CN111294137A - Multi-channel transmission scheduling method based on time domain interference alignment in underwater acoustic network - Google Patents

Abstract

The invention provides a multi-channel transmission scheduling method based on time domain interference alignment in an underwater acoustic network, which comprises the following steps: s1 network topological structure representation, the invention uses time-slot model to divide the whole message transmission process into several time slots with length tau, the transmission delay between nodes refers to the time needed by the message transmission between two nodes, the invention uses transmission delay matrix to represent network topological structure, the elements in the matrix represent the time slot number needed by the message transmission between nodes; s2 transmission scheduling initialization, using the state of three-dimensional matrix storage node to determine the transmission time slot of the node, the selected transmission channel and the destination node; s3 search for an optimal transmission decision, finding an optimal scheduling problem may be considered a sequential decision problem, which may be solved using dynamic programming. In the multi-channel network model, a plurality of nodes can transmit messages simultaneously in one time slot, so that data collision is reduced, and the network throughput is increased.

Description

A Multi-Channel Transmission Scheduling Method Based on Time Domain Interference Alignment in Underwater Acoustic Networks

technical field

The invention relates to the technical field of underwater acoustic communication networks, in particular to a multi-channel underwater acoustic network transmission scheduling method based on time-domain interference alignment.

Background technique

The underwater acoustic network is a wireless sensor network that uses underwater acoustic channels for communication. It is widely used in civil, military and industrial fields, such as marine environmental research, monitoring and collection of military intelligence, and the exploitation and detection of marine resources. Due to the slow propagation speed of the signal in the underwater acoustic channel, the large transmission delay has become a major feature of the underwater acoustic network that is different from the terrestrial sensor network. The traditional wireless sensor network MAC algorithm does not consider the large propagation delay, and the wireless sensor network MAC algorithm is directly used in the underwater acoustic network, which will affect the network throughput. Therefore, this type of MAC algorithm is not suitable for the underwater acoustic network. The characteristics of the propagation delay make it necessary to design a scheduling algorithm.

Most underwater acoustic network MAC algorithms focus on eliminating the negative effects of large propagation delays. Unlike the above studies, the present invention utilizes large propagation delays to achieve time-domain interference alignment. Time-domain interference alignment refers to aligning the interference at unintended nodes within a certain time domain as much as possible, while maintaining no interference when receiving messages. The large propagation delay in the underwater acoustic network just provides favorable conditions for realizing temporal interference alignment. With the large propagation delay, multiple nodes can work simultaneously in the same time slot without conflict, which is beneficial to the improvement of throughput.

Underwater high-speed OFDM technology provides technical support for multi-channel communication in underwater acoustic network. Multi-channel can make better use of underwater acoustic channel bandwidth resources, and by dividing the total bandwidth into several sub-channels, multiple data can be transmitted in parallel on the sub-channels. Compared with the single-channel communication mechanism, the multi-channel mechanism can achieve higher throughput and channel utilization.

SUMMARY OF THE INVENTION

In view of this, the present invention aims at the deficiencies of the prior art, and its main purpose is to provide a multi-channel transmission scheduling method based on time-domain interference alignment in an underwater acoustic network, which is used for message transmission scheduling in a multi-channel underwater acoustic network, to improve network throughput.

In order to achieve the above purpose, the present invention is achieved through the following technical solutions: a multi-channel underwater acoustic network transmission scheduling method based on time-domain interference alignment, comprising the following steps:

S1 network topology representation: the propagation delay between nodes refers to the time required for a signal to be transmitted from a source node to a destination node in a given medium. The present invention uses a transmission scheduling matrix T to represent the network topology, and the elements in the matrix are the pairs of nodes. The number of time slots required for inter-propagation;

S2 transmission scheduling matrix initialization: The transmission schedule S specifies the transmission situation of the node in each time slot. Efficiently transmit and receive messages according to the scheduling matrix nodes to maximize throughput. In the initial stage of the network, the transmission scheduling matrix is empty, and then the transmission scheduling matrix of each time slot is continuously updated;

S3 optimal transmission decision search: Finding the optimal scheduling problem can be regarded as a sequential decision problem optimization, which can be solved using dynamic programming. The qualified decision is determined by using the time-domain interference alignment idea and network multi-channel transmission conditions, and then the value function is determined to find the optimal transmission decision among the qualified decisions.

