Spectrum Management Research Articles

Antenna coupled bolometer simulation for terahertz radiation detection diffracted by a small metal object

Terahertz (THz) wave technologies are becoming near future wireless technologies because of its advantages of considerable bandwidth and still unlicensed spectrum. Its small bandwidth offers a more compact wireless device on a scale of micrometers. Detection is a challenge for applying these potential technologies. An antenna coupled with a bolometer device is a candidate to capture emitted THz wave radiation. Another consideration can be any possible obstacles. This paper presents a simulation of Terahertz wave radiation detected with a dipole antenna connected to a bolometer. This research uses a 3D simulation software of CST Microwave Studio to simulate linearly THz plane waves radiation passing through a small metal object. Some dipole antennas coupled bolometer detect the power of the plane wave radiation in location after the emitted plane waves hit the object. The result shows that the highest detected power can be detected. Even the detector is located at the center of the area blocked by the metal object. This condition indicates that diffraction is high enough, affected by the comparable size, the metal, wavelength, and observation distances. More studies to characterize this diffraction effect are becoming useful, especially for THz imaging applications, such as metal detection for an airport security system.

Something old, something new and something borrowed.

Surgical management of Placenta Accreta Spectrum (PAS) is, if anything, wrought with complexity. Placental invasion can be confined and relatively easy to manage or as surgically challenging as a Stage IV ovarian tumor. In the most extensive cases, the placenta is equally capable of encasing not only the ureter, but also the femoral vessels and obturator nerve, but instead of solid tumor, the surgeon is faced with a mixed solid-cystic structure that behaves like a large arteriovenous fistula until the blood supply from the uterine arteries and collaterals from the bladder are controlled.

Optimal ADMM-Based Spectrum and Power Allocation for Heterogeneous Small-Cell Networks with Hybrid Energy Supplies

Powering cellular networks with hybrid energy supplies is not only environment-friendly but can also reduce the on-grid energy consumption, thus being emerging as a promising solution for green networking. Intelligent management of spectrum and power can increase the network utility in cellular networks with hybrid energy supplies, usually at the cost of higher energy consumption. Unlike prior studies on either the network utility maximization or on-grid energy cost minimization, this paper studies the joint spectrum and power allocation problem that maximizes the system revenue in a heterogeneous small-cell network with hybrid energy supplies. Specifically, the system revenue is considered as the difference between the network utility and on-grid energy cost. By developing the convexity of the optimization problem through transformation and reparameterization, we propose a joint spectrum and power allocation algorithm based on the primal-dual arguments to obtain the optimal solution by iteratively solving the primal and dual sub-problems of the convex optimization problem. To solve the primal sub-problem, we further propose the Lagrangian maximization based on the alternating direction method of multipliers (ADMM), and derive the optimal solution in the closed-form expression at each iteration. It is shown that the proposed joint spectrum and power allocation algorithm approaches the global optimality at the rate of 1=n with n being the number of iterations. Also, the proposed ADMM-based Lagrangian maximization algorithm approaches the primal optimal solution with the time complexity of O(1=ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> ) iterations with ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> being the termination parameter. Simulation results show that in comparison with the power control with equal frequency allocation algorithm and frequency allocation with equal power allocation algorithms the proposed algorithm increases the system revenue by over 20 and 60 percent without consuming more on-grid energy when the proportional fairness utility and the weighted sum rate utility are considered with the approximate system parameter settings, respectively. Meanwhile, in comparison with the full frequency reuse case, the proposed algorithm increases the system revenue by 20 percent at least in terms of the weighted sum rate utility, although it achieves the similar system revenue when considering the proportional fairness utility. Simulation results also show that our proposed algorithm can perform well under the realistic fast fading channel conditions.

