CN109905918B - NOMA cellular Internet of vehicles dynamic resource scheduling method based on energy efficiency - Google Patents
NOMA cellular Internet of vehicles dynamic resource scheduling method based on energy efficiency Download PDFInfo
- Publication number
- CN109905918B CN109905918B CN201910138998.4A CN201910138998A CN109905918B CN 109905918 B CN109905918 B CN 109905918B CN 201910138998 A CN201910138998 A CN 201910138998A CN 109905918 B CN109905918 B CN 109905918B
- Authority
- CN
- China
- Prior art keywords
- user
- noma
- users
- sub
- channel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 206010042135 Stomatitis necrotising Diseases 0.000 title claims abstract description 93
- 201000008585 noma Diseases 0.000 title claims abstract description 93
- 230000001413 cellular effect Effects 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000005457 optimization Methods 0.000 claims abstract description 32
- 238000004891 communication Methods 0.000 claims abstract description 31
- 238000001228 spectrum Methods 0.000 claims abstract description 13
- 230000007774 longterm Effects 0.000 claims abstract description 3
- 238000004422 calculation algorithm Methods 0.000 claims description 30
- 230000005540 biological transmission Effects 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 claims description 7
- 230000009977 dual effect Effects 0.000 claims description 5
- 238000013468 resource allocation Methods 0.000 claims description 5
- 238000012935 Averaging Methods 0.000 claims description 4
- 238000009795 derivation Methods 0.000 claims description 2
- 239000000126 substance Substances 0.000 claims description 2
- 238000010295 mobile communication Methods 0.000 abstract description 2
- 238000005562 fading Methods 0.000 description 6
- 239000011159 matrix material Substances 0.000 description 6
- 125000004122 cyclic group Chemical group 0.000 description 4
- 238000000354 decomposition reaction Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000010267 cellular communication Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- GVVPGTZRZFNKDS-JXMROGBWSA-N geranyl diphosphate Chemical compound CC(C)=CCC\C(C)=C\CO[P@](O)(=O)OP(O)(O)=O GVVPGTZRZFNKDS-JXMROGBWSA-N 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention relates to an energy efficiency-based NOMA cellular Internet of vehicles dynamic resource scheduling method, and belongs to the field of mobile communication. The method comprises the following steps: in a NOMA cellular network scenario supporting V2V communication, according to the reliability of a V2V user, the time delay of a V2V user, the NOMA user rate requirement and the power limitation of the user as constraint conditions, and with the long-term average energy efficiency maximizing the system energy efficiency as an optimization target, a random optimization model combining the sub-channel allocation of the NOMA user, the frequency spectrum allocation of the V2V user and the congestion control requirement is established, and a power allocation and sub-channel scheduling strategy is established for the NOMA user and the V2V user. The invention can maximize the system energy efficiency on the premise of ensuring the system stability, and simultaneously meet the time delay and reliability of a V2V user and the speed requirements of a NOMA user.
Description
Technical Field
The invention belongs to the field of mobile communication, and relates to a NOMA cellular internet of vehicles dynamic resource scheduling method based on energy efficiency.
Background
In recent years, an Intelligent Transportation System (ITS) is continuously paid extensive attention, and cellular Internet of vehicles communication (C-V2X) is used as a mainstream technology of Internet of vehicles, and vehicle-to-infrastructure communication (C-V2I) and vehicle-to-vehicle direct communication (C-V2V) are realized by utilizing the existing cellular communication technology, so that the traffic efficiency is improved, and the ultra-high reliability low delay requirements such as future automatic driving and the like are met.
In the communication of mass vehicles in dense urban areas, the frequency spectrum resources are in short supply, on one hand, the limited frequency spectrum resources are fully utilized, and the effective resource optimization algorithm is designed, so that the throughput of the system can be improved, and the requirements of different users on QoS (quality of service) and the like can be met. On the other hand, by introducing the NOMA technology, more cellular users can be simultaneously accessed to the network, and the method has obvious performance advantages for dense scenes, is more suitable for system deployment in urban areas in the future, and can improve the spectrum efficiency and the system throughput.
In order to meet the requirements of high reliability and low time delay of communication under high-speed movement of a vehicle, 3GPP (3rd Generation Partnership Project) proposes an enhanced V2V communication technology facing vehicle communication on the basis of D2D communication, so that the current situation of overload of a base station is relieved, and transmission time delay is also reduced. Meanwhile, in practice, the data arrival amount is random and unknown, and when the data arrival amount exceeds the allowed access data amount of the network, the accessible data amount needs to be controlled to avoid network congestion.
