured during daytime for the microtopographic features of hollows and hummocks in a wetland in the Tibetan Plateau under two elevated temperatures, increments of ∼1°C (T1 treatment) and ∼2°C (T2 treatment), during the growing season in 2019. The results showed that warming significantly increased the cumulative net ecosystem CO2 exchanges (NEE) for both microtopographic features in the wetland compared to the control due to a combination of the increased gross primary production (GPP) with an increase in ecosystem respiration (ER). Similarly, warming also increased cumulative CH4 emission significantly. The effect was stronger for T2 than that for T1 for all component fluxes (GPP, ER, NEE, and CH4). Generally, NEE and CH4 fluxes both rose at first and then decreased. NEE peaked at the end of July for both hollows and hummocks, while CH4 emissions peaked in the middle of August. The cumulative CH4 emissions from the hummocks were significantly higher than those of the hollows, and CH4 emissions under illumination were significantly higher than those in darkness, which may be caused by the irradiation-sensitive vegetable internal convective gas transport system which diffuses CH4 from the pedosphere. This study revealed that warming strengthened the function of the CO2 sink but also increased CH4 emissions from the alpine wetlands on the Qinghai–Tibet Plateau.

Satellite Internet has the ability of global seamless coverage, which will be an important part of the future land-sea-air-space integrated network.Firstly, the architecture of satellite Internet was discussed, which included the general composition of the system, networking mode, working process, etc.Secondly, the methods of modeling broadband and narrowband traffic were proposed to extract the uneven space-time distribution characteristics of traffic for satellite Internet.Then, the co-frequency interference of spectrum-sharing was analyzed, showing that there would be more serious co-frequency interference among different non-geostationary satellite orbit (NGSO) constellations in the future.Next, the schemes of both beamforming design for low earth orbit satellite and beam-hopping design for high orbit satellite were explored, which could significantly improve the system capacity through efficient mining of space-domain resources.Finally, the development prospect of several technologies of satellite Internet were given.

Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light areas, but often these dark light areas have rich structural information. The structural features hidden in the darkness are difficult to extract, resulting in ghosting and misalignment when stitching, which makes it difficult to meet the practical application requirements. Therefore, a nighttime image stitching method based on image decomposition enhancement is proposed to address the problem of insufficient line feature extraction in the stitching process of nighttime images. The proposed algorithm performs luminance enhancement on the structural layer, smoothes the nighttime image noise using a denoising algorithm on the texture layer, and finally complements the texture of the fused image by an edge enhancement algorithm. The experimental results show that the proposed algorithm improves the image quality in terms of information entropy, contrast, and noise suppression compared with other algorithms. Moreover, the proposed algorithm extracts the most line features from the processed nighttime images, which is more helpful for the stitching of nighttime images.

Frame buildings as important nodes of urban space. The include high-speed railway stations, airports, residences, and office buildings, which carry various activities and functions. Due to illumination irrationality and mutual occlusion between complex objects, low illumination situations frequently develop in these architectural environments. In this case, the location information of the target is difficult to determine. At the same time, the change in the indoor electromagnetic environment also affects the location information of the target. Therefore, this paper adopts the vision method to achieve target localization in low-illumination environments by feature matching of images collected in the offline state. However, the acquired images have serious quality degradation problems in low-illumination conditions, such as low brightness, low contrast, color distortion, and noise interference. These problems mean that the local features in the collected images are missing, meaning that they fail to achieve a match with the offline database images; as a result, the location information of the target cannot be determined. Therefore, a Visual Localization with Multiple-Similarity Fusions (VLMSF) is proposed based on the Nonlinear Enhancement And Local Mean Filtering (NEALMF) preprocessing enhancement. The NEALMF method solves the problem of missing local features by improving the quality of the acquired images, thus improving the robustness of the visual positioning system. The VLMSF method solves the problem of low matching accuracy in similarity retrieval methods by effectively extracting and matching feature information. Experiments show that the average localization error of the VLMSF method is only 8 cm, which is 33.33% lower than that of the Kears-based VGG-16 similarity retrieval method. Meanwhile, the localization error is reduced by 75.76% compared with the Perceptual hash (Phash) retrieval method. The results show that the method proposed in this paper greatly alleviates the influence of low illumination on visual methods, thus helping city managers accurately grasp the location information of targets under complex illumination conditions.

