CN114980339B - C-V2X multi-service downlink resource allocation method based on variable time slot scheduling - Google Patents
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Abstract
The invention discloses a C-V2X multi-service downlink resource allocation method based on variable time slot scheduling, which is applied to the technical field of mobile communication and aims at solving the problem that the prior art is difficult to ensure the service transmission service quality of a plurality of different demands under the scene of dynamic change of the Internet of vehicles; the invention aims at maximizing the service quality of low priority service transmission, adopts a preemption mode to allocate downlink channel resources, determines the pre-allocation and preemption strategy of the time-frequency resources based on DDPG algorithm according to the service arrival condition and the residence time of the vehicle in the cell, and improves the successful sending probability of eMBB service under the constraint of URLLC service delay threshold.
Description
Technical Field
The invention belongs to the technical field of mobile communication, and particularly relates to a time-frequency resource joint scheduling technology in the Internet of vehicles.
Background
In recent years, with the development of 5G internet of vehicles, realization of advanced applications such as automatic driving and remote driving has become possible. At the same time, however, the network resource scheduling needs to ensure millisecond-level delay requirements of strong real-time services such as automatic driving. In addition, in the case of large-scale vehicle access, network resource starvation is likely to cause channel congestion. The traditional resource scheduling strategy is difficult to meet the standard of the novel Internet of vehicles use case, the intelligent Internet of vehicles resource scheduling strategy which is more flexible and efficient in design has important significance for improving the performance of the 5G Internet of vehicles, and simultaneously, more challenges are brought to researchers.
The 3GPP introduced in Rel-16 a V2X standard, namely NR V2X, based on a 5G new air interface (5G New Radio,5G NR). The new features in NR provide some useful solutions. In 5G NR, different slot formats may be supported according to the setting of the parameter set. There have been some studies demonstrating the role of variable slot formats in cellular networks, but there have been few studies to optimize the internet of vehicles resource scheduling strategies based on NR new technology.
The Internet of vehicles has various services with different requirements, such as high-flow services such as sensing information sharing, and the like, and needs to occupy high bandwidth for a long time; the key information of delay in security alarm and the like needs to ensure extremely low delay. If a traditional slicing strategy is adopted to reserve channels for safety information, due to randomness and sparsity of alarm information arrival, the channels can be idle and wasted for a long time, and the time-frequency resource utilization efficiency can be maximized only by ensuring the time delay requirement of key service and simultaneously enabling more channels to be used for the transmission of large-flow service. Meanwhile, vehicle users in the Internet of vehicles scene have mobility, stay time in the same cell is limited, and if the vehicle stay time can be considered when a resource allocation strategy is designed, and the transmission completion of the service in the same cell is ensured, unnecessary connection interruption or switching can be reduced. In summary, how to organically combine the NR new technology with the environmental characteristics of the internet of vehicles, and design an intelligent resource scheduling policy more conforming to the characteristics of the internet of vehicles, further research is required.
Disclosure of Invention
In order to solve the technical problems, the invention provides a C-V2X multi-service downlink resource allocation method based on variable time slot scheduling, which aims to maximize the probability of successful transmission of eMBB service with low priority under the condition of ensuring URLLC service successful transmission, adopts an allocation-preemption mode to allocate downlink channel resources for eMBB service and URLLC service with high priority respectively, and dynamically performs a pre-allocation and preemption strategy of time-frequency resources under the constraint of a service delay threshold according to service arrival condition and residence time of vehicles in a cell, so as to reduce the probability of interruption of service transmission.
The invention adopts the technical scheme that: the C-V2X multi-service downlink resource allocation method based on variable time slot scheduling divides a road into a plurality of intervals according to the coverage area of a base station;
For communication between the vehicle and the base station in the cell, at the beginning of each time slot, if there is no URLLC traffic to be processed, the whole time slot is allocated to eMBB traffic, and when URLLC traffic arrives during the transmission of eMBB traffic, the resources allocated to eMBB traffic are preempted on the time scale of the ultrashort time slot.