During the research on the prior art, the following disadvantages are found:
first, early studies considered the resource allocation problem of V2V communication mainly under OFDMA system, limiting the number of cellular users allowed to access the network. In the context of a NOMA cellular network supporting V2V communications, a great deal of work has focused on maximizing spectral efficiency and throughput, without considering the problem of resource allocation that maximizes system energy efficiency while guaranteeing V2V user latency and reliability. Also, in practice, network congestion is likely to be caused by data arrival amounts exceeding a threshold value of network capacity, and most studies do not incorporate congestion control into a resource optimization model. Finally, in the existing literature, in the NOMA scenario supporting V2V communication, establishment of a static optimization model is mostly considered, and resource scheduling cannot be dynamically performed in real time according to the network load state.
Therefore, in the NOMA cellular network scene supporting V2V communication, the invention fully considers the time delay and reliability of V2V users, the speed requirement of NOMA users, the queue stability, the access data amount control, the user power control and other limiting conditions, constructs an optimization problem with the aim of maximizing the system energy efficiency aiming at the common channel interference of V2V users and NOMA users and the power distribution problem in NOMA criteria, and finally provides a dynamic resource distribution algorithm combining sub-channel scheduling, power control and congestion control.
Disclosure of Invention
In view of this, the present invention aims to provide an energy efficiency-based NOMA cellular internet of vehicles dynamic resource scheduling method, which maximizes system energy efficiency on the premise of ensuring system stability.
In order to achieve the purpose, the invention provides the following technical scheme:
a NOMA cellular Internet of vehicles dynamic resource scheduling method based on energy efficiency is characterized in that the method comprises the following steps: in a NOMA cellular network scenario supporting V2V communication, according to the reliability of a V2V user, the time delay of a V2V user, the NOMA user rate requirement and the power limitation of the user as constraint conditions, the long-term average energy efficiency maximizing the system energy efficiency is taken as an optimization target, a random optimization model combining the sub-channel allocation of the NOMA user, the spectrum allocation of the V2V user and the congestion control requirement is established aiming at the interference between the V2V user and the cellular user and the power allocation problem under the NOMA criterion, and a power allocation and sub-channel scheduling strategy is formulated for the NOMA user and the V2V user.
Further, the reliability requirements for meeting the V2V user are: interference caused by the V2V user in the shared NOMA user sub-channel can reduce the communication quality of the V2V user, the communication quality of the V2V user is guaranteed by adopting Bit Error Rate (BER) constraint, and signal interruption and packet loss caused by interference and the like are reduced;
the requirement for meeting the delay of the V2V user is as follows: the V2V communication carries time delay sensitive service, generally transmits safety information related to vehicle driving and road traffic, and the time delay requirement constraint is used for avoiding unnecessary packet loss or transmission delay caused by factors such as interference in the user transmission process;
the rate requirements for the NOMA users are met as follows: to control the impact of interference caused by V2V when users share the spectrum on the quality of the communications link for NOMA users;
the power requirements for NOMA users and V2V users are: the sum of the powers of NOMA users sharing the same sub-channel does not exceed their threshold, nor does the sum of the powers of V2V users sharing the same sub-channel with NOMA users exceed their threshold.
Further, the sub-channel allocation requirements of the NOMA users are as follows: when cellular users share sub-channels by using the NOMA technology, the maximum multiplexing times M must not be exceeded in order to ensure the communication quality;
the spectrum allocation requirements of the V2V users are as follows: although the spectrum efficiency can be improved by multiplexing multiple users with the same cellular user spectrum, V2V users can cause interference to NOMA users when sharing the spectrum, and it is generally assumed that V2V users share at most one NOMA user subchannel;
the congestion control requirements are: when the data packet arrival amount exceeds the allowed access data amount of the network, the stability of the queue is ensured by controlling the data amount accessed to the network and increasing the data transmission rate to avoid network congestion.
Further, the buffer queue update process of the NOMA user in the NOMA cellular network at each time slot is as follows:
Qi(t+1)＝max{Qi(t)-ri(t),0}+Γi(t)
wherein Q isi(t +1) represents the queue length of the ith NOMA user at the beginning of the next time slot; qi(t) represents the queue length of the ith NOMA user at the beginning of the current time slot; gamma-shapedi(t) represents the amount of data that the ith NOMA user is allowed to access in the current time slot; r isi(t) indicates the number of packets the ith cellular user left at the current time slot.