Abstract Forest biomass is an important component of terrestrial carbon pools. However, how climate, biodiversity, and structural attributes co‐determine spatiotemporal variation in forest biomass remains not well known. We aimed to shed light on these drivers of forest biomass by measuring diversity and structural attributes of tree species in 400‐m2 plots located every 100 m along a 4200‐m elevational gradient in the eastern Himalayas. We applied structural equation models to test how climate, species richness, structural attributes, and their interactions influence forest biomass. Importantly, species richness was a stronger driver of biomass than environmental and structural attributes such as annual air temperature or stem density. Integrating the availability of energy and the demand for water, potential evapotranspiration was more strongly correlated with biomass than water availability, likely due to the strong influence of the Indian summer monsoon. Thus, interactions between climate and tree community composition ultimately control how much carbon is stored in woody biomass across bioclimatic gradients. This fundamental understanding will support predictive efforts of the forest carbon sink in this hydroclimatically important region and help preserving regional forests as a potent natural solution for climate change mitigation.

Considering “internet of everything” of the future internet of things (IoT), the space-based integrated IoT architecture was designed, and the interference scenarios of the coexistence of satellite and ground was analyzed, with the aid of analyzing the architectures of the spatial information network and space-based access network.Specifi cally, fi rstly, relying on both the integration of satellite network and ground network, and the advanced design concept of decoupling control and data, a spacebased satellite IoT architecture was designed in the presence of the specifi c characteristics of access network and terminals, which included space-based backbone network, space-based access network, ground-based IoT, core network and multimodal IoT terminals.Moreover, the process of space-based control plane was also given.Secondly, interference analysis was carried out for satellite spectrum sharing scenario and satellite-ground spectrum sharing scenario respectively, and preliminary analysis results were presented.

Beam-hopping technology provides a basis for flexible allocation and efficient utilization of satellite resources,and it is considered as a key technology of the new generation of high-throughput satellite.With the goal of service-driven,efficient utilization of resources and on-demand coverage of hot spots area,from the aspects of system architecture,communication system,working mechanism,resource allocation method and beam-hopping pattern design,user access strategy,air interface protocol design and system workflow,a top-down feasibility and technical foundation was provided for the design of a new generation of high-throughput satellite communication system based on beam hopping.Finally,it meet the requirements of flexible and mobile information service for the space-earth integration network,as well as the demand of the space-based backbone network with high reliability and large capacity transmission.

In this paper, an asynchronous collision-tolerant ACRDA scheme based on satellite-selection collaboration-beamforming (SC-ACRDA) is proposed to solve the avalanche effect caused by packet collision under random access (RA) high load in the low earth orbit (LEO) satellite Internet of Things (IoT) networks. A non-convex optimization problem is formulated to realize the satellite selection problem in multi-satellite collaboration-beamforming. To solve this problem, we employ the Charnes-Cooper transformation to transform a convex optimization problem. In addition, an iterative binary search algorithm is also designed to obtain the optimization parameter. Furthermore, we present a signal processing flow combined with ACRDA protocol and serial interference cancellation (SIC) to solve the packet collision problem effectively in the gateway station. Simulation results show that the proposed SC-ACRDA scheme can effectively solve the avalanche effect and improve the performance of the RA protocol in LEO satellite IoT networks compared with benchmark problems.

