71results about How to "Address resource allocation" patented technology

The invention provides a resource allocation method, device and terminal for D2D communication. The resource allocation method for D2D communication comprises the first step of determining whether the relay function for D2D communication needs to be provided for other terminals or not, the second step of configuring transmission resources for D2D communication to the other terminals when it is determined that the relay function for D2D communication needs to be provided for other terminals so that the other terminals can conduct D2D communication through the transmission resources. By means of the technical scheme, an auxiliary link for D2D communication can be fully utilized to conduct connection, so flexible configuration on the transmission resources of the other terminals is achieved, and the problem of allocation of the resources for D2D communication is effectively solved.

The invention discloses a D2D resource allocation method based on multi-agent deep reinforcement learning, and belongs to the field of wireless communication. The method comprises the following steps:firstly, constructing a heterogeneous network model of a cellular network and D2D communication shared spectrum; establishing a signal to interference plus noise ratio (SINR) of a D2D receiving userand an SINR of a cellular user based on the existing interference, respectively calculating unit bandwidth communication rates of a cellular link and a D2D link, and constructing a D2D resource allocation optimization model in a heterogeneous network by taking the maximum system capacity as an optimization target; For the time slot t, constructing a deep reinforcement learning model of each D2D communication pair on the basis of the D2D resource allocation optimization model; And respectively extracting respective state feature vectors from each D2D communication pair in the subsequent time slot, and inputting the state feature vectors into the trained deep reinforcement learning model to obtain a resource allocation scheme of each D2D communication pair. According to the invention, spectrum allocation and transmission power are optimized, the system capacity is maximized, and a low-complexity resource allocation algorithm is provided.

The invention discloses a resource distribution system based on service features under a distributed cloud architecture and a method thereof. The method comprises the following steps: (1) establishing a cloud resource distribution system based on service features; (2) establishing an updating mechanism of a data center state list; (3) making a cloud user submit a cloud resource request to a resource dispatcher; (4) selecting to carry out step (5) or step (6) according to the service feature of the cloud user; (5) deploying a virtual machine for the cloud user by adopting a centralized way; (6) deploying a virtual machine for the cloud user by adopting a distributed way; (7) finishing the distribution of the virtual machine, and feeding back a distribution result to the cloud user through a resource manager. The resource distribution system mainly aims to solve the problem of resource distribution of cloud user services with different service features in a distributed cloud computing environment.

The invention discloses an initial configuration method and an initial configuration device for a wireless backhaul link. The method comprises the following steps of: starting an access node, and reporting information to a base station by using an original authorized frequency spectrum; and reconfiguring a wireless backhaul link between the base station and the access point according to a TVWS (Television White Spectrum) resource distributed by a system by using the base station, and switching the wireless backhaul link onto the distributed TVWS resource. According to the method and the device, the resource distribution problem of the wireless backhaul link is solved, and the volume or service quality of a wireless access link is not influenced simultaneously.

The invention discloses a flow control method and a flow control system in an SDN network and relates to the field of communication technology. An application module in the system determines average KQI indexes and weight of all kinds of services. An SDN controller gathers the latest KPI data, calculates a forward table, and monitors a transfer process in real time. An OpenFlow switch reports link flow information, and forwards data according to the sent down flow table. A QoE feedback module establishes mapping relation between KPI and KQI. Flow control is performed through the feedback information of the QoE. With application of the flow control system and the flow control method, through real-time QoE information of users, the problem of network congestion can be found in time, and therefore forward strategies can be dynamically adjusted; link load can be effectively mitigated; the stable performance of the application is maintained; and efficient network operation is ensured.

The invention discloses a cloud computing resource scheduling system and method. The cloud computing resource scheduling method includes that building a resource distribution model according to task number and resource number, and defining a resource distribution matrix, a resource scheduling time matrix and a scheduling target; generating codes of an initial population according to a code rule; computing the fitness value of the population according to fitness functions; selecting a dominant population according to the fitness value; updating the population through learning factors, and generating the next generation of new population according to the updating result; judging whether conforming to stop conditions, if so, stopping, and obtaining the optimal solution; otherwise, returning to compute the fitness value; decoding the optimal solution according to a decoding rule to obtain the resource distribution result. The cloud computing resource scheduling system and method are capable of well solving the cloud computing resource distribution problem.