Further, the queue stability for each NOMA user is:
wherein the content of the first and second substances,
represents the time-averaged length of the ith NOMA user; t representsi NOMA user queuing periods; e denotes averaging the queue length of NOMA user i in the system over the whole period T.
Further, the Lyapunov function represents the queue congestion degree of the system, the larger the function value is, the longer the queue is, and the longer the waiting time for the user to transmit data is. The queue vector for a t-slot system is denoted herein as Q (t) ═ Qi(t),Qk(t),Hi(t)]The optimization problem can be regarded as time averaging of the maximum rate and the minimum power, so that the selection of the control strategy can be performed by using the upper bound of the sum of the lyapunov offset and the weighting cost function, the optimal power distribution is obtained, and the purpose of maximizing the user rate under the network time averaging is achieved while the stability of the queue is ensured. Defining the Lyapunov function as:
wherein Q isi(t) is the NOMA user traffic queue at the current time, Hi(t) virtual queue, Q, of NOMA users at the present timek(t) is the V2V user virtual queue at the current time.
Further, the optimization target is divided into three steps to respectively obtain optimized solutions of congestion control, sub-channel scheduling and power allocation, the congestion control problem is a linear problem and can be directly solved, then a sub-optimal sub-channel matching algorithm is designed to obtain a sub-channel scheduling scheme, and the specific sub-channel matching algorithm comprises the following steps: on each scheduling time slot, dynamically allocating sub-channels for each NOMA user and V2V user to meet each constraint condition, and the specific steps are as follows:
in each scheduling time slot, wireless channel state information of all NOMA users and V2V users in the current time slot is estimated through a channel model, in the channel model of the users, a cellular user adopts a Rayleigh channel fast fading model, the fast mobility of the V2V user is considered, a 3D geometric space channel model can more accurately simulate an actual channel model, and a WINNER II urban area channel model is adopted. And observing queue buffer information and virtual queue state information of each user;
and matching the user with the sub-channel according to the channel preference matrix of the user so as to maximize the energy efficiency of the system, wherein the maximum number of the users which can be reused on the same sub-channel is less than M, so that a global optimal solution is obtained by an exhaustive search method, but the complexity is exponentially increased relative to the number of the users, and therefore, a suboptimal user scheduling scheme for reducing the complexity is provided. The user power is first initialized, assuming that each user is assigned the same average power
And
user set
And
respectively initializing and recording users without sub-channels, in the scheduling scheme, when the number of users multiplexing the same sub-channel does not exceed a threshold value M, firstly according to a channel preference matrix of NOMA users
Matching out the best channel gain and distributing the channel to the user correspondingly, setting the element in the corresponding channel matrix to zero and updating the user set
Since both NOMA and V2V users can reuse the channel, their corresponding channel preference matrices are searched again
And
finding out the user with maximum channel gain by comparison, and distributing the corresponding channels until the number of the multiplexed users of each channel reaches MTo obtain a feasible user set Un,possibleAnd selecting the user set which minimizes the objective function through calculation, and returning the users which are not allocated with the sub-channels to the user set
And
the algorithm is executed by looping until all users are assigned to a subchannel.
And finally, solving the residual power optimization problem, wherein the problem is a non-convex optimization problem which is difficult to directly solve, the optimization problem is converted by using a continuous convex approximation theory, and a power distribution strategy is obtained by using a Lagrange decomposition scheme, wherein the specific power distribution scheme solving step is as follows:
initializing an approximate vector value, initializing a Lagrange multiplier, initializing the queue length of the NOMA user and the virtual queue length of the V2V user, carrying out multiple iterations according to the initialized user resource amount and a continuous convex approximation algorithm until a convergence condition is met to obtain an optimized solution of power and the optimized approximate vector value, and converting the original non-convex optimization problem into a convex optimization problem; and taking the obtained power as the initialization power of the algorithm 3, and iteratively updating the Lagrange multiplier and the power strategy after continuous convex optimization conversion for many times. Judging whether a convergence condition is met or not through a plurality of iterations, if the absolute value of the difference between the relaxed target function values of the two iterations is less than or equal to a given maximum allowable error or reaches the maximum iteration number, terminating the iteration process and taking the power distribution result obtained by the last iteration as the optimal power distribution strategy of the current time slot, and if the continuous convex approximation algorithm and the power distribution algorithm both meet the convergence condition, distributing the power distribution strategy to all users; and according to the resource allocation strategy, all users update the queue length according to the cache queue and the virtual queue update formula, and wait for the next scheduling time slot.