Further comprises: the residence time of a vehicle user in the cell is monitored in real time, the probability that eMBB or URLLC service can finish transmission in the cell is estimated according to the residence time of the user, the data volume requirement of eMBB or URLLC service and channel resources, an optimization problem is constructed by maximizing eMBB service successful transmission probability and URLLC service successful transmission probability, and an optimal eMBB service resource allocation strategy and URLLC service resource preemption strategy are obtained by solving the DDPG algorithm.
The invention has the beneficial effects that: time-frequency resources are allocated for eMBB traffic and URLLC traffic at different slot granularities, the time-frequency resources are pre-allocated in each slot to maximize the transmission rate of eMBB traffic, and resource preemption is performed at the scale of the ultrashort slots to meet the delay threshold of URLLC traffic that arrives at random. The mobility of the vehicle and the residence time of the vehicle user in the cell are considered in the resource pre-allocation and the preemption, and finally, the optimal strategy of the pre-allocation and the re-allocation is determined based on DDPG algorithm, so that the successful sending proportion of eMBB service can be improved on the premise of meeting the QoS requirement of URLLC service, and the QoS improvement of eMBB service is more remarkable in the scene that the vehicle is far away from the base station; the method of the invention comprises the following advantages:
1. The invention inserts the temporary short message in the time slot of the data transmission, has no extra interference and only needs extra control message. At the beginning of each slot, if there is no URLLC traffic to process, then the entire slot is allocated to eMBB traffic, and full-load transmission maximizes throughput. When URLLC service arrives in eMBB service transmission process, the resources allocated to eMBB service are preempted in the time scale of the ultrashort time slot, so as to ensure the real-time performance of the key service.
2. The cell will monitor the stay time of the vehicle user in the cell in real time. And estimating the probability of successful transmission of the service according to the residence time of the user and the data volume of the service. Through the special design of the reward function, the policy obtained by the DDPG algorithm tends to sacrifice the transmission rate of a part of services which are difficult to complete transmission in the cell, so that more users can complete data transmission before leaving the cell, connection interruption is reduced, and the successful sending probability of eMBB services is improved.
Drawings
Fig. 1 is a schematic flow chart of a C-V2X multi-service downlink resource allocation method based on variable slot scheduling according to the present invention;
FIG. 2 is a schematic diagram of a scenario of a C-V2X multi-service downlink resource allocation method of the present invention;
fig. 3 is a schematic diagram of preemptive resource scheduling at different slot granularity according to the present invention.
Detailed Description
In order to facilitate understanding of the present invention by those of ordinary skill in the art, the following definitions are first made for technical terms involved in the present invention:
Cell: the base station covers an area in which a vehicle user can reliably communicate with the base station over a wireless channel.
Ultra-short time slots: the 5G NR allows the transmission of delay sensitive information to begin with the duration of one slot, and the scheduling period (e.g., 2 OFDM symbols) at this time is called an ultrashort slot.
Time-frequency resource block: the traffic is transmitted on the sub-band for a minimum scheduling period time, which is called being allocated a resource block.
Vehicle user stay time: from the current time to the time the vehicle is driving out of the cell service range.
Service remaining service time: and the current stay time of the vehicle user corresponding to the V2I service.
Successful transmission: and measuring the index of the service transmission service quality. For URLLC services, the transmission is completed in an ultra-short time slot; for eMBB traffic, this refers to completing the transmission before the vehicle leaves the cell.
The following is a further explanation of the present invention in conjunction with fig. 1-3, in order to facilitate the understanding of the technical content of the present invention by those skilled in the art.
As shown in fig. 1, the multi-service downlink resource allocation method based on variable time slot scheduling in the scene of internet of vehicles of the invention comprises the following steps:
s1, acquiring the position of a base station and the position and speed information of a vehicle user, and generating and recording the running track of the vehicle and the stay time in a cell;
s2, acquiring environment information, and calculating the gains of all channels from the vehicle users to the base station in each time slot according to the position information of the base station and the vehicle users in the S1;
S3, periodically acquiring service information, and generating and maintaining a service queue matrix;
And S4, modeling a preemptive resource allocation strategy, and under the condition of ensuring URLLC service instantaneity, taking the ratio of maximizing eMBB service transmission in a cell as a target, and providing an optimization problem model.