Dynamic Spectrum Management with Network Function Virtualization for UAV Communication

Rapid increases in unmanned aerial vehicles (UAVs) applications are attributed to severe spectrum collision issues, especially when UAVs operate in spectrum scarce environments, such as urban areas. Dynamic air-to-ground (A2G) link solutions can mitigate this issue by utilizing programmable communication hardware in the air and real-time assignment of spectrum resources to achieve high-throughput and low-latency connectivity between UAVs and operators. To mitigate the high-computation issue among ground control station (GCS) networks and provide a broad communication coverage for large number of UAVs, we propose an advanced UAV A2G communication solution integrated with the dynamic spectrum management (DSM) and network function virtualization (NFV) technology to serve urban operations. The edge-cutting UAV communication technologies are surveyed. The proposed scheme is discussed in terms of the high-level system architecture, virtual network architecture, specific virtual functions (SVFs), and affiliated operation support databases. Some major research challenges are highlighted and the possible directions of future research are identified.

Ready-to-use Dasiglucagon Injection as a Rapid and Effective Treatment for Severe Hypoglycemia

Background: Severe hypoglycemia (SH) is an all too common and feared complication of insulin therapy and necessitates urgent medical intervention. Dasiglucagon, a novel peptide analog of human glucagon, is uniquely stable in aqueous solutions and is currently in development for treatment of SH via a ready-to-use auto-injector. Objective: To assess the efficacy and safety of dasiglucagon injection as a rapid and dependable treatment for SH. Methods: In the pivotal Phase 3 trial, NCT03378635, dasiglucagon (0.6 mg) was compared to placebo, and GlucaGen®, in adults with type 1 diabetes mellitus (N=168), randomized (2:1:1) after insulin-induced hypoglycemia. The primary endpoint was time to plasma glucose (PG) recovery, defined as first PG increase of 20 mg/dL after treatment initiation. Results: The median time to recovery was 10 min for dasiglucagon vs 12 and 40 min for GlucaGen® and placebo, respectively. In the dasiglucagon arm, 65% of patients recovered within 10 min vs 49% receiving GlucaGen®. After 15 min, 99% of dasiglucagon-treated patients recovered vs 95% treated with GlucaGen®. Dasiglucagon was generally well tolerated. Similar frequencies of nausea (55% and 53%), and vomiting (23% and 19%), were reported with dasiglucagon and GlucaGen®, respectively. Injection-site reactions 30 minutes post- dose were reported in 2%, 7%, and 5% of patients in the dasiglucagon, GlucaGen®, and placebo groups, respectively. Conclusions: Dasiglucagon 0.6 mg demonstrated rapid and efficacious treatment for SH. The stability of dasiglucagon in aqueous formulation supports its further development as a unique treatment option for the management of the entire spectrum of mild, moderate, and SH.

A Deep Reinforcement Learning Framework for Spectrum Management in Dynamic Spectrum Access

Dynamic spectrum access (DSA) has the great potential to alleviate spectrum shortage and promote network capacity. However, two fundamental technical issues have to be addressed, namely, interference coordination between DSA users and interference suppression for primary users (PUs). These two issues are very challenging since generally there is no powerful infrastructures in DSA networks to support centralized control. As a result, DSA users have to perform spectrum management individually, including spectrum access and power allocation, without accurate channel state information and centralized control. In this article, a novel spectrum management framework is proposed, in which Q-learning, a type of reinforcement learning, is utilized to enable DSA users to carry out effective spectrum management individually and intelligently. For more efficient process, neural networks (NNs) are employed to implement Q-learning processes, so-called deep Q-network (DQN). Furthermore, we also investigate the optimal way to construct DQN considering both the performance of wireless communications and the difficulty of NN training. Finally, extensive simulation studies are conducted to demonstrate the effectiveness of the proposed spectrum management framework.

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview.

Unmanned Aerial Vehicles (UAVs) demand technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices, or even other UAVs. Still, UAVs usually operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. Combining UAVs with Cognitive Radio (CR) may increase their general communication performance, thus allowing them to execute missions where the conventional UAVs face limitations. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software transmission. CR smartly uses free transmission channels and/or chooses them according to application’s requirements. Moreover, CR is considered a key enabler for deploying technologies that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT), and the Internet of Flying Things (IoFT). This paper presents an overview on the field of CR for UAV communications and its state-of-the-art, testbed alternatives for real data experiments, as well as specifications to build a simple and low-cost testbed, and indicates key opportunities and future challenges in the field.