S4 transmission scheduling update mode: the optimal decision is added to the current scheduling and a transmission scheduling update is completed, and the decision judgment and optimal transmission decision search are performed on all possible transmissions again until there is no qualified decision, and the scheduling update of a time slot is completed. A time slot update. The scheduling update process is cyclically repeating steps S3 and S4.

Further, for a network including N nodes in the step S1, the network topology is represented by a matrix T of N*N, and the element T _ij in the matrix represents the time required for message propagation between a node pair (i, j). The number of gaps, as shown in formula (1),

Among them, τ is the length of a time slot, d _ij is the propagation delay between the node pair (i, j), and the propagation delay refers to the time required for a signal to travel from the source node to the destination node in a given medium, as shown in Eq. As shown in (2),

D _ij is the distance between node i and node j, and v is the propagation speed of the signal. In the underwater acoustic network, the underwater acoustic signal is used to carry information, and its propagation speed is about 1500 m/s.

Further, in the step S2, the transmission scheduling determines the transmission state of the node in each time slot. The three-dimensional matrix S={S _i,c,t } represents the transmission scheduling model. If S _{i, c, t} = j > 0, it means that node i in time slot t uses the c channel to transmit messages to node j; if S _{i, c, t} = -j < 0, it means that node i in time slot t uses c The channel receives messages from node j; if S _{i, c, t} = 0, it means that node i in time slot t is in an idle state.

Further, the step S3 includes:

S31, modeling a sequential decision problem;

S32, using the time domain interference alignment idea and the network multi-channel transmission conditions to make a qualified decision decision;

S33, determine the value function, use the dynamic programming algorithm to solve the sequential decision problem, and find the optimal transmission decision.

Further, the step S4 includes:

S41, allocate a channel for the optimal transmission decision;

S42, the optimal transmission decision is added to the current scheduling S ^{t} to form a new scheduling S ^{{t, 1}} ;

S43, go to step S32 to continue to find new optimal scheduling, go to step S41, until there is no qualified transmission decision;

S44, the scheduling update of the t time slot is completed, and the update of the t+1 time slot is performed.

Further, in the step S31, each transmission scheduling is regarded as a decision, denoted as x ^{{t, h}} , and the state of the decision problem is denoted by S ^{t} , including all transmissions made up to time slot t . A new decision and the current state jointly determine a new state, and all decisions in time slot t and the state in time slot t determine the state in time slot t+1, as shown in equation (3),

S ^{{t+1, 1}} = Π(S ^{t} ,x ^{t} )#(3)

where x ^{t} is the set of all transmissions in time slot t, and Π() is the state transition function.

Further, only the transmission that satisfies the time-domain interference alignment and multi-channel network transmission conditions in the step S32 is regarded as a qualified decision. For a partial schedule S ^{t} of a t slot, a qualified transmission decision at slot t' needs to satisfy the following constraints (t'=t+δ):

Condition 1: The node cannot self-transmit, that is, j≠k;

Condition 2: The source node j needs to keep the idle state in the t' time slot, and the node can only use one channel to transmit messages in a time slot, so in the scheduling S ^{t} , the source node j in the t' time slot is in any channel. must be in an idle state, i.e.

Condition 3: Due to the half-duplex and single-packet reception limitations of the node, the destination node k of the t′+T _jk time slot is in an idle state on any channel in the scheduling S ^{t} , that is,

Condition 4: A node can only receive one message in a time slot, so it is necessary to ensure that no other message arrives when the destination node k receives the message from node j at t'+T _jk , which means that any node i is at t'+T _jk -T _ik slots cannot transmit messages to k, i.e.