Nanoparticles widely exist in nature and may be formed through inorganic or organic pathways, exhibiting unique physical and chemical properties different from those of bulk materials. However, little is known about the potential consequences of nanomaterials on microbes in natural environments. Herein, we investigated the interactions between microbes and nanoparticles by performing experiments on the inhibition effects of gold, ludox and laponite nanoparticles on Escherichia coli in liquid Luria–Bertani (LB) medium at different nanoparticle concentrations. These nanoparticles were shown to be effective bactericides. Scanning electron microscopy (SEM) images revealed the distinct aggregation of cells and nanoparticles. Transmission electron microscopy (TEM) images showed considerable cell membrane disruption due to nanoparticle accumulation on the cell surfaces, resulting in cell death. We hypothesized that this nanoparticle accumulation on the cell surfaces not only disrupted the cell membranes but also physically blocked the microbes from accessing nutrients. An iron-reducing bacterium, Shewanella putrefaciens, was tested for its ability to reduce the Fe (III) in solid ferrihydrite (HFO) or aqueous ferric citrate in the presence of laponite nanoparticles. It was found that the laponite nanoparticles inhibited the reduction of the Fe (III) in solid ferrihydrite. Moreover, direct contact between the cells and solid Fe (III) coated with the laponite nanoparticles was physically blocked, as confirmed by SEM images and particle size measurements. However, the laponite particles had an insignificant effect on the extent of aqueous Fe (III) bioreduction but slightly enhanced the rate of bioreduction of the Fe (III) in aqueous ferric citrate. The slightly increased rate of bioreduction by laponite nanoparticles may be due to the removal of inhibitory Fe (II) from the cell surface by its sorption onto the laponite nanoparticle surface. This result indicates that the scavenging of toxic heavy metals, such as Fe (II), by nanoparticles may be beneficial for microbes in the environment. On the other hand, microbial cells are also capable of detoxifying nanoparticles by coagulating nanoparticles with extracellular polymeric substances or by changing nanoparticle morphologies. Hence, the interactions between microbes and nanoparticles in natural environments should receive more attention.

In recent years, low-orbit satellites have become an important development direction in satellite IoT systems. The number of terminals is large and data collisions occur frequently in the low-orbit satellite IoT scenario. How to design a reliable random access protocol to improve the tolerance of the system for collision is one of the research hotspots in this field. In this paper, the random access protocol, used in the Internet of Things (IoT), for low-orbit satellites is studied, and the access process of the IoT terminals in the scenario is constructed. The access performance of the SSA protocol is analyzed and an improved SSA random access strategy, called Retransmission-SSA (R-SSA), is proposed. The simulation results show that the designed R-SSA can effectively tolerate the signal conflicts between terminals in the beam-hopping LEO IoT scenario and improve the probability of the concurrent access of low-orbit sIoT terminals.

With the rapid development of mobile Internet technology, localization using visual image information has become a hot problem in the field of indoor localization research, which is not affected by signal multipath and fading and can achieve high accuracy localization in indoor areas with complex electromagnetic environments. However, in practical applications, position estimation using visual images is easily influenced by the user’s photo pose. In this paper, we propose a multiple-sensor-assisted visual localization method in which the method constructs a machine learning classifier using multiple smart sensors for pedestrian pose estimation, which improves the retrieval efficiency and localization accuracy. The method mainly combines the advantages of visual image location estimation and pedestrian pose estimation based on multiple smart sensors and considers the effect of pedestrian photographing poses on location estimation. The built-in sensors of smartphones are used as the source of pedestrian pose estimation data, which constitutes a feasible location estimation method based on visual information. Experimental results show that the method proposed in this paper has good localization accuracy and robustness. In addition, the experimental scene in this paper is a common indoor scene and the experimental device is a common smartphone. Therefore, we believe that the proposed method in this paper has the potential to be widely used in future indoor navigation applications in complex scenarios (e.g., mall navigation).