The invention discloses an Internet of vehicles (IoV) data transmission scheduling method based on channel prediction. Large-scale channel information between vehicles and roadside enter units and between vehicles is predicted by using a channel prediction algorithm according to collected vehicle state information, a signal to noise ratio and communication benefit of a potential communication link are computed, and a relay vehicle node set is selected to scheduling data distribution by using a greedy algorithm. According to the method, an effective vehicle node is selected as a relay node for distributing data by predicting the channel fading in large scale to achieve the maximum communication benefit; and in a central data distribution scene having the vehicles and the roadside center units, a throughput rate of the system is improved, and data distribution delay is reduced by reasonably allocating time resources.

The invention relates to a method for changing main machine QoS strategies of a cloud data center. The method comprises that a resource pool business load monitor which is arranged in the cloud data center monitors business load types and load conditions of all physical servers; when the fact that physical servers conform to changing conditions is monitored, proper new resource scheduling algorithms are selected for physical servers; changing commands which inform physical servers of adopting new resource scheduling algorithms are sent to physical servers; after physical servers receive changing commands, existing resource scheduling requests and new resource scheduling requests are sequenced and sectioned, wherein the first section part continues to adopt original resource scheduling algorithms for scheduling implementation, and the second section part adopts new resource scheduling algorithms for scheduling implementation. The invention also relates to a system for changing main machine QoS strategies of the cloud data center. By the aid of the method and the system, the problem of resource allocation caused by diversification of demands in cloud calculation can be effectively solved, and the online changing of QoS strategies can be achieved.

The invention discloses a resource allocation method used for a full duplex cellular network physical layer safe scene, aims at a full duplex cellular network where a plurality of upstream users, downstream users and eavesdroppers, adopts BGRA(Bipartite Graph Based Resource Allocation Scheme), constructs a bipartite graph according to a full duplex cellular network, converts the multi-user resource allocation problem into the bipartite graph maximum weight matching problem, an optimal subgraph is a subgraph with the sum of all edge weights being the largest, and through an acquired optical subgraph G' of a bipartite graph, reasonably allocates Resource Blocks (RBs) and power resources. The resource allocation method used for the full duplex cellular network physical layer safe scene can realize mutual cooperation of uplinks and downlinks in the full duplex system, thereby enhancing safety performance of the physical layer; and can also obtain quasi-optimal safety capacity of a full duplex cellular system with relatively low computation complexity, thereby achieving the purpose of improving the system safety capacity.

The invention relates to a digital-to-analog video transmission method based on superposition modulation coding. The method includes a video code sending process and a video receiving reconstruction process, and the video code sending process includes the following steps: an original video signal is subjected to hierarchical coding of base layer digital coding and enhancement layer analog coding, thereby correspondingly obtaining a digital channel coded signal and an analog coded signal, and the digital channel coded signal and the analog coded signal are superposed according to optimal allocation power based on a power constraint model and then sent. Compared with the prior art, the digital-to-analog video transmission method based on the superposition modulation coding makes full use of a power headroom in digital coded modulation to transmit information related with the enhancement layer, and reconstruction quality of video transmission is enhanced while the power utilization rate is improved.

The invention discloses a baseband processing resource allocation system under a C-RAN architecture. A baseband processing resource allocation method comprises the specific steps of 1 establishing the baseband processing resource allocation system under the C-RAN architecture; 2 establishing logic cell state monitoring processes; 3 enabling the logic cell state monitoring processes to send logic cell state information to a resource scheduler; 4 enabling the resource scheduler to calculate the optimum number of baseband processing virtual machines required by logic cells according to the logic cell state information; 5 enabling the resource scheduler to sequentially allocate baseband processing resources according to matching indexes; 6 enabling the resource scheduler to send allocation results to the logic cells after finishing the baseband processing resource allocation. The invention mainly aims at solving the problem of resource allocation after baseband processing resource centralization of multiple traditional cells under the C-RAN architecture, and the utilization rate of baseband processing resources is obviously improved.