Interleukin-1 Blockade in Polygenic Autoinflammatory Disorders: Where Are We now?

Polygenic autoinflammatory diseases (AIDs), such as systemic juvenile idiopathic arthritis (sJIA), adult-onset Still's disease, Kawasaki disease, idiopathic recurrent pericarditis (IRP), Behçet’s Syndrome, Crystal-induced arthropatihes such as gout or Calcium pyrophosphate deposition disease are characterized by the overexpression of inflammasome-associated genes, leading to a dysregulation of the innate immune response. The IL-1 cytokine family (IL-1α, IL-1β, IL-1Ra, IL-18, IL-36Ra, IL-36α, IL-37, IL-36β, IL-36g, IL-38, IL-33) was defined to be principally responsible for the inflammatory nature of polygenic AIDs. Several clinical trials were initiated, and IL-1 blockade has been proven to cause a rapid reduction of clinical symptoms and normalization of laboratory parameters in the majority of cases. Randomized, placebo-controlled, clinical trials, together with registry-based clinical trials and open-label, retrospective and prospective observational studies, supported the efficacy and safety of IL-1 inhibitors in the treatment of polygenic AIDs. Most of the current data are focused on the therapeutic use of anakinra, an IL-1 receptor antagonist, canakinumab, an anti-IL-1β monoclonal antibody, and rilonacept, a soluble decoy receptor. However, other promising agents, such as gevokizumab, IL-1β blocking monoclonal antibody, tadekinig alfa, a human recombinant IL-18-binding protein, and tranilast, an analog of a tryptophan metabolite, are currently being tested. Anakinra, canakinumab and rilonacept caused impressive improvements in both systemic and musculoskeletal symptoms. Furthermore, the anti-IL-1 therapy allowed corticosteroid tapering and, in some cases, even withdrawal. This article reviews the current IL-1 inhibitors and the results of all clinical trials in which they have been tested for the management of broad spectrum of polygenic AIDs.

Radio Frequency Interference Detection and Localization in Sentinel-1 Images

The C-band Sentinel-1 synthetic aperture radar (SAR) images are affected by radio frequency interference (RFI), and this article proposes an RFI detection and localization method for these images. First, a detailed analysis of RFI based on the generation mechanism is provided, which shows that RFI in the ground range detected (GRD) products differs from common backscattering not only in frequency spectrums but also in radiometric characteristics. Then, an RFI index (RFII) is proposed for RFI detection, which takes full advantage of unique RFI characteristics in dual-polarization GRD images. Finally, both detection results in ascending and descending passes are used to locate the ground RFI sources, assuming that the presence of RFI is persistent. Theoretical analyses show that the location area is a diamond of approximately 88.76 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Experimental results demonstrate that the proposed method performs quite well on Sentinel-1 GRD data. It provides a convenient and tractable tool for assessing Sentinel-1 data quality as well as for monitoring RFI. Thus, Sentinel-1 measurements can be used to monitor C-band RFI, which is an important task of electromagnetic spectrum management.

Coexistence algorithms for LTE and WiFi networks in unlicensed spectrum: performance optimization and comparison

Facing the challenges brought by the surge in the demand for mobile data traffic, many coexistence algorithms for long-term evolution (LTE) and WiFi networks in the unlicensed spectrum have been developed recently. In this article, we investigate, optimize, and compare several solutions to operate LTE in the unlicensed band, which include duty-cycle muting (DCM) mechanism, listen-before-talk (LBT) mechanism, and LTE and WLAN aggregation (LWA) algorithm. We first overview the architecture, working mechanism, coexistence problem with WiFi network, and overall cost of the three different coexistence mechanisms. Their advantages and disadvantages are also compared and analyzed in detail. Then, to maximize the spectrum efficiency of unlicensed band, we optimize the corresponding adjustable variables in the three algorithms. Furthermore, numerical simulations are conducted to compare the performances of DCM, LBT, and LWA explicitly. The results show that DCM can achieve the highest throughput for the LTE network while LBT and LWA have less impact on the WiFi system.