Condition 5: The node in the network is a single-packet receiving model, when two or more messages arrive at a node at the same time, all messages cannot be received. In order to ensure that the message transmitted from node j to node k does not affect the ongoing transmission (m, n), it should be avoided that the message from node j and the message from node m arrive at node n at the same time, i.e.

满足上述限制条件，则指示函数

表示对于调度S^{t}传输

为合格传输；否则当传输

不满足上述限制条件时，指示函数

when transmitting

Satisfying the above constraints, the indicator function

represents that for a schedule S ^{t} transmission

is a qualified transmission; otherwise, when the transmission

When the above constraints are not met, the indicator function

通过判断新调度

中合格决策的个数可评价新决策，因此价值函数可表示为：Further, in the step S33, the dynamic programming algorithm to solve the sequential decision problem needs to determine the value function, which is used to find the optimal decision. Different new decisions added to the current schedule S ^{t} will form different new schedules

By judging the new schedule

The number of qualified decisions in the new decision can be evaluated, so the value function can be expressed as:

The optimal decision can maximize the value function. Here, since each new decision will add new interference, the optimal decision can ensure that the interference and the previous interference overlap as much as possible, reducing the impact on the scheduling transmission potential.

Compared with the prior art, the beneficial effects of the present invention are:

The multi-channel underwater acoustic network transmission scheduling method based on time-domain interference alignment considers the message scheduling problem in the multi-channel transmission model of underwater acoustic network, and uses the time-domain interference alignment idea to design the scheduling algorithm to improve the network throughput. The multi-channel network transmission model can accommodate more users and reduce data conflicts, but a user can only use one channel to transmit messages at a time, so a reasonable scheduling algorithm can make better use of the channel and improve throughput. Time-domain interference alignment can align interference at non-destination nodes, avoid interference when the destination node receives messages, and reduce data collisions. Increased network throughput.

Description of drawings

1 is an overall flow chart of a preferred embodiment of a multi-channel transmission scheduling method based on time-domain interference alignment in an underwater acoustic network provided by the present invention;

2 is a schematic diagram of a four-node network topology provided by the present invention;

Fig. 3 is the t-slot transmission scheduling update process pseudocode provided by the present invention;

FIG. 4 is the throughput result of the multi-channel scheduling method based on time-domain interference alignment in the underwater acoustic network provided by the present invention as the number of nodes changes.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

It should be noted that like numerals and letters refer to like items in the following figures, so once an item is defined in one figure, it does not require further definition and explanation in subsequent figures.

The present invention is based on the following scenarios: nodes in the network use multiple channels to transmit messages, each node is equipped with an antenna, the antenna can use C channels for information transmission, and each time slot node can only use one of the channels. The present invention considers a single collision domain model, except for the source node and the destination node, the message interferes with other nodes. Nodes in the network are limited to half-duplex, that is, they cannot transmit or receive messages at the same time. The node uses the single-packet receiving model, and the destination node can only receive one message at the same time. When two or more messages arrive at a node at the same time, a conflict occurs and all messages cannot be received normally.

1-4, the present invention provides a technical solution: a multi-channel transmission scheduling method based on time-domain interference alignment in an underwater acoustic network, including the following stage modes:

S1 network topology representation: the propagation delay between nodes refers to the time required for a signal to be transmitted from a source node to a destination node in a given medium. The present invention uses a transmission scheduling matrix T to represent the network topology, and the elements in the matrix are the pairs of nodes. The number of time slots required for inter-propagation;

S2 transmission scheduling matrix initialization: The transmission schedule S specifies the transmission situation of the node in each time slot. Efficiently transmit and receive messages according to the scheduling matrix nodes to maximize throughput. In the initial stage of the network, the transmission scheduling matrix is empty, and then the transmission scheduling matrix of each time slot is continuously updated;

S3 optimal transmission decision search: Finding the optimal scheduling problem can be regarded as a sequential decision problem, and dynamic programming is used to solve this problem. The qualified decision is determined by using the time-domain interference alignment idea and network multi-channel transmission conditions, and then the value function is determined to find the optimal transmission decision among the qualified decisions.