The invention discloses a two-stage central cooperative frequency spectrum sharing mechanism based on a priority queue. A cooperative cognitive radio network comprises a pair of master users and a plurality of cognitive user pairs, wherein the master users assist with authorized communication through recruiting the cognitive users as relay, and through management cooperation, the cooperative cognitive users coordinate access of other uncooperative cognitive users, so that a two-stage central management structure formed by the master users and the cooperative cognitive users is formed. According to the two-stage central cooperative frequency spectrum sharing mechanism, through designing selection and updating of the cooperative cognitive users and data transmission control with different priorities, dynamic and highly-efficient frequency spectrum sharing of many types of nodes can be implemented under the condition of stronger business randomness.

The invention provides a heterogeneous cellular network D2D communication resource allocation method and system. The heterogeneous cellular network D2D communication resource allocation method comprises the steps: introducing a millimeter wave frequency band into a constructed heterogeneous cellular network for the communication of a D2D user, and constructing a cellular communication mode and a millimeter wave communication mode; calculating interference power, a signal-to-noise ratio and a transmission rate received by the cellular user and the D2D user in different communication modes, andestablishing a utility function of the D2D user according to the interference power, the signal-to-noise ratio and the transmission rate; and under the condition that the service quality requirementsof the cellular user and the D2D user are met, selecting a communication mode and a channel of the D2D user by utilizing the constructed deep reinforcement learning model and taking the utility maximization of the D2D user as a target according to the current state of the D2D user. The heterogeneous cellular network D2D communication resource allocation method more intelligently solves the resource allocation problem of D2D user communication, effectively reduces the network communication overhead, maximizes the user utility, and improves the overall performance of the network.

The invention provides a double-layer heterogeneous network spectrum allocation method based on a quantum monarch butterfly optimization mechanism. The method comprises the steps: establishing a double-layer heterogeneous network system model; obtaining an integer coding position of monarch butterflies; calculating fitness values of all the monarch butterflies to obtain a globally optimal quantumposition and a globally optimal position corresponding to the globally optimal quantum position; sorting the monarch butterfly population, and dividing the monarch butterfly population into two monarch butterfly sub-populations; updating the transition quantum position of each monarch butterfly individual in the sub-populations; combining the two newly generated sub-populations into a new transition population, updating the quantum position of the monarch butterfly population, calculating the fitness value of the quantum monarch butterflies, and updating the global optimal quantum position andthe global optimal position; whether the maximum iteration frequency is reached or not is judged, if the maximum iteration frequency is reached, outputting a global optimal quantum position and a global optimal position, wherein the global optimal position is the optimal scheme of spectrum allocation; and otherwise, adding 1 to the number of iterations, and returning to carry out a new round of iteration. According to the invention, the spectrum allocation problem of the integer discrete optimization double-layer heterogeneous network is solved.

The invention discloses an edge container resource allocation method based on deep reinforcement learning, which comprises an Actor network, a Critic network and an echo state network (ESN), and effectively solves the problem of container resource allocation of an edge computing environment of time delay sensitivity application. According to the resource allocation method provided by the invention, an end-to-end time delay model is established on the basis of an M / D / 1 queuing model to obtain end-to-end time delay of a service flow s data packet on a container n, and a deep reinforcement learning model is adopted to solve the problem of edge container resource allocation. An ECA3C network is obtained by improving a traditional A3C algorithm through an echo state network (ESN), a resource allocation strategy At is allocated to different container clusters Sz and t in a resource pool z and used for solving the problem of container resource allocation in an edge computing environment, andthe ECA3C network changes an end-to-end delay reward value rt to obtain an end-to-end delay reward value rt. Therefore, the method is suitable for various edge computing environments.

The invention provides a satellite electronic system architecture suitable for on-orbit dynamic configuration. A satellite electronic system is divided into six areas: an integrated management controlarea, which is responsible for the most basic management and control function of a satellite; an interface driving area, which is formed by a sensor access module and a driver module of an executionmechanism; a data storage area, which is formed by a bulk memory having file management capability and is responsible for data storage; a data processing area, which is formed by a plurality of same-configuration high-performance processor modules, wherein each processor module can load and remove application waveforms as needed, thereby realizing dynamic configuration of its function; and a radiofrequency comprehensive area and an optical frequency comprehensive area, which are connected with a microwave antenna array and an optical antenna array respectively. The provided satellite electronic system architecture suitable for on-orbit dynamic configuration can be suitable for change in application scenarios and on-orbit dynamic function change, thereby realizing a one-satellite multi-purpose effect, and improving satellite resource utilization rate.