Intelligent Cognitive Radio Ad-Hoc Network: Planning, Learning and Dynamic Configuration

Cognitive radio (CR) is an adaptive radio technology that can automatically detect available channels in a wireless spectrum and change transmission parameters to improve the radio operating behavior. A CR ad-hoc network (CRAHN) should be able to coexist with primary user (PU) systems and other CR secondary systems without causing harmful interference to licensed PUs as well as dynamically configure autonomous and decentralized networks. Therefore, an intelligent system structure is required for efficient spectrum use. In this paper, we present a learning-based distributed autonomous CRAHN network system model for network planning, learning, and dynamic configuration. Based on the system model, we propose machine learning-based optimization algorithms for spectrum sensing, cluster-based ad-hoc network configuration, and context-aware signal classification. Using the sensing engine and the cognitive engine, the surrounding spectrum usage and the neighbor network operation status can be analyzed. The proposed policy engine can create network operation policies for the dynamically changing surrounding wireless environment, detect policy conflicts, and infer the optimal policy for the current situation. The decision engine finally determines and configures the optimal CRAHN configuration parameters through cooperation with a learning engine, in which we implement the proposed machine-learning algorithms. The simulation results show that the proposed machine-learning CRAHN algorithms can construct CR cluster networks that have a long network lifetime and high spectrum utility. Additionally, with high signal context recognition performance, we can ensure coexistence with neighboring systems.

Price of Fairness in Digital Subscriber Line Systems Using Dynamic Spectrum Management

Bit rate fairness is an important concern in current G.fast and the forthcoming fifth generation DSL systems where the frequency is extended to hundreds of MHz and crosstalk couplings reach unprecedented levels. In this paper, we study the price of fairness (PoF) in dynamic spectrum management (DSM) enabled DSL systems. We propose optimal low complexity PoF-constrained max-min fairness (MMF), weighted max-min fairness (WMMF), proportional fairness (PF), and (p,α)-PF algorithms. The proposed algorithms inherently provide weight factors which can be used to reduce the computational complexity of user encoding or decoding order optimizing algorithms used in nonlinear vectoring. Our simulation results show that although PoF grows exponentially with the minimum bit rate, fairness can be improved considerably with a relatively small price in DSM level 2 spectrum balancing and particularly in DSM level 3 using minimum mean square error (MMSE) generalized decision feedback equalizer (GDFE). It is also seen that the proposed optimal PoF-constrained MMF algorithm can reach the solutions of PF and (p,α)-PF measures for some PoF. That is, the PoF-constrained MMF or WMMF algorithms can be used instead of the non-linear PF and (p,α)-PF measures, which often result in computationally intensive solutions.

CQI-Based Interference Detection and Resource Allocation With QoS Provision in LTE-U Systems

Long-Term Evolution in unlicensed spectrum (LTE-U) is a promising solution to address the spectrum scarcity in the cellular networks. However, the LTE-U and WiFi systems may interfere with each other and degrade the performance. Thus, many works have focused on coexistence design and proposed various solutions. Nevertheless, the hidden terminal problem has not been well studied in the LTE-U system. In this paper, we investigate joint interference detection and resource allocation in the LTE-U system to maximize the overall system throughput while guaranteeing each user's quality-of-service (QoS) requirements. Firstly, to solve the hidden terminal problem and avoid the co-channel interference between the WiFi and LTE-U users, we adopt a cluster-based algorithm to find the potential WiFi interference for each LTE-U user according to their channel quality indicator (CQI) feedback and location information. Secondly, we formulate a joint subcarrier and power allocation problem over the licensed and unlicensed bands with each user's QoS provision. To make the original problem more tractable, we separate it into licensed and unlicensed subproblems and then derive the optimal solution. Moreover, we propose a low-complexity resource allocation scheme. The simulation results show that the proposed schemes can significantly improve the system performance and outperform the existing schemes.

Modeling and Performance Analysis of Opportunistic Link Selection For UAV Communication.