In this article, we investigate the spectrum-sharing for a space information network comprised of a geostationary orbit (GEO) satellite and a pair of low earth orbit (LEO) satellites, where one spectrum-sensing LEO (SLEO) is used to sense the status of spectrum occupancy of the GEO, and one data-transmission LEO (DLEO) satellite is allowed to access the shared spectrum of the GEO with the aid of the SLEO. In order to improve the throughput of the DLEO satellite, a spectrum sharing framework has been conceived relying on two stages. Differing from the conventional spectrum-sharing scheme, the data-transmission LEO satellite has the ability to access the shared spectrum both in overlay mode and in underlay mode. In overlay mode, the DLEO can perform transmission at any required transmit power. By contrast, in underlay mode, a spectrum-sensing and power allocation aided spectrum-sharing (SPA-SS) scheme is proposed to guarantee that the maximal amount of interference is imposed on the GEO caused by the DLEO. To be specific, in the first stage, the SLEO is invoked for performing spectrum-sensing, where the time intervals of spectrum-sensing have been specifically optimized to alleviate the interference received at the GEO in the second stage. Moreover, an adaptive power allocation is designed to maximal the throughput of the DLEO in the second stage, whilst maintaining the maximal interference imposed on the GEO is lower than the tolerable interference temperature of the GEO. Numerical results show that the proposed SPA-SS scheme outperforms the traditional spectrum-sharing scheme in terms of not only the average throughput, but also of the time-utilization ratio.

Abstract Interference alignment (IA) is a key technology for achieving the capacity scaling required by next generation wireless networks, which is proved to obtain the maximum degrees of freedom (DoF). The aim of this paper is to propose interference alignment schemes through manifold optimization theory for K-user interference channel. We limit the optimization only at transmitters and relax the hypothesis of channel reciprocity to mitigate the overhead caused by alternation between the forward and reverse links significantly. Firstly, we introduce a classical algorithm based on the steepest descent (SD) algorithm in a multi-dimensional complex space to achieve feasible IA. Then, we reform the optimization problem on Stiefel manifold and propose a novel SD algorithm based on this manifold with lower dimensions. Moreover, aiming at further reducing the complexity, the Grassmann manifold is introduced to derive corresponding algorithm for reaching the perfect IA. Numerical simulations show that the proposed algorithms on manifolds have better performance both on system throughput and convergence than classical methods and also achieve the maximum DoF.

The low earth orbit (LEO) internet of things (IoT) is an effective solution to solve the coverage inefficiency problem of terrestrial IoT in ocean,mountain and desert areas.On the basis of analyzing the developing background and situation of LEO IoT,the main application and service characteristics were given,finally the technical challenges and key technologies need to be solved for LEO IoT were discussed.

UAVs are flexible in action, changeable in shooting angles, and complex and changeable in the shooting environment. Most of the existing stitching algorithms are suitable for images collected by UAVs in static environments, but the images are in fact being captured dynamically, especially in low-altitude flights. Considering that the great changes of the object position may cause the low-altitude aerial images to be affected by the moving foreground during stitching, so as to result in quality problems, such as splicing misalignment and tearing, a UAV aerial image stitching algorithm is proposed based on semantic segmentation and ORB. In the image registration, the algorithm introduces a semantic segmentation network to separate the foreground and background of the image and obtains the foreground semantic information. At the same time, it uses the quadtree decomposition idea and the classical ORB algorithm to extract feature points. By comparing the feature point information with the foreground semantic information, the foreground feature points can be deleted to realize feature point matching. Based on the accurate image registration, the image stitching and fusion will be achieved by the homography matrix and the weighted fusion algorithm. The proposed algorithm not only preserves the details of the original image, but also improves the four objective data points of information entropy, average gradient, peak signal-to-noise ratio and root mean square error. It can solve the problem of splicing misalignment tearing during background stitching caused by dynamic foreground and improves the stitching quality of UAV low-altitude aerial images.

Image stitching refers to stitching two or more images with overlapping areas through feature points matching to generate a panoramic image, which plays an important role in geological survey, military reconnaissance, and other fields. At present, the existing image stitching technologies mostly adopt images with good lighting conditions, but the lack of feature points in scenes with weak light such as morning or night will affect the image stitching effect, making it difficult to meet the needs of practical applications. When there exist concentrated areas of brightness such as lights and large dark areas in the nighttime image, it will further cause the loss of image details making the feature point matching unavailable. The obtained perspective transformation matrix cannot reflect the mapping relationship of the entire image, resulting in poor splicing effect, and it is difficult to meet the actual application requirements. Therefore, an adaptive image enhancement algorithm is proposed based on guided filtering to preprocess the nighttime image, and use the enhanced image for feature registration. The experimental results show that the image obtained by preprocessing the nighttime image with the proposed enhancement algorithm has better detail performance and color restoration, and greatly improves the image quality. By performing feature registration on the enhanced image, the number of matching logarithms of the image increases, so as to achieve high accuracy for images stitching.