The invention relates to a contract-based bandwidth and power combined optimization cooperative spectrum sharing method. In the method, an authorized user and a cognitive user perform cooperation according to a contract; the cognitive user capable of correcting decoding a signal of the authorized user designs the type of the cognitive user itself according to the conditions, such as a channel, the power, the power consumption cost and the rate of the cognitive user itself, and tells the authorized user of the type; the authorized user designs a corresponding contract for the cognitive user according to the type provided by the cognitive user; and the cognitive user performs communication according to the contract provided by the authorized user, sends the self signal to a receiving end of the cognitive user by using a part of bandwidth in the second time slot, and then, assists to forward the signal of the authorized user to a receiving end of the authorized user by utilizing the rest bandwidth. By means of the contract-based bandwidth and power combined optimization cooperative spectrum sharing method disclosed by the invention, the problem that the resource distribution calculation complexity in the game theory method is high can be effectively solved; the possible cheating phenomenon of the cognitive user can be effectively avoided; and the user utility is improved easily.

The invention discloses a millimeter wave communication system multi-beam multi-user resource allocation method, and belongs to the technical field of fifth-generation mobile communication. Accordingto the method, a plurality of wave beams are deployed on a base station in a same-frequency mode, a spatial domain is equally divided into a plurality of areas, and each wave beam rotates in the corresponding area so as to cover the area and serve users in the area; and in each scheduling time slot, to-be-allocated users of the beams in the first area are determined firstly, and then to-be-allocated users of the beams in the next adjacent region are determined in sequence. According to the method, a beam resource utility function is constructed on the basis of differentiated service requirements, user fairness, time delay and user cache, the service quality of the network can be effectively represented, and a multi-user multi-beam resource allocation optimization model is constructed by taking maximization of the utility of total network users as a target function. According to the method, efficient distribution of multiple beams is realized through two processes of beam selection anduser scheduling, and the service quality of the network can be effectively improved.

The invention relates to a method for realizing intelligent cloud management based on knowledge driving. The method comprises the following steps: collecting structured data, semi-structured data andunstructured data through a multi-cloud management platform; cleaning, converting and storing the data, performing centralized management on the data, and performing multi-cloud management operation and maintenance knowledge extraction; performing multi-cloud management knowledge fusion; and performing knowledge retrieval on the trained knowledge, and applying the knowledge retrieval method to various scenes of multi-cloud management. The method and the system for realizing intelligent cloud management based on knowledge driving are adopted, multi-cloud management is used as a main scene, an offline cloud management knowledge learning technology and a real-time intelligent cloud management technology are researched, the knowledge-driven intelligent cloud management system is constructed and used for solving the core problems of multi-cloud management platform resource allocation, intelligent operation and maintenance faults, alarms, root causes and the like, providing domain knowledgeservices and forming frontier application and intelligent practice based on multiple technical fields of cloud computing, artificial intelligence, big data and the like.

The invention discloses a virtual network resource allocation method with high reliability and load balance. A vEPC and a network link is included, and the vEPC virtualizes basic hardware equipment into universal network resources; according to the resource allocation method, basic hardware resources in universal network resources are used as an underlying network, the virtual service carried by the underlying network is called as a virtual network; the basic hardware resources in universal network resources are used as the underlying network, the underlying network is divided into communitiesand the communities are optimized; the underlying network and the virtual network are sorted; and finally, resources are allocated to the virtual network. The method comprises two processes of allocating resources to virtual nodes and allocating resources to virtual links, load balance and reliability are guaranteed when the resources are allocated to the virtual nodes, bandwidth requirements ofa virtual network are guaranteed when the resources are allocated to the virtual links, and the problem of large-scale virtual network resource allocation of a 5G core network is effectively solved.