S5, constructing a Markov process based on the model in the step S4;
S6, training the reinforcement learning model by utilizing DDPG algorithm.
In step S1, in order to reduce the amount of calculation, the present invention divides the cell road into discrete areas and numbers them, periodically acquires the arrival vehicle information, and represents the travel track of the vehicle in the cell as a time series of recording area numbers.
The step S1 specifically comprises the following sub-steps:
s11, as in fig. 2, the road is divided into discrete areas and numbered 1, 2. Assuming that the channel parameters within each region remain unchanged;
S12, using the time slot as a time interval, and representing the movement track of the vehicle user by using the time sequence of the recorded area numbers.
In the step S12, the number 0 indicates that the vehicle has not entered or has traveled out of the cell range.
In step S2, the channel gains of the respective areas to the base station are calculated according to the division of the road areas in step S1.
Step S2, a region association matrix is calculated according to the channel parameters, the channel gain from each region to the base station is represented, and the spectrum efficiency is obtained as follows:
Where phi u,t denotes the bandwidth occupied by user u in time slot t, p is the base station transmit power, h u,t is the small-scale channel fading factor, D d is the distance between road region D and the base station, α is the path loss index, and σ 2 is the random gaussian noise power.
In step S3, the cell scheduling center periodically collects service arrival information and maintains a service information queue matrix.
The step S3 specifically comprises the following sub-steps:
S31, the cell dispatching center collects current service requests at each dispatching moment; each piece of service information specifically includes: service sequence number, service arrival time, user number, service type, total data volume, delay threshold, remaining service time, actual transmission time, and remaining data volume.
S32, generating and maintaining a service queue matrix; the method specifically comprises the following sub-steps:
s321, the service number u arrived at present is sequentially recorded, and the service arrival time is recorded A user number m for initiating the request;
S322, recording QoS requirements of the service, including service type f= { B, u }, total data volume Delay threshold/>
S323, obtaining the residual service time of the business according to the vehicle track information in the step S1Remaining stay time of the current vehicle in the cell;
s324, setting the actual transmission time as the service arriving at the current moment The residual data quantity is set as
S325, updating the actual transmission time of the service arrived before the current time if the residual data amount is not 0For/>Wherein t 0 is a scheduling period while updating the remaining data amount/>
In step S4, as shown in fig. 3, at the beginning of each slot, the system pre-allocates a resource block to eMBB traffic in the current traffic queue. Meanwhile, to ensure that the ultra-low latency requirement of URLLC traffic can be met, when URLLC traffic arrives in the eMBB traffic transmission process, the resources allocated to eMBB traffic are preempted on the time scale of the ultra-short time slot.
The step S4 specifically comprises the following sub-steps:
S41, representing a resource scheduling strategy through a resource block allocation matrix. The resource scheduling of eMBB traffic is periodic with time slots, and the resource scheduling of URLLC traffic is periodic with ultrashort time slots. If the current moment is at the beginning of the time slot, pre-distributing the resource blocks in the time slot to the business in the queue, and defining a resource block distribution matrix The proportion of eMBB users u B to the resource blocks in slot t is shown. The allocated bandwidth for user u B can be expressed as
In the pair ofRounding downwards to ensure that the obtained resource blocks are integers, wherein W represents the number of sub-channels in the system, and b represents the bandwidth of the sub-channels;
S42, if URLLC service arrives currently, whether the service is at the time of starting time slot or not, the resources allocated to eMBB service are directly preempted. Defining a preemption matrix The preemption bandwidth is that if the ratio of the user u L to the user u B resource block is preempted in the ultra-short time slot k
S43, calculating URLLC transmission rate of service, and obtaining total bandwidth of service u L in time slot k as follows
The transmission rate can be expressed as
S44, calculating eMBB data rate of service, u B lost bandwidth is
Assuming that the transmission rate in the system accords with the linear loss model, the u B transmission rate is
S45, in order to balance the QoS requirements of various services, under the condition of ensuring URLLC that the service is successfully transmitted, the optimization problem is put forward by taking the maximum proportion of eMBB service to successful transmission in a cell as a target.