Alpine grasslands provide essential ecosystem functions and services, yet they are increasingly subject to anthropogenic perturbations. Northwest Yunnan (NWY) is one of the most popular tourist destinations in China, and alpine grasslands of NWY are particularly susceptible to human recreational activities such as hiker trampling. However, studies that explicitly evaluate the effects of trampling disturbance on alpine communities of NWY are very limited, let alone research testing the responses of these communities to trampling from a functional trait perspective. The present research is performed in alpine grasslands within and outside of a core nature conservation area of Shangri-La, NWY of China, to study species- and community-level responses of alpine communities to simulated trampling through a functional trait-based approach. The results showed that although species-specific differences existed, alpine plants generally showed rapid morphological changes in response to trampling disturbance. At the community level, trampling disturbance caused strong shifts in taxonomy-based diversity metrics (e.g., species richness, Simpson’s index and Shannon’s index) and functional trait-based diversity metrics (e.g., functional richness, functional evenness, functional divergence, and community-weighted mean trait values). Specifically, increased trampling intensity led to a decrease in taxonomic diversity. Meanwhile, functional richness decreased, while functional evenness and functional divergence increased with an increase in trampling intensity. Also, characteristics of alpine community traits had changed towards those with shorter height, reduced leaf size and lower LDMC, and such strong shifts in taxonomic and functional diversity might further affect the functioning and value of alpine grasslands. Therefore, the protection of high-altitude natural grasslands with high sensitivity and vulnerability is urgent, especially as they are increasingly experiencing multiple environmental and climatic stressors nowadays.

Space information network (SIN) plays an extremely important role in civil and military applications. However, a specific definition of SIN still remains a challenging issue. Unlike space communication network (SCN), which merely focuses on information delivery, SIN has the ability to process the delivered information and providing corresponding services based on the information, which makes SIN seem to be able to understand information. This paper offers architecture and corresponding network model for time-space uninterrupted SIN. Initially, both physical and logical constitution of SIN architecture are presented with several corresponding constellation designs, which is the fundamental work for setting nodes in SIN. Based on this SIN architecture, a hierarchical autonomous system (AS)-based network model for SIN is proposed in order to manage SIN more efficiently by separating the whole network into several relatively stable ASs. Furthermore, we analyze topology control schemes and network capacity trend of AS-model based SIN. This paper gives the future research directions in the conclusion.

In this paper, we explore the spectrum inference to achieve the spectrum occupancy in advance through analyzing the historical spectrum. We have conceived an offline-online cooperative framework. Specifically, the hyperparameters can be achieved on an offline way, which will be used for online prediction. Moreover, based on the accuracy of online spectrum inference, the hyperparameters can be further optimized relying on specifically designed grid search and K-fold cross-validation combined method in an iterative manner. We present a long short-term memory (LSTM) aided spectrum occupancy prediction method, relying on adaptive threshold quantization aided data preprocessing (ATQ-DP). To be specific, first, the captured spectrum data may be quantized by the adaptive thresholds in order to lesson the influence of noise imposed on them, where the thresholds are obtained by kernel density estimation (KDE) method. Then, LSTM will be activated to perform spectrum prediction based on the quantized data, thus, future spectrum occupancy can be inferred in advance. Additionally, performance evaluations show that the accuracy of spectrum inference is always better than that of the LSTM aided spectrum inference relying on the traditional fixed threshold quantization aided data preprocessing (FTQ-DP), where the FTQ-DP is used for comparison purposes.