The invention has the beneficial effects that:
in the invention, under the NOMA cellular network scene supporting V2V communication, aiming at the interference between V2V users and cellular users and the power distribution problem under the NOMA criterion, a random optimization model combining sub-channel scheduling, power distribution and congestion control is established, and the system energy efficiency is maximized while the time delay, reliability and cellular user rate requirements of the V2V users are ensured. Dynamic resource scheduling is carried out according to the load state of the current network by utilizing a Lyapunov random optimization method, a random optimization model is converted and decomposed into three sub-problems solved by a single time slot, the stability of a queue is ensured by controlling the accessible data volume so as to avoid network congestion, and a sub-optimal user and sub-channel matching algorithm is further designed to obtain a sub-channel scheduling scheme. And finally, converting the power distribution sub-problem into a convex optimization problem by using a continuous convex approximation theory, and obtaining a power distribution strategy by using a Lagrange dual method.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a vehicle communication scene diagram under the dense urban area cellular Internet of vehicles;
FIG. 2 is a flow chart of a sub-optimal sub-channel scheduling algorithm;
FIG. 3 is a flow chart of an iterative power distribution algorithm based on successive convex approximation and Lagrangian dual decomposition;
FIG. 4 is a flowchart of a NOMA joint resource scheduling global algorithm based on energy efficiency.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a view of a vehicle communication scenario under a dense urban area cellular internet of vehicles adopted by the present invention. In the example of the present invention, a transmission scenario including both V2I downlink and V2V sideline communication modes is considered. In the downlink V2I communication, the number of devices accessing the network is increased by introducing the NOMA technology, and the burden of the base station is reduced by the V2V side-line direct communication, and the end-to-end transmission delay is low. The urban road is in a Manhattan grid shape, and the vehicle distribution obeys a one-dimensional Poisson point distribution model
The wireless channel model of the user is composed of slow fading and fast fading composed of path loss and shadow fading. The NOMA user adopts a Rayleigh channel fast fading model, the fast mobility of the V2V user is considered, the 3D geometric space channel model can more accurately simulate an actual channel model, and the WINNER II urban fast fading channel model is adopted by referring to 3 GPP.
Referring to fig. 2, a flow chart of a sub-optimal sub-channel scheduling algorithm is shown, and the goal is to obtain a channel scheduling policy of a user through the sub-channel matching algorithm. The method comprises the following steps:
step 201: initializing user power, and observing user service queue state and virtual queue state.
Step 202: constructing a channel gain matrix H of NOMA and V2V users respectivelyiH and Hk。
Step 203: from a channel matrix HiAnd searching the maximum channel gain and carrying out corresponding channel scheduling.
Step 204: determine whether a loop termination condition is satisfied, i.e.
If yes, the algorithm is stopped, and a user channel scheduling scheme is output. If not, step 205 is continued.
Step 205: further respectively from a channel matrix HiH and HkSearching the maximum channel gain and comparing to obtain the maximum, and carrying out corresponding channel scheduling.
Step 206: calculating according to a formula to obtain a scheduling solution of the user and updating a user scheduling set
And
step 207: determine whether a loop termination condition is satisfied, i.e.
If yes, the algorithm is stopped, and a user channel scheduling scheme is output. If not, step 205 is continued.
Referring to fig. 3, fig. 3 is a flowchart of an iterative power allocation algorithm based on successive convex approximation and lagrangian dual decomposition, and the steps are as follows:
step 301: number of initialization iterations N1And maximum allowable error Δ1The feasible points, i.e., NOMA user and V2V user powers, are initialized, and the iteration number index is initialized.
Step 302: and substituting the initialized power into the calculation formula to obtain an approximate vector.
Step 303: and judging whether the cyclic convergence condition is met, if not, terminating the algorithm, and outputting the optimal power solution introduced into the convex optimization theory. If so, continue with step 304.
Step 304: and substituting the updated power into the calculation formula respectively to obtain an updated approximate vector.
Step 305: and judging whether the cyclic convergence condition is met, if not, terminating the algorithm, and outputting the optimal power solution introduced into the convex optimization theory. If so, continue with step 304.
Step 306: and substituting the updated approximate vector into the optimization problem to solve, updating the current optimal power solution, and gradually increasing the iteration times.