The main objectives of eMBB traffic and URLLC traffic are to complete the transmission before the vehicle leaves the cell and to ensure that the transmission is completed in an ultra-short time slot, respectively. Defining the successful sending probability of two services as
Wherein,Representing the remaining service time of service u B,/>The residual data quantity is represented by a very small constant, and the value is generally one thousandth. /(I)The value of (2) represents the proportion of data that the service can be transmitted before the vehicle leaves the cell, according to the current transmission rate, so as to estimate the probability of the service being transmitted before the vehicle leaves the cell. /(I)Representing the size of the demand traffic arriving at user u L in ultrashort slot k,/>It indicates URLLC that the traffic can be completed within the delay threshold.
In order to balance the QoS requirements of various services, the optimization target is set to maximize the successful sending probability of two groups of users at the same time, and the optimization problem is as follows
Wherein constraint 2 allocates a resource block number limit for a single service; constraint 3 is the allocation limit of the total resource blocks of the system; constraint 4 is limited by the number of preemptive resource blocks per eMBB users and K t represents the set of all ultrashort slots in the t slots.
The optimization target is written into the logarithmic form, so that the influence of partial QoS (quality of service) oversatisfaction on the optimization target can be reduced, and the fairness of each service is further ensured. The optimization problem can be written as
Wherein M is a large number, and generally takes a value of 1000. Only all URLLC services are successfully transmitted, ensuringHas the following componentsOtherwise a large penalty is obtained. And after the successful sending probability function of eMBB services is written in a logarithmic form, the services finished in advance at a high speed can not have excessive influence on the optimization target, so that more services can be sent successfully as much as possible.
In step S5, the problem in step S4 described above is modeled as a markov decision problem.
The step S5 specifically comprises the following sub-steps:
s5 state space S: the decision is made once per time slot t, and the state of the system at the time of t can be expressed as
Wherein the method comprises the steps ofRepresenting a current channel state;
Action space a: since eMBB traffic u B for each preempted resource only concerns how many resource blocks are preempted and not which URLLC traffic is preempted, the parameters of the coverage matrix in step S42 Can be equivalently replaced byIs used to represent the ratio of the sum of the preempted resource blocks of traffic u B in the ultra-short slot k to the total resource blocks occupied by u B.
Same reasonAnd/>Or can be rewritten as
Then the action at time t may be defined as
Current prize r: at decision time t, when the action network observes state S t ε S, the action is implemented following policy μ (S t|θμ)The prize r is then received (s t,at), and the state transitions to s t+1. Then, according to the optimization problem in step S45, the bonus function may be defined as:
The logarithmic form of the reward function may enable the network to learn that the resulting strategy tends to send more traffic successfully than to get a portion of the traffic far beyond the required transmission rate.
The goal of the action network is to learn a set of actions that maximize long-term returnWherein η < 1 is a decay factor that balances current rewards with long-term rewards.
According to eMBB service u B successful transmission probability expressionWhen u B transmission in the cell is completed, the remaining data volume/>And taking the maximum value. Under the condition of ensuring URLLC service reliability,/>Positively correlated to the current prize.
In a scenario where the vehicle is far from the base station, i.e. is about to travel away from the cell range, it is difficult for the traffic to complete transmission within the cell. According to long-term return functionSuccessful delivery and higher prize values at this time will greatly increase the value of the long-term return function. When a plurality of eMBB services are in the queue at this time, a lower transmission rate is allocated to the service which has more residual data and is difficult to complete transmission through resource pre-allocation and preemption, otherwise, more resources are allocated to the service which is more likely to be successfully transmitted, and higher long-term return can be obtained.