The present invention discloses a transmission method of time division duplex wireless communication system. The base station and the terminal use a superframe as a unit for data transmission. In the time length of one superframe, the base station transmits a predecessor to the terminal and transmits downlink data to the terminal through 12 downlink physical frames, and the terminal transmits uplink data to the base station through 12 uplink physical frames. The invention also discloses a frame structure of time division duplex wireless communication system. The frame structure is the superframe which is composed of one predecessor and 24 physical frames. The 24 physical frames are divided into downlink physical frames and uplink physical frames according to the number ratio of 1:1 between the downlink physical frames and the uplink physical frames. The solution of the invention settles the problem for dividing uplink and downlink of PHY frames, and simultaneously settles the problems of resource allocation, time delay, guard interval, etc.

The invention discloses a low earth orbit satellite mobile communication resource intelligent scheduling method. The method comprises the following steps: S1, initializing deep learning network parameters; s2, inputting prior low-orbit satellite sample data, and fitting the sample data by using a deep learning network training method; s3, analyzing the sample deviation, if the fitting deviation isgreater than 0.01, indicating that the existing deep learning network belongs to an under-fitting state, and turning to the step S2; otherwise, entering the step S4; s4, low-orbit satellite sample data is collected again, and state marking is carried out to serve as a test sample; s5, analyzing the variance of the sample, if the fitting variance is greater than 0.005, indicating that the currentsample is in an over-fitting state, performing regularization processing on the deep learning network, and turning to the step S2 after the processing is completed; otherwise, entering the step S6; s6, sample data needing to be predicted of the low-orbit satellite is input, and a low-orbit satellite distribution network resource strategy is obtained. And intelligent scheduling of low-orbit satellite mobile communication resources can be better realized.

The invention provides a resource transfer method and device, a server and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: when resource transfer information is received, generating a certificate according to the resource transfer information, and storing the certificate; when resources are allocated to the resource transfer object, obtaining at least one stored certificate, and determining at least one resource transfer object corresponding to the resource transfer scene according to a scene identification code carried by the certificate; for each resource transfer object corresponding to the resource transfer scene, determining a resource transfer rule corresponding to the resource transfer object; and transferring resources tothe resource transfer object according to the resource transfer rule and the certificate of the at least one piece of resource transfer information. According to the invention, resources can be directly transferred to the resource transfer objects according to the resource transfer rules corresponding to different resource transfer objects, the problem of resource allocation between the resourcetransfer objects corresponding to the resource transfer scene can be quickly solved, and the resource transfer efficiency is improved.

The invention discloses an intermediate frequency efficiency optimization method for an energy acquisition D2D heterogeneous network. The method mainly comprises the following steps: 1, constructing amathematical model of energy acquisition D2D heterogeneous network optimization frequency efficiency; 2, simplifying frequency efficiency optimization energy to obtain a mathematical model of the D2Dheterogeneous network; 3, converting a multi-objective optimization problem into a single-objective optimization problem; 4, solving a frequency efficiency optimization problem when alpha is greaterthan 0 based on a convex optimization theory; 5, solving a frequency efficiency optimization problem when alpha is equal to 0 based on a convex optimization theory; and 6, solving a frequency efficiency optimization problem based on a gradient descent method. On the premise of ensuring QoS of CU users, the optimization problems of channel allocation, transmission time allocation and power allocation in an energy acquisition D2D heterogeneous network are solved, and the spectral efficiency and the energy efficiency of the system can be maximized at the same time.

The invention discloses an environmental backscatter communication system information transmission method with optimal energy efficiency. A communication system relates to the technical field of Internet of Things communication, comprises a multi-antenna source end node, a single-antenna sensor relay node, a single-antenna electronic tag and an information user node, and performs joint optimization on transmitting power, a transmitting antenna and a power distribution factor in the system to obtain an optimal solution, namely an information transmission method with optimal energy efficiency. The method has the advantages that under the energy efficiency maximization criterion, the collected energy can be used for realizing environment backscatter communication long-distance efficient transmission of the future Internet of Things, and compared with a traditional method, environment reflection communication of large-scale sensors / labels is achieved, the system energy efficiency is greatly improved, and the bit error rate is reduced.