Step 307: initializing an approximation vector, lagrange multiplier v0,λ0,u0,η0Maximum number of iterations N2Convergence criteria, and iteration index, etc.
Step 308: and in the current time slot, observing the service queue state and the virtual queue of the user of the time slot and estimating the channel state information of the time slot.
Step 309: and judging whether the cyclic convergence condition is met, if not, terminating the cyclic condition and outputting a power distribution scheme.
Step 310: if yes, substituting the previous iteration power and the Lagrange multiplier into a derivation formula through a KKT condition to obtain the optimal power distribution strategy of the iteration.
Step 311: updating Lagrange multiplier v according to sub-gradient algorithmm,λm,um,ηmAnd the number of iterations.
Step 312: and judging whether the circulation convergence condition is met. If not, the circulation condition is terminated, and the optimized power distribution scheme is output.
Referring to fig. 4, a flowchart of a NOMA joint resource scheduling global algorithm based on energy efficiency is shown, which includes the following steps:
step 401: the initialization control parameters V, NOMA user queue length and virtual queue length set the slot length.
Step 402: judging whether the current time slot is in a set time slot range, if so, executing a step 403; otherwise the algorithm ends.
Step 403: observing the NOMA queue state and the virtual queue length of the time slot, estimating the channel state information of the time slot, and calculating to obtain a linear solution of congestion control.
Step 404: and executing a sub-optimal sub-channel allocation algorithm to obtain the channel scheduling strategies of the NOMA user and the V2V user.
Step 405: and executing an iterative power distribution algorithm based on continuous convex approximation and Lagrange dual decomposition to obtain an optimized power distribution scheme.
Step 406: and updating the queue state and the virtual queue length of the NOMA user in the next time slot according to a queue updating formula.
Step 407: and moving to the next time slot to continue the steps.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (4)
1. A NOMA cellular Internet of vehicles dynamic resource scheduling method based on energy efficiency is characterized in that the method comprises the following steps: in a NOMA cellular network scene supporting V2V communication, according to the reliability of a V2V user, the time delay of a V2V user, the rate requirement of the NOMA user and the power limit of the user as constraint conditions, and with the long-term average energy efficiency of maximizing the energy efficiency of a system as an optimization target, a random optimization model combining the sub-channel allocation of the NOMA user, the frequency spectrum allocation of the V2V user and the congestion control requirement is established, and power allocation and sub-channel scheduling strategies are formulated for the NOMA user and the V2V user;
the optimization target is joint optimization congestion control, sub-channel scheduling and power distribution, the stability of the queue is guaranteed by controlling the accessible data volume to avoid network congestion, a sub-channel scheduling scheme is obtained by adopting a sub-optimal user and sub-channel matching algorithm, finally, the power distribution sub-problem is converted into a convex optimization problem by utilizing a continuous convex approximation theory, and a power distribution strategy is obtained by adopting a Lagrange dual method;
the sub-channel matching algorithm is as follows: on each scheduling time slot, firstly, an optimization solution of congestion control is calculated, and appropriate sub-channels and power are dynamically allocated to each NOMA user and V2V user so as to meet each constraint condition, and the specific steps are as follows:
1) in each scheduling time slot, estimating the wireless channel state information of all NOMA users and V2V users in the current time slot through a channel model, and collecting the queue state of each NOMA user and the state information of other virtual queues;
2) calculating to obtain a linear optimization solution of congestion control, and further obtaining a sub-optimal sub-channel matching strategy;
3) calculating according to the initialized user resource amount and the Lagrange multiplier value to obtain a power distribution scheme;
4) respectively updating the real queue state and the virtual queue state of the NOMA user of the next time slot;
5) after several iterations, judging whether a convergence condition is met, if not, repeating the steps; otherwise, optimizing the solution for congestion control of all users in the system, and matching strategy and power distribution scheme of sub-channels;
on each scheduling time slot, dynamically allocating proper power for each NOMA user and V2V user to meet each constraint condition, and the specific steps are as follows:
1) in each scheduling time slot, estimating the wireless channel state information of all NOMA users and V2V users in the current time slot through a channel model, and collecting queue cache information and virtual queue state information of each NOMA user;
2) performing multiple iterations according to a continuous convex approximation algorithm until a convergence condition is met to obtain an optimized solution of power and optimized related parameters, so that a non-convex problem is converted into a convex optimization problem;
3) the power obtained by utilizing the convex optimization theory is used as the initialization power of the Lagrange method, and the initial power solution and the Lagrange multiplier are substituted into a derivation expression to obtain an optimized power solution;
4) judging whether the convergence condition is met or not through a plurality of iterations;
5) if the successive convex approximation algorithm and the resource allocation algorithm both meet the convergence condition, notifying all users of the power allocation strategy;
6) according to a resource allocation strategy, all users update the queue length and the Lagrange multiplier according to a cache queue and a virtual queue update formula, and wait for the next scheduling time slot;
the buffer queue updating process of the NOMA user in the NOMA cellular network on each time slot is as follows:
Qi(t+1)＝max{Qi(t)-ri(t),0}+Γi(t)
wherein Q isi(t +1) represents the queue length of the ith NOMA user at the beginning of the next time slot; qi(t) represents the queue length of the ith NOMA user at the beginning of the current time slot; gamma-shapedi(t) represents the amount of data that the ith NOMA user is allowed to access in the current time slot; r isi(t) indicates the number of packets the ith cellular user left at the current time slot.