In anticipation of wide implementation of 5G technologies, the scarcity of spectrum resources for the unmanned aerial vehicles (UAVs) communication remains one of the major challenges in arranging safe drone operations. Dynamic spectrum management among multiple UAVs as a tool that is able to address this issue, requires integrated solutions with considerations of heterogeneous link types and support of the multi-UAV operations. This paper proposes a synthesized resource allocation and opportunistic link selection (RA-OLS) scheme for the air-to-ground (A2G) UAV communication with dynamic link selections. The link opportunities using link hopping sequences (LHSs) are allocated in the GCSs for alleviating the internal collisions within the UAV network, offloading the on-board computations in the spectrum processing function, and avoiding the contention in the air. In this context, exclusive technical solutions are proposed to form the prototype system. A sub-optimal allocation method based on the greedy algorithm is presented for addressing the resource allocation problem. A mathematical model of the RA-OLS throughput with above propositions is formulated for the spectrum dense and scarce environments. An interference factor is introduced to measure the protection effects on the primary users. The proposed throughput model approximates the simulated communication under requirements of small errors in the spectrum dense environment and the spectrum scarce environment, where the sensitivity analysis is implemented. The proposed RA-OLS outperforms the static communication scheme in terms of the utilization rate by over 50% in case when multiple links are available. It also enables the collaborative communication when the spectral resources are in scarcity. The impacts from diverse parameters on the RA-OLS communication performance are analyzed.

IMPORTANCE OF NANOCARRIERS AND PROBIOTICS IN THE TREATMENT OF ULCERATIVE COLITIS

Ulcerative colitis (UC) is an inflammatory chronic disease primarily affecting the colonic mucosa; the extent and severity of colon involvement are variable. Ulcerative colitis is identified by mucus diarrhea, tenesmus, bowel distension, and anemia. 5-aminosalicylic acid drugs, steroids, and immune suppressants are used for the therapy of ulcerative colitis. The mainchallenges in the management of thediseaseare drug-related side-effects and local targeting. To overcome these challengesprobiotics and micro and Nanoparticulate systemauspiciousapproaches to overcome drug-related adverse side effects and local targeting.Upon ingestion, the probiotics can result in beneficial health effects. Probiotics and micro and nanoparticulate approaches for suitable targeting and overcome the drug-associated side effect. Probiotics are mainly used as gut modulators but are also nowadays explored for their use in ulcerative colitis.The current therapeutic goals are to achieve clinical remission along with mucosal healing, avoidance of complications such as side effects of the drug and to improve the quality of life. The use of probiotics to increase the health of the intestine and used to block or manage intestinal disorders. They may prevent the induction of inflammatory reactions. Probiotics must be inspected for efficacy in the prevention and management of a wide spectrum of gastrointestinal diseases, like antibiotic-associated diarrhea.Micro and Nanoparticulate drug delivery system has been achieving huge importance for targeting of the drug to colon locally at a controlled and sustained rate.

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

The spectrum low utilization and high demand conundrum created a bottleneck towards fulfilling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became the first step towards the intelligentization of mobile and wireless networks, and in order to strengthen its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of artificial intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at filling the gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies of spectrum management. This is a prospective review whose primary objective is to provide an in-depth exploration of the recent trends in AI strategies employed in mobile and wireless communication networks. The existing reviews in this area have not considered the relevance of incorporating the mathematical fundamentals of each AI strategy and how to tailor them to specific mobile and wireless networking problems. Therefore, this review addresses that problem by detailing how deep architectures can be integrated into spectrum management problems. Beyond reviewing different ways in which deep architectures can be integrated into spectrum management, model selection strategies and how different deep architectures can be tailored into the CR space to achieve better performance in complex environments are then reported in the context of future research directions.