The invention discloses a networking radar resource allocation method based on mutual information maximization, and relates to the technical field of radars. The method comprises the steps of determining a networking radar model of a networking radar system, and carrying out the initialization of the networking radar model; determining an extended state vector and an extended state transition equation of each target at the current fusion moment; determining an extended state prediction value of each target at the first fusion moment; in the current fusion time interval, observing each target by a preset number of radars, and obtaining an asynchronous measurement value of each target in the current fusion time interval; and sending the asynchronous measurement value to a fusion center so as to enable the fusion center to determine an optimal solution of a resource allocation vector of each target at the first fusion moment after calculating a target tracking information measurement function of each target at the first fusion moment. According to the method, the defect that the objective function is relatively large in computation burden is overcome, and the real-time requirement is met while the resource allocation problem of the heterogeneous networking radar system is solved.

The invention discloses a resource allocation method of a distributed wireless energy and information transmission system. Finally, on the premise that the total transmission delay is minimized and the signal-to-noise ratio requirement is met, the resource optimization problem in the wireless energy simultaneous transmission scene is solved. At a user side, bit data signals are uniformly sent to an RRH side for processing in a time division multiplexing sending mode; at an RRH end, firstly, energy is sent to a user through a beam forming technology, and then received bit data signals are uniformly sent to a cloud end for processing after being subjected to spatial filtering and a k-nearest neighbor algorithm; and at the cloud, diversity reception is carried out on the received data information and the data information is finally sent back to the user side. In the spatial filtering, the received user signals are processed by using a vector quantization method, so that the task calculation efficiency is improved.

The invention relates to a nonvolatile Boolean logic operation unit, a nonvolatile Boolean logic operation method and a nonvolatile Boolean logic operation device. The nonvolatile Boolean logic operation unit comprises a diode and a phase change storage unit; the positive electrode of the diode serves as a first pulse signal input end to receive a first pulse signal, the negative electrode of thediode is connected to one end of the phase change storage unit, the other end of the phase change storage unit serves as a second pulse signal input end to receive a second pulse signal, and a corresponding operation method is designed to achieve logic operation. Compared with the prior art, according to the invention, the nonvolatile Boolean logic enables the calculation unit and the storage unitto be fused; the data processing process is put into the storage unit, and the calculation result is directly stored in the phase change storage unit, so that the performance and energy consumption problems caused by frequent data carrying are relieved, the logic operation steps are simplified, the time required for data transmission is shortened, and the calculation and storage integrated operation is realized.

The invention provides a carrier resource dynamic adjustment method and a carrier resource dynamic adjustment device based on carrier aggregation. The method comprises the following steps: determining distribution and utilization information of all physical resource blocks PRB in a carrier; and adjusting the number of the PRBs in the carrier distributed to primary cell user equipment UE taking a current cell as a primary cell according to preset rules corresponding to the distribution and utilization information so as to enable at least one piece of secondary cell UE taking the current cell as a secondary cell to use the PRBs in the carrier not distributed to the primary cell UE after adjustment, wherein the preset rules include rules set according to a preset heavy load decision threshold LC_Threshold, the secondary cell UE is UE supporting carrier aggregation, and LC_Threshold belongs to [0, 1]. By using the method, the problem of resource distribution under carrier aggregation is solved, the number of times of carrier resource dynamic adjustment is reduced, packet loss of retransmission data is avoided, the frequency resources of UE taking the carrier as a primary cell are first guaranteed, and the performance of UE is ensured to the greatest extent while effective utilization of frequency resources is achieved.

The invention discloses a D2D resource allocation method based on multi-agent deep reinforcement learning, and belongs to the field of wireless communication. The method comprises the following steps:firstly, constructing a heterogeneous network model of a cellular network and D2D communication shared spectrum; establishing a signal to interference plus noise ratio (SINR) of a D2D receiving userand an SINR of a cellular user based on the existing interference, respectively calculating unit bandwidth communication rates of a cellular link and a D2D link, and constructing a D2D resource allocation optimization model in a heterogeneous network by taking the maximum system capacity as an optimization target; For the time slot t, constructing a deep reinforcement learning model of each D2D communication pair on the basis of the D2D resource allocation optimization model; And respectively extracting respective state feature vectors from each D2D communication pair in the subsequent time slot, and inputting the state feature vectors into the trained deep reinforcement learning model to obtain a resource allocation scheme of each D2D communication pair. According to the invention, spectrum allocation and transmission power are optimized, the system capacity is maximized, and a low-complexity resource allocation algorithm is provided.