2. The energy-efficiency-based NOMA cellular Internet of things dynamic resource scheduling method of claim 1, wherein the reliability requirements for meeting the V2V user are: interference caused by the V2V user in the shared NOMA user sub-channel can reduce the communication quality of the V2V user, the communication quality of the V2V user is ensured by adopting Bit Error Rate (BER) constraint, and signal interruption and packet loss caused by the interference are reduced;
the requirement for meeting the delay of the V2V user is as follows: the V2V communication carries time delay sensitive service, transmits safety information related to vehicle driving and road traffic, and the time delay requirement constraint is used for avoiding unnecessary packet loss or sending delay caused by interference factors in the user transmission process;
the rate requirements for the NOMA users are met as follows: to control the impact of interference caused by V2V when users share the spectrum on the quality of the communications link for NOMA users;
the power requirements for NOMA users and V2V users are: the sum of the powers of NOMA users sharing the same sub-channel does not exceed their threshold, nor does the sum of the powers of V2V users sharing the same sub-channel with NOMA users exceed their threshold.
3. The energy-efficiency-based NOMA cellular Internet of things dynamic resource scheduling method of claim 1, wherein the sub-channel allocation requirements of NOMA users are: when cellular users share sub-channels by using the NOMA technology, the maximum multiplexing times M must not be exceeded in order to ensure the communication quality;
the spectrum allocation requirements of the V2V users are as follows: suppose that V2V users share at most one NOMA user subchannel;
the congestion control requirements are: when the data packet arrival amount exceeds the allowed access data amount of the network, the stability of the queue is ensured by controlling the data amount accessed to the network and increasing the data transmission rate to avoid network congestion.
4. The energy efficiency-based NOMA cellular Internet of things dynamic resource scheduling method of claim 1, wherein the queue stability of each NOMA user is:
wherein the content of the first and second substances,
represents the time-averaged length of the ith NOMA user; t represents the ith NOMA user queuing period; e denotes averaging the queue length of NOMA user i in the system over the whole period T.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910138998.4A CN109905918B (en) | 2019-02-25 | 2019-02-25 | NOMA cellular Internet of vehicles dynamic resource scheduling method based on energy efficiency |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910138998.4A CN109905918B (en) | 2019-02-25 | 2019-02-25 | NOMA cellular Internet of vehicles dynamic resource scheduling method based on energy efficiency |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109905918A CN109905918A (en) | 2019-06-18 |
| CN109905918B true CN109905918B (en) | 2022-04-01 |
Family
ID=66945558
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910138998.4A Active CN109905918B (en) | 2019-02-25 | 2019-02-25 | NOMA cellular Internet of vehicles dynamic resource scheduling method based on energy efficiency |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109905918B (en) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110417496B (en) * | 2019-07-15 | 2021-12-17 | 重庆邮电大学 | Cognitive NOMA network stubborn resource allocation method based on energy efficiency |
| CN110445519B (en) * | 2019-07-24 | 2022-07-26 | 南京邮电大学 | Method and device for resisting inter-group interference based on signal-to-interference-and-noise ratio constraint |
| CN110418399B (en) * | 2019-07-24 | 2022-02-11 | 东南大学 | NOMA-based Internet of vehicles resource allocation method |
| CN114557101A (en) * | 2019-11-26 | 2022-05-27 | Oppo广东移动通信有限公司 | Method, user equipment and computer readable medium for determining sidechain transmission parameters |
| CN115643210A (en) * | 2019-11-30 | 2023-01-24 | 华为技术有限公司 | Control data packet sending method and system |