Based on the above policies, neural networks thus tend to derive policies with higher probability of successful transmission. The step S6 specifically comprises the following sub-steps:
S61, inputting a current action network, a target action network, a current evaluation network and a target evaluation network, wherein parameters are respectively theta Q,θQ′,θμ,θμ, an attenuation factor eta, a soft update rate w, a sample set D with batch gradient descent, a maximum iteration number T, a random noise function Z, randomly initializing theta Q,θμ, enabling theta μ′＝θμ,θQ′＝θQ, and emptying an experience playback set M.
Initializing training round ep=0;
S62, initializing time t=0;
s63, executing action a t＝μ(st|θμ)+Zt according to the current state S t, and calculating rewards r (S t,at) and the next state S t+1;
S64, storing the training set (S t,at,r(st,at),st+1) into an experience pool M;
s65, judging whether T is less than T, wherein T is the total decision times of ep rounds, if so, t=t+1, returning to S63, and if not, entering S66;
S66, sampling D samples from the experience pool M, and sending the samples to the current Actor network, the target Actor network, the current Critic network and the target Critic network. The training set stored in the experience pool M is denoted by M (s t,at,r(st,at),st+1);
Wherein rank (m) represents the priority of the state transition matrix m, and alpha E [0,1] reflects the intensity of sampling bias to high priority;
S67, updating the current network parameter theta μ、θQ by minimizing a loss function;
S68, updating a parameter theta μ′ in the target Actor network by using a current Actor network parameter theta μ, and updating a parameter theta Q′ in the target cric network by using a current Critic network parameter theta Q;
And S69, judging whether the turns EP < EP are met, if so, ep=ep+1, returning to S62, and if not, ending the iteration to obtain the reinforced learning model after training is completed.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (3)
1. The C-V2X multi-service downlink resource allocation method based on variable time slot scheduling is characterized in that a road is divided into a plurality of areas according to the coverage area of a base station;
For communication between a vehicle and a base station in a cell, when each time slot starts, allocating the whole time slot to eMBB service, and when URLLC service arrives in the eMBB service transmission process, preempting the resources allocated to eMBB service in the time scale of the ultra-short time slot;
Further comprises:
the cell base station monitors the stay time of a vehicle user in a cell in real time;
Estimating the probability that eMBB service can complete transmission in a cell according to the residence time of a user, eMBB data volume requirements, URLLC service data volume requirements and channel resources, and judging whether URLLC service can complete transmission in an ultra-short time slot;
the optimization problem is constructed by taking the maximization eMBB of successful transmission probability of the service and the guarantee of successful transmission of URLLC of the service as a target; the optimization problem is expressed as:
s.t.C1:uB∈UB,uL∈UL
Wherein, Representing eMBB probability of successful transmission of service,/>Denote URLLC the probability of successful transmission of a service, C1 denote constraint 1, C2 denote constraint 2, C3 denote constraint 3, C4 denote constraint 4, U B denote eMBB service, U B denote eMBB service set, U L denote URLLC service, U L denote URLLC service set, M is a large number,/>The proportion of service allocated resource blocks is shown eMBB,Representing the ratio of u L to u B resource block in time slot K, K t represents the set of all ultrashort time slots in t time slots;
eMBB service successful sending probability calculation formula is:
Wherein, Representing u B transmission rate,/>Representing the remaining service time of service u B,/>A represents the residual data quantity, a is a very small constant;
URLLC service successful sending probability calculation formula is:
Wherein, Representing u L transmission rate,/>Indicating the size of the demand traffic arriving at user u L in ultrashort slot k;
modeling the optimization problem as a Markov decision problem;
based on DDPG algorithm, solving the Markov decision problem to obtain an optimal eMBB service resource allocation strategy and URLLC service resource preemption strategy.
2. The method for allocating C-V2X multi-service downlink resources based on variable slot scheduling according to claim 1, wherein,Calculated from the allocated bandwidth of u B, the bandwidth lost by u B, and the spectral efficiency of u B.