S4 transmission scheduling update mode: the optimal decision is added to the current scheduling and a transmission scheduling update is completed, and the decision judgment and optimal transmission decision search are performed on all possible transmissions again until there is no qualified decision, and the scheduling update of a time slot is completed. A time slot update. The scheduling update process is cyclically repeating steps S3 and S4.

In the step S1, for a network containing N nodes, the network topology is represented by an N*N matrix T, and the element T _ij in the matrix represents the number of time slots required for message propagation between a node pair (i, j). , for the convenience of analysis, we assume that T _ij are all integers, as shown in formula (1),

Among them, τ is the length of a time slot, d _ij is the propagation delay between the node pair (i, j), and the propagation delay refers to the time required for a signal to travel from the source node to the destination node in a given medium, as shown in Eq. As shown in (2),

D _ij is the distance between node i and node j, and v is the propagation speed of the signal. In the underwater acoustic network, the underwater acoustic signal is used to carry information, and its propagation speed is about 1500 m/s. In the initial state of the network, first calculate the number of propagation delay time slots between nodes according to the node position, and use this to represent the network topology. ).

Wherein, T ₁₃ =2 indicates that the number of propagation delay time slots between node 1 and node 2 is 2, T _ij =T _ij , and T _ii =0.

In the step S2, the transmission scheduling determines the transmission state of the node in each time slot. The three-dimensional matrix S={S _i,c,t } represents the transmission scheduling model. If S _{i, c, t} = j > 0, it means that node i in time slot t uses the c channel to transmit messages to node j; if S _{i, c, t} = -j < 0, it means that node i in time slot t uses c The channel receives messages from node j; if S _{i, c, t} = 0, it means that node i in time slot t is in an idle state.

In a schedule, node i in time slot t transmits a message to node j using channel c. After a propagation delay of T _ij time slots, node j in time slot t + T _ij receives a message from node i on channel c, so

Since the node can only select one channel per time slot, all

The step S3 includes:

S31, modeling a sequential decision problem;

S32, using the time domain interference alignment idea and the network multi-channel transmission conditions to make a qualified decision decision;

S33, determine the value function, use the dynamic programming algorithm to solve the sequential decision problem, and find the optimal transmission decision.

In the step S31, each transmission scheduling is regarded as a decision, denoted by x ^{{t, h}} , and the state of the decision problem is denoted by S ^{t} , including all transmissions made up to time slot t. A new decision and the current state jointly determine a new state, and all decisions in time slot t and the state in time slot t determine the state in time slot t+1, as shown in equation (3),

S ^{{t+1, 1}} = Π(S ^{t} , x ^{t} )#(6)

where x ^{t} is the set of all transmissions in time slot t, and Π( ) is the state transition function.

Only the transmission that satisfies the time-domain interference alignment and multi-channel network transmission conditions in the step S32 is regarded as a qualified decision. For a partial schedule S ^{t} of a t slot, a qualified transmission decision at slot t' needs to satisfy the following constraints (t'=t+δ):

Condition 1: The node cannot self-transmit, that is, j≠k;

Condition 2: The source node j needs to keep the idle state in the t' time slot, and the node can only use one channel to transmit messages in a time slot, so in the scheduling S ^{t} , the source node j in the t' time slot is in any channel must be in an idle state, i.e.

Condition 3: Due to the half-duplex and single-packet reception limitations of the node, the destination node k of the t′+T _jk time slot is in an idle state on any channel in the scheduling S ^{t} , that is,

Condition 4: A node can only receive one message in a time slot, so it is necessary to ensure that no other message arrives when the destination node k receives the message from node j at t'+T _jk , which means that any node i is at t'+T _jk . -T _ik slots cannot transmit messages to k, i.e.