| CN111132083B (en) * | 2019-12-02 | 2021-10-22 | 北京邮电大学 | NOMA-based distributed resource allocation method in vehicle formation mode |
| CN111787571B (en) * | 2020-06-29 | 2023-04-18 | 中国电子科技集团公司第七研究所 | Joint optimization method for network user association and resource allocation |
| CN113014295B (en) * | 2021-02-24 | 2022-03-08 | 南京邮电大学 | Uplink joint receiving method for large-scale de-cellular MIMO system |
| CN113163368B (en) * | 2021-05-19 | 2022-09-13 | 浙江凡双科技有限公司 | Resource allocation method of low-delay high-reliability V2V system |
| CN113423087B (en) * | 2021-06-17 | 2022-04-15 | 天津大学 | Wireless resource allocation method facing vehicle queue control requirement |
| CN113795013A (en) * | 2021-09-28 | 2021-12-14 | 山东大学 | Lyapunov optimization-based V2V communication resource allocation method in Internet of vehicles |
| CN114338756B (en) * | 2021-11-11 | 2024-03-08 | 北京蜂云科创信息技术有限公司 | Method and system for intelligent network high concurrency communication of commercial vehicle |
| CN114124254B (en) * | 2021-11-24 | 2024-03-29 | 东莞理工学院 | NOMA downlink user selection method and system with maximized total rate |
| CN114520989B (en) * | 2022-01-21 | 2023-05-26 | 重庆邮电大学 | Multi-carrier wave number energy simultaneous transmission NOMA network energy efficiency maximization method |
| CN114501504B (en) * | 2022-02-23 | 2024-02-13 | 国网电力科学研究院有限公司 | Combined optimization method and system based on non-cellular network |
| CN114630413B (en) * | 2022-03-31 | 2024-02-06 | 华北电力大学 | Energy efficiency optimization-oriented C-V2V Internet of vehicles power control method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108513348A (en) * | 2017-02-28 | 2018-09-07 | 大唐高鸿信息通信研究院（义乌）有限公司 | The ant colony power distribution of the non-orthogonal multiple access of 5G networks optimizes without algorithm |
| CN108513314A (en) * | 2017-02-28 | 2018-09-07 | 大唐高鸿信息通信研究院（义乌）有限公司 | The non-orthogonal multiple of 5G networks accesses cross-layer power distribution optimization method |
| CN108737057A (en) * | 2018-04-27 | 2018-11-02 | 南京邮电大学 | Multicarrier based on deep learning recognizes NOMA resource allocation methods |
| CN108834112A (en) * | 2018-06-13 | 2018-11-16 | 南京邮电大学 | A kind of relaying auxiliary D2D communication system power distribution method based on NOMA |
| CN109041193A (en) * | 2018-08-01 | 2018-12-18 | 重庆邮电大学 | A kind of dynamic syndicated user-association of network slice and power distribution method based on NOMA |
| CN109068391A (en) * | 2018-09-27 | 2018-12-21 | 青岛智能产业技术研究院 | Car networking communication optimization algorithm based on edge calculations and Actor-Critic algorithm |
| AU2018102043A4 (en) * | 2018-04-03 | 2019-01-17 | Jinan University | Large-scale D2D Communication Method based on HARQ Assisted NOMA |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102194490B1 (en) * | 2014-09-16 | 2020-12-23 | 삼성전자주식회사 | Apparatus and method for scheduling in wireless communication system |
-
2019
- 2019-02-25 CN CN201910138998.4A patent/CN109905918B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108513348A (en) * | 2017-02-28 | 2018-09-07 | 大唐高鸿信息通信研究院（义乌）有限公司 | The ant colony power distribution of the non-orthogonal multiple access of 5G networks optimizes without algorithm |
| CN108513314A (en) * | 2017-02-28 | 2018-09-07 | 大唐高鸿信息通信研究院（义乌）有限公司 | The non-orthogonal multiple of 5G networks accesses cross-layer power distribution optimization method |
| AU2018102043A4 (en) * | 2018-04-03 | 2019-01-17 | Jinan University | Large-scale D2D Communication Method based on HARQ Assisted NOMA |
| CN108737057A (en) * | 2018-04-27 | 2018-11-02 | 南京邮电大学 | Multicarrier based on deep learning recognizes NOMA resource allocation methods |
| CN108834112A (en) * | 2018-06-13 | 2018-11-16 | 南京邮电大学 | A kind of relaying auxiliary D2D communication system power distribution method based on NOMA |
| CN109041193A (en) * | 2018-08-01 | 2018-12-18 | 重庆邮电大学 | A kind of dynamic syndicated user-association of network slice and power distribution method based on NOMA |
| CN109068391A (en) * | 2018-09-27 | 2018-12-21 | 青岛智能产业技术研究院 | Car networking communication optimization algorithm based on edge calculations and Actor-Critic algorithm |