Four new lipid siderophores bearing both L-threo- and L-erythro-β-hydroxyaspartic acids, potashchelins A-D (1-4), were isolated from the potash-salt-ore-derived extremophile Halomonas sp. MG34. The planar structures of 1-4 were elucidated on the basis of extensive 1D and 2D NMR studies and MS/MS data. Potashchelins 1-4 contain a hydrophilic nonapeptide headgroup sequentially consisting of β-hydroxyaspartic acid, serine, glycine, serine, serine, β-hydroxyaspartic acid, threonine, serine, and cyclic N(δ)-hydroxy-ornithine, which is appended by one of a series of fatty acids ranging from dodecanoic acid to tetradecanoic acid. The absolute configurations of the amino acids of potashchelins 1-4 were determined by C3 and advanced Marfey's reaction, partial hydrolysis, and bioinformatics analysis, which revealed that potashchelins 1-4 bear both L-threo- and L-erythro-β-hydroxyaspartic acid. Phylogenetic analysis showed that the stand-alone β-hydroxylase, PtcA, and the fused domain with β-hydroxylase activity in PtcB are expected to be responsible for the formation of L-erythro and L-threo diastereomers, respectively. Additionally, utilizing a comparative genomics approach, we revealed an evolutionary mechanism for lipid siderophores in Halomonas involving horizontal transfer. Bioassays showed that potashchelin A and D had weak antibacterial activity against B. subtilis CPCC 100029 with an MIC value of 64 μg/mL.

We investigate a distributed-satellite-clusters (DSC)-system-based spectrum sensing, to enhance the ability for sensing weak signals. However, the spectrum-sensing performance may be significantly decreased by the phase deviations among different satellite clusters, where the deviations may be caused by the movement and the perturbation of satellites. To eliminate such a decrement, we propose a cooperative spectrum-sensing scheme in the presence of phase deviations, where the deviations are alleviated by a special two-stage phase synchronization. Specifically, the phase compensation is first performed relying on broadcasting reference signals and the ephemeris, to address the challenges of the deviations caused by the movement. Then, a two-bit feedback algorithm, having a dynamic disturbance step size, is further adopted for controlling and mitigating the deviations caused by the perturbation. Additionally, we provide the closed-form expression of the correct detection probability of the proposed spectrum-sensing scheme, using the specially derived probability density function of the sum of the shadowed-Rician random variables with independently identical distribution. Simulation results show that the proposed scheme can achieve the best spectrum-sensing performance, comparing with the traditional energy detection, eigenvalue ratio test and the generalized likelihood ratio test.

With the development of satellite communication, the number of satellites in space continuously increases. However, the available spectrum resources are scarce. To address the spectrum scarcity, sharing the spectrum between different communication systems is a promising option. In this paper, a novel cognitive satellite network with geostationary earth orbit (GEO) and low earth orbit (LEO) broadband systems is studied in the downlink case. First, we present a general interference analysis model and simplify it by transforming the spatial dimension into the time dimension according to the satellite motion. Based on the interference analysis, an optimization algorithm with beamhopping and adaptive power control techniques is proposed, which can simultaneously enhance the spectral efficiency and protect the primary system. The system performance of the coexistence scenario is evaluated in terms of the system throughput. Numerical results demonstrate that the spectrum-sharing method between GEO and LEO systems is feasible, and the cognitive network can achieve a high spectral efficiency. Furthermore, the factors that affect the system in the spectral coexistence scenario are analyzed.