Listen Before Receive (LBR) Assisted Network Access in LAA and WiFi Heterogeneous Networks

The Listen-Before-Talk (LBT) is the main procedure for Licensed Assisted Access (LAA) to accomplish fair and friendly coexistence with other operators or technologies operating over unlicensed spectrum. However, in LBT, the lack of coordination with other existing systems brings challenges in sustaining performance in the coexistence of LAA and WiFi networks. Specifically, the hidden node problem (HNP) and exposed node problem (ENP) cannot be effectively handled when both LAA and WiFi nodes attempt to access the unlicensed spectrum at the same time. Thus, transmission failure might occur and the network performance would be degraded. In order to mitigate the influences caused by HNP and ENP, based on LBT, we firstly analyze HNP and ENP by means of mathematical approach. The analytical results surprisingly reveal that the hidden node and exposed node probabilities are as high as 41% and 39.33%, respectively. Then, a Give And p-persistent Take (GAT) mechanism with the Listen-Before-Receive (LBR) procedure, namely LBR-GAT, is proposed to cope with LBT to reduce the collision caused by HNP as well as to retrieve the bandwidth sacrificed by the ENP. With LBR-GAT, the LAA sender conditionally gives up or takes back transmission opportunities, and thus the unlicensed spectrum could be efficiently shared between LAA and WiFi. Evaluation results show that the proposed LBR-GAT could conditionally obtain better network performance comparing to legacy LBT.

Market Competition Between LTE-U and WiFi

LTE-U is an extension of the Long Term Evolution (LTE) standard for operation in unlicensed spectrum. LTE-U differs from WiFi, the predominant technology used in unlicensed spectrum in that it utilizes a duty cycle mode for accessing the spectrum and allows for a more seamless integration with LTE deployments in licensed spectrum. There have been a number of technical studies on the co-existence of LTE-U and WiFi. In this paper, we instead investigate the impact of such a technology from an economic perspective. We consider a model where an incumbent service provider (SP) deploys a duty cycle-based technology like LTE-U in an unlicensed band along with operating in a licensed band and competes with one or more entrants that only operate in the unlicensed band using a different technology like WiFi. We characterize the impact of a technology like LTE-U on the market outcome and show that the welfare impacts are subtle, depending in part on the amount of unlicensed spectrum and number of entrants. The difference in spectral efficiency between LTE and WiFi also plays a role in the competition. Finally, we investigate the impact of the duty cycle and the portion of unlicensed spectrum used by the technology.

Enhanced Collision Resolution Methods With Mini-Slot Support for 5G NR-U

To improve the capacity of cellular systems without additional expenses on licensed frequency bands, the 3GPP consortium has designed cellular technologies that use the unlicensed spectrum. The important peculiarity of the usage models of these technologies, the latest of which is New Radio Unlicensed (NR-U), is the coexistence with Wi-Fi networks deployed in the same frequency band. That is why NR-U uses channel access methods similar to those of Wi-Fi. However, the performance of Wi-Fi networks notably deteriorates in coexistence scenarios. One of the reasons is the slotted structure of transmissions of cellular base stations in the unlicensed spectrum, which may imply the use of a reservation signal. The paper proposes two novel channel access methods for NR-U, in which an NR-U base station randomly stops sending the reservation signal to listen to the channel to detect and resolve collisions. Analytical models are developed to validate the proposed methods and evaluate their efficiency, taking into account important features of NR-U networks, such as flexible numerology and mini-slot transmissions. The obtained numerical results show that the proposed methods significantly improve the performance of the Wi-Fi or NR-U network without degradation of the throughput of the other technology in coexistence scenarios.

A Survey on the AI and Spectrum Management for Cache‐Enabled Internet of Things in Smart Cities

In this paper, we provide a comprehensive survey for the artificial intelligence and spectrum management, which are used for cache‐enabled Internet of Things (IoT) in smart cities. In smart cities, there emerge a lot of new applications such as data collection and communication, environment monitoring, and real‐time processing, which cannot be supported by the conventional wireless transmission techniques. Hence, some new wireless transmission techniques should be developed to support the emerging applications in smart cities. In this survey, we focus on the artificial intelligence, spectrum management, and caching techniques, where the interference arises due to the limited spectrum resources. In particular, we first review the current research status of these new techniques and, then, give some challenges on the system design. We further provide several feasible solutions on these challenges, in order to implement the IoT networks in smart cities. Finally, we conclude the work in the part of conclusions and give some discussions on the future works.