Condition 5: The node in the network is a single-packet receiving model, when two or more messages arrive at a node at the same time, all messages cannot be received. In order to ensure that the message transmitted from node j to node k does not affect the ongoing transmission (m, n), it should be avoided that the message from node j and the message from node m arrive at node n at the same time, i.e.

满足上述限制条件，则指示函数

表示对于调度S^{t}传输

为合格传输；否则当传输

不满足上述任一限制条件时，指示函数

when transmitting

Satisfying the above constraints, the indicator function

represents that for a schedule S ^{t} transmission

is a qualified transmission; otherwise, when the transmission

When any of the above constraints are not met, the indicator function

通过判断新调度

中合格决策的个数可评价新决策，因此价值函数可表示为：In the step S33, the dynamic programming algorithm to solve the sequential decision problem needs to determine the value function, which is used to find the optimal decision. Different new decisions added to the current schedule S ^{t} will form different new schedules

By judging the new schedule

The number of qualified decisions in the new decision can be evaluated, so the value function can be expressed as:

The optimal decision can maximize the value function. Here, since each new decision will add new interference, the optimal decision can ensure that the interference and the previous interference overlap as much as possible, reducing the impact on the scheduling transmission potential. The optimal schedule is

Further, the step S4 includes:

S41, allocate a channel for the optimal transmission decision;

S42, the optimal transmission decision is added to the current scheduling S ^{t} to form a new scheduling S ^{{t, 1}} ;

S43, go to step S32 to continue to find new optimal scheduling, go to step S41, until there is no qualified transmission decision;

S44, the scheduling update of the t time slot is completed, and the update of the t+1 time slot is performed.

The update process of the example t-slot transmission schedule in Figure 3 is as follows:

Step (1): N nodes in the network have a total of N*N possible two-tuple combinations, denoted as (j, k);

Step (2): carry out decision-making judgment on N*N two-tuple combinations, and the qualified two-tuple groups are added to the decision space ψ;

Step (3): Determine whether the decision space is empty, if it is empty, it means that there is no transmission update available in the current time slot, S ^{t+1} = S ^(t) , the t time slot transmission scheduling update is completed, and jump to Step (1) performs t+1 time slot update; if the decision space is not empty, jump to step (4);

Step (4): The transmission decisions of the decision space are added to the current transmission schedule S ^{t} to form Π(S ^{t} , x), and the value of the new transmission schedule is calculated according to the value function (as shown in equation (7)). transmission potential;

Step (5): The transmission decision with the greatest transmission potential of the new transmission scheduling is the optimal transmission decision, and the channel is allocated for the optimal transmission decision.

Step (6): Add the optimal decision x ^* to S ^{t} to form a new transmission schedule

一次传输调度更新完成，跳转到步骤(1)进行下一次传输调度更新。

Once the transmission schedule update is completed, jump to step (1) to perform the next transmission schedule update.

The effect of the present invention is illustrated by the simulation results:

The experiment is simulated by Java programming to realize the scheduling algorithm in the present invention. The purpose of the present invention is to find a scheduling algorithm that can achieve optimal throughput. The throughput Y of a schedule in a period of time can be represented by the number of transmitted messages or the number of received messages in the schedule,

Among them, T is the number of time slots spent in the whole scheduling, 1A(x) is the indicator function, when x is true, the value of 1A(x) is 1, otherwise, the value of 1A(x) is 0.

Assuming that there are enough messages to be transmitted in each time slot in the network, and changing the number of nodes, the result of network throughput is shown in Figure 4. The network throughput has a linear relationship with the number of nodes, because the scheduling algorithm uses the idea of time-domain interference alignment, so that nodes can transmit messages in the interfered time slot, which improves the channel utilization. And the multi-channel model allows multiple nodes to use the channel at the same time, increasing the network throughput.