Seasonality, an exogenous driver, motivates the biological and ecological temporal dynamics of animal and plant communities. Underexplored microbial temporal endogenous dynamics hinders the prediction of microbial response to climate change. To elucidate temporal dynamics of microbial communities, temporal turnover rates, phylogenetic relatedness, and species interactions were integrated to compare those of a series of forest ecosystems along latitudinal gradients. The seasonal turnover rhythm of microbial communities, estimated by the slope (w value) of similarity-time decay relationship, was spatially structured across the latitudinal gradient, which may be caused by a mixture of both diurnal temperature variation and seasonal patterns of plants. Statistical analyses revealed that diurnal temperature variation instead of average temperature imposed a positive and considerable effect alone and also jointly with plants. Due to higher diurnal temperature variation with more climatic niches, microbial communities might evolutionarily adapt into more dispersed phylogenetic assembly based on the standardized effect size of MNTD metric, and ecologically form higher community resistance and resiliency with stronger network interactions among species. Archaea and the bacterial groups of Chloroflexi, Alphaproteobacteria, and Deltaproteobacteria were sensitive to diurnal temperature variation with greater turnover rates at higher latitudes, indicating that greater diurnal temperature fluctuation imposes stronger selective pressure on thermal specialists, because bacteria and archaea, single-celled organisms, have extreme short generation period compared to animal and plant. Our findings thus illustrate that the dynamics of microbial community and species interactions are crucial to assess ecosystem stability to climate variations in an increased climatic variability era.

Internet of Things (IoT) is one of the evolutionary directions of the Internet. This paper focuses on the low earth orbit (LEO) satellite constellation-based IoT services for their irreplaceable functions. In many cases, IoT devices are distributed in remote areas (e.g., desert, ocean, and forest) in some special applications, they are placed in some extreme topography, where are unable to have direct terrestrial network accesses and can only be covered by satellite. Comparing with the traditional geostationary earth orbit (GEO) systems, LEO satellite constellation has the advantages of low propagation delay, small propagation loss and global coverage. Furthermore, revision of existing IoT protocol are necessary to enhance the compatibility of the LEO satellite constellation-based IoT with terrestrial IoT systems. In this paper, we provide an overview of the architecture of the LEO satellite constellation-based IoT including the following topics: LEO satellite constellation structure, efficient spectrum allocation, heterogeneous networks compatibility, and access and routing protocols.

An interference source single satellite location method based on movable antenna with spot beam was proposed for GEO satellites.In this method,a mapping relationship between orientation of interference and antenna gain is first established with the function of antenna pattern.Equations were established to calculate the interference signal link combining the orientation information of center of movable spot beam and the intensity of interference signal which derived from moving the beam several times.The orientation of interference was obtained by solving the equations with the help of particle swarm optimization(PSO).Then the longitude and latitude of the interference source could be calculated,jointly considering the ephemeris and the information of the earth.We made a positioning simulation of the ground interference source with the proposed method.The results validate the effectiveness of this method.

This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN.

This article investigates the data persistence problem in the planetary surface network of interplanetary Internet using the distributed raptor codes. In order to improve the lifetime of space information and space nodes’ energy efficiency in planetary surface network, we propose an efficient data persistence strategy based on raptor codes and probabilistic broadcasting. Unlike most existing data persistence strategies where the random walks are used to disseminate source packets, the probabilistic broadcasting mechanism is employed in the proposed strategy to reduce the data dissemination cost by exploiting the broadcast property of wireless networks. The decoding performance and data dissemination cost are analyzed. Simulation results validate that the proposed strategy consumes the least data dissemination cost while achieving a better decoding performance compared with other representative strategies.

This article investigates the distributed data storage problem with compressed sensing in the space information network. Since there exists a performance-energy trade-off, most existing strategies focus only on improving the compressed sensing construction performance or reducing the energy consumption, respectively. In order to achieve a better balance, a novel and efficient strategy, referred to as distributed storage strategy based on compressed sensing, is proposed in this article. Unlike other strategies which require source packets visiting the entire network, the proposed strategy is a “one-hop” method since information exchange is only performed between neighbors. Therefore, the compressed sensing measurement matrix depends heavily on the degree of each space node. We prove that the proposed strategy guarantees the compressed sensing reconstruction performance under both sparse orthonormal basis and dense orthonormal basis. Simulation results validate that, compared with the representative CStorage strategy and compressive data persistence strategy, the proposed strategy consumes the least energy and computational overheads, while almost without sacrificing the compressed sensing reconstruction performance.