Non-Patent Citations (2)
| Title |
|---|
| NOMA-SM for cooperatively enhancing vehicle-to-vehicle transmissions;Yingyang Chen,et al;《IEEE Globecom Workshops(GC Wkshps)》;20171208;全文 * |
| 非正交多址接入系统中资源分配机制研究;于中华;《中国优秀硕士学位论文辑》;20180415;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109905918A (en) | 2019-06-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109905918B (en) | NOMA cellular Internet of vehicles dynamic resource scheduling method based on energy efficiency | |
| CN110493826B (en) | Heterogeneous cloud wireless access network resource allocation method based on deep reinforcement learning | |
| CN109729528B (en) | D2D resource allocation method based on multi-agent deep reinforcement learning | |
| CN109041193B (en) | NOMA-based network slice dynamic combined user association and power allocation method | |
| CN107172704B (en) | Cognitive heterogeneous network power distribution method based on cooperative spectrum sensing and interference constraint | |
| CN112601284B (en) | Downlink multi-cell OFDMA resource allocation method based on multi-agent deep reinforcement learning | |
| CN108093435B (en) | Cellular downlink network energy efficiency optimization system and method based on cached popular content | |
| CN108271172B (en) | Cellular D2D communication joint clustering and content deployment method | |
| Masoudi et al. | Reinforcement learning for traffic-adaptive sleep mode management in 5G networks | |
| Xu et al. | Delay-oriented cross-tier handover optimization in ultra-dense heterogeneous networks | |
| CN110602722A (en) | Design method for joint content pushing and transmission based on NOMA | |
| CN110753365B (en) | Heterogeneous cellular network interference coordination method | |
| CN108601083B (en) | Resource management method based on non-cooperative game in D2D communication | |
| Zhang et al. | Statistical QoS-driven power adaptation for distributed caching based mobile offloading over 5G wireless networks | |
| CN109963272A (en) | A kind of accidental access method towards in differentiation MTC network | |
| CN113453197B (en) | User pairing method combining mobile prediction and dynamic power | |
| Hu et al. | Performance analysis for D2D-enabled cellular networks with mobile edge computing | |
| CN109803352B (en) | Resource allocation method and device for fog wireless access network | |
| Chen et al. | Performance study of cybertwin-assisted random access noma | |
| Wang et al. | Handoff algorithms in dynamic spreading WCDMA system supporting multimedia traffic | |
| Li et al. | Intercell interference-aware scheduling for delay sensitive applications in C-RAN | |
| KR102636756B1 (en) | Deep-reinforcement-learning-based distributed power control for vehicle-to-vehicle communications | |
| Wang et al. | Traffic offloading and resource allocation for PDMA-based integrated satellite/terrestrial networks | |
| Lv et al. | Joint optimization of file placement and delivery in cache-assisted wireless networks | |
| Lu et al. | Deep reinforcement learning-based power allocation for ultra reliable low latency communications in vehicular networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231108 Address after: Room 538, Yuesheng International Finance Building, No. 972 Kejiguan Street, Xixing Street, Binjiang District, Hangzhou City, Zhejiang Province, 310000 Patentee after: Hangzhou Shiyi Network Technology Co.,Ltd. Address before: 1003, Building A, Zhiyun Industrial Park, No. 13 Huaxing Road, Henglang Community, Dalang Street, Longhua District, Shenzhen City, Guangdong Province, 518000 Patentee before: Shenzhen Wanzhida Technology Transfer Center Co.,Ltd. Effective date of registration: 20231108 Address after: 1003, Building A, Zhiyun Industrial Park, No. 13 Huaxing Road, Henglang Community, Dalang Street, Longhua District, Shenzhen City, Guangdong Province, 518000 Patentee after: Shenzhen Wanzhida Technology Transfer Center Co.,Ltd. Address before: 400065 Chongqing Nan'an District huangjuezhen pass Chongwen Road No. 2 Patentee before: CHONGQING University OF POSTS AND TELECOMMUNICATIONS |
|
| TR01 | Transfer of patent right |