3. The method for allocating C-V2X multi-service downlink resources based on variable slot scheduling according to claim 2, wherein,Calculated from the allocated bandwidth of u L, the preempted bandwidth of u L, and the spectral efficiency of u L.
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| CN115665867B (en) * | 2022-09-28 | 2023-09-01 | 广东省新一代通信与网络创新研究院 | Spectrum management method and system for Internet of Vehicles |
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107889225A (en) * | 2016-09-29 | 2018-04-06 | 深圳市金立通信设备有限公司 | A kind of resource regulating method and equipment |
| KR20190113574A (en) * | 2018-03-27 | 2019-10-08 | 현대자동차주식회사 | Method and apparatus for performing communication using aggregated carriers in v2x(vehicle to everything) communication system |
| CN110769503A (en) * | 2018-07-26 | 2020-02-07 | 中国电信股份有限公司 | Uplink data multiplexing transmission method, device, terminal and storage medium |
| CN112888072A (en) * | 2021-01-25 | 2021-06-01 | 陕西师范大学 | eMBB and URLLC resource multiplexing method for guaranteeing service requirements |
| CN113475158A (en) * | 2019-02-13 | 2021-10-01 | 康维达无线有限责任公司 | Apparatus, system, method, and computer readable medium for connection-oriented vehicle-to-X (VTX) communication in 5G |
| CN113709774A (en) * | 2021-10-27 | 2021-11-26 | 鹏城实验室 | Multiplexing method and device of authorization-free URLLC and dynamic scheduling eMBB |
| CN114222371A (en) * | 2021-12-20 | 2022-03-22 | 北京邮电大学 | Flow scheduling method for coexistence of eMBB (enhanced multimedia broadcast/multicast service) and uRLLC (unified radio link control) equipment |
| CN114586306A (en) * | 2020-03-18 | 2022-06-03 | Lg 电子株式会社 | Method and device for determining HARQ feedback priority in NR V2X |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11805528B2 (en) * | 2019-05-13 | 2023-10-31 | Qualcomm Incorporated | Communication preemption applicability techniques |
-
2022
- 2022-06-10 CN CN202210650813.XA patent/CN114980339B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107889225A (en) * | 2016-09-29 | 2018-04-06 | 深圳市金立通信设备有限公司 | A kind of resource regulating method and equipment |
| KR20190113574A (en) * | 2018-03-27 | 2019-10-08 | 현대자동차주식회사 | Method and apparatus for performing communication using aggregated carriers in v2x(vehicle to everything) communication system |
| CN110769503A (en) * | 2018-07-26 | 2020-02-07 | 中国电信股份有限公司 | Uplink data multiplexing transmission method, device, terminal and storage medium |
| CN113475158A (en) * | 2019-02-13 | 2021-10-01 | 康维达无线有限责任公司 | Apparatus, system, method, and computer readable medium for connection-oriented vehicle-to-X (VTX) communication in 5G |
| CN114586306A (en) * | 2020-03-18 | 2022-06-03 | Lg 电子株式会社 | Method and device for determining HARQ feedback priority in NR V2X |
| CN112888072A (en) * | 2021-01-25 | 2021-06-01 | 陕西师范大学 | eMBB and URLLC resource multiplexing method for guaranteeing service requirements |
| CN113709774A (en) * | 2021-10-27 | 2021-11-26 | 鹏城实验室 | Multiplexing method and device of authorization-free URLLC and dynamic scheduling eMBB |
| CN114222371A (en) * | 2021-12-20 | 2022-03-22 | 北京邮电大学 | Flow scheduling method for coexistence of eMBB (enhanced multimedia broadcast/multicast service) and uRLLC (unified radio link control) equipment |
Non-Patent Citations (2)
| Title |
|---|
| Qimei Chen ; Hao Jiang ; Guanding Yu.Service Oriented Resource Management in Spatial Reuse-Based C-V2X Networks.《IEEE Wireless Communications Letters》.2019,全文. * |
| 段向阳 ; 郝鹏 ; 许玲.((V2X or V2I or V2V or 车联网) and eMBB and URLLC).《移动通信》.2020,全文. * |
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