With the growing demand, Wireless Multimedia Sensor Networks (WMSNs) play an increasingly important role, which enhances the capacity of typical Wireless Sensor Networks (WSNs). Additionally, integrating satellite systems into WMSNs brings about the beneficial synergy, especially in rural and sparsely populated areas. However, the available spectrum resource is scarce, which contradicts the high-speed content required for multimedia. Cognitive radio is a promising solution to address the conflict. In this context, we propose a novel spectrum-sharing method for the integrated wireless multimedia sensor and cognitive satellite network based on the dynamic frequency allocation. Specifically, the Low Earth Orbit (LEO) satellite system plays the role of the auxiliary to connect sensor nodes and the remote control host, and it shares the same frequency with the Geostationary Earth Orbit (GEO) system in the downlink. Because the altitudes of GEO and LEO satellites differ greatly, the beam size of GEO is much larger than that of LEO, which provides the opportunity for LEO beam to reuse the frequency that was allocated to the GEO beam. A keep-out region is defined to guarantee the spectral coexistence based on the interference analysis in the worst case. In addition, a dynamic frequency allocation algorithm is presented to deal with the dynamic configuration caused by the satellite motion. Numerical results demonstrate that the dynamic spectrum-sharing method can improve the throughput.

This paper investigates the topology control problem in the planetary surface network (PSN) of Interplanetary Internet (IPN) using an autonomous system (AS) approach. We propose a delay minimization topology control (DMTC) algorithm to achieve low time delay and strong connectivity in the planetary surface network. Compared with the most existing approaches where either the purely centralized or the purely distributed control method is adopted, the proposed algorithm is a hybrid control method. In order to reduce the cost of control, the control message exchange is constrained among neighboring AS networks. We prove that the proposed algorithm could achieve logical k -connectivity on the condition that the original physical topology is k -connectivity. Simulation results validate the theoretic analysis and effectiveness of the DMTC algorithm.

In recent decades, satellite-derived start of vegetation growing season (SOS) has advanced in many northern temperate and boreal regions. Both the magnitude of temperature increase and the sensitivity of the greenness phenology to temperature-the phenological change per unit temperature-can contribute the advancement. To determine the temperature-sensitivity, we examined the satellite-derived SOS and the potentially effective pre-season temperature (T eff) from 1982 to 2008 for vegetated land between 30°N and 80°N. Earlier season vegetation types, i.e., the vegetation types with earlier SOSmean (mean SOS for 1982-2008), showed greater advancement of SOS during 1982-2008. The advancing rate of SOS against year was also greater in the vegetation with earlier SOSmean even the T eff increase was the same. These results suggest that the spring phenology of vegetation may have high temperature sensitivity in a warmer area. Therefore it is important to consider temperature-sensitivity in assessing broad-scale phenological responses to climatic warming. Further studies are needed to explore the mechanisms and ecological consequences of the temperature-sensitivity of start of growing season in a warming climate.

Discrete cosine transform (DCT) is a special type of transform which is widely used for compression of speech and image. However, its use for spectrum sensing has not yet received widespread attention. This paper aims to alleviate the sampling requirements of wideband spectrum sensing by utilizing the compressive sampling (CS) principle and exploiting the unique sparsity structure in the DCT domain. Compared with discrete Fourier transform (DFT), wideband communication signal has much sparser representation and easier implementation in DCT domain. Simulation result shows that the proposed DCT-CSS scheme outperforms the conventional DFT-CSS scheme in terms of MSE of reconstruction signal, detection probability, and computational complexity.

This paper derives the channel estimation of a discrete cosine transform- (DCT-) based orthogonal frequency-division multiplexing (OFDM) system over a frequency-selective multipath fading channel. Channel estimation has been proved to improve system throughput and performance by allowing for coherent demodulation. Pilot-aided methods are traditionally used to learn the channel response. Least square (LS) and mean square error estimators (MMSE) are investigated. We also study a compressed sensing (CS) based channel estimation, which takes the sparse property of wireless channel into account. Simulation results have shown that the CS based channel estimation is expected to have better performance than LS. However MMSE can achieve optimal performance because of prior knowledge of the channel statistic.