The mathematical proof of the maximum throughput achievable by the network is as follows:

Assuming that the network contains N nodes, there are NT elements in the schedule S with T time slots, and the elements in the schedule S are the transmission status of the node, including the transmission message, the received message and the idle,

In the optimal scheduling, when the nodes are not in an idle state, all nodes transmit or receive messages in all time slots.

The number of messages received in the schedule is equal to the number of transmissions, so

Combining formula (5) and formula (8), we have

The multi-channel underwater acoustic network transmission scheduling method based on time-domain interference alignment considers the message scheduling in the multi-channel network environment, and uses the idea of time-domain interference alignment to design the scheduling algorithm, aiming to improve the network throughput. Time domain interference alignment refers to aligning the interference at the non-destination node to ensure that the destination node has no interference in the time slot that receives the message. Minimize data conflicts as much as possible. However, multi-channel transmission can realize that multiple nodes transmit messages without conflict in the same time slot, and can also use the time slot that has been interfered for message transmission, which improves the channel utilization rate. The present invention regards finding the optimal scheduling problem as a sequential decision problem, and uses the dynamic programming method to solve this problem. When making decisions, qualified decisions need to meet the time-domain interference alignment idea and multi-channel transmission constraints. An approximate value function is determined according to the objective of maximizing throughput to select the transmission decision with the least impact on future transmission potential as the optimal decision. The invention provides a method for determining transmission scheduling for a given network topology, the scheduling can maximize throughput, improve network performance, and has high application value.

It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any relationship between these entities or operations. any such actual relationship or sequence exists. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of the present invention. within the scope of protection.

Claims (8)

1. A multi-channel transmission scheduling method based on time domain interference alignment in an underwater acoustic network is characterized in that:

the method comprises the following steps:

s1, network topology representation: the propagation delay among the nodes refers to the time required by signals to be transmitted from a source node to a destination node in a given medium, a transmission scheduling matrix T is used for representing a network topological structure, and the number of time slots required by the propagation of messages among the node pairs is the elements in the matrix;

s2, initializing a transmission scheduling matrix: the transmission scheduling S appoints the transmission condition of the nodes in each time slot, and efficiently transmits and receives messages according to the scheduling matrix nodes so as to reduce data collision and improve throughput as much as possible, wherein the transmission scheduling matrix is empty in the initial stage of the network, and then the transmission scheduling matrix of each time slot is continuously updated;

s3, searching an optimal transmission decision: finding an optimal scheduling problem can be regarded as a sequential decision problem, solving the problem by using dynamic programming, carrying out qualified decision judgment by using a time domain interference alignment idea and network multichannel transmission conditions, and then determining a cost function to find an optimal transmission decision in the qualified decisions;

s4, transmission scheduling update mode: the optimal decision is added into the current scheduling, one-time transmission scheduling updating is completed, decision judgment and optimal transmission decision searching are carried out on all possible transmissions again until no qualified decision is made, the scheduling updating of one time slot is completed, and the updating of the next time slot is carried out; the updating process is scheduled, i.e., the repeated steps of S3 and S4 of the loop.

2. The method for scheduling multi-channel transmission based on time domain interference alignment in an underwater acoustic network according to claim 1, wherein: in step S1, for a network including N nodes, the network topology is represented by a propagation delay matrix T of N × N, and the elements T in the matrix_ijIndicating the number of time slots required for message propagation between a node pair (i, j), as shown in equation (1),

where τ is the length of a time slot, d_ijThe propagation delay, which is the propagation delay between a node pair (i, j), refers to the time required for a signal to travel from a source node to a destination node in a given medium, as shown in equation (2),

D_ijand v is the propagation speed of the signal, and the underwater sound signal is used in the underwater sound network to carry information.

3. The method of claim 1, wherein the method comprises scheduling the multi-channel transmission based on the time domain interference alignment in the underwater acoustic network: the transmission schedule in step S2 determines the transmission status of the node in each time slot, and the node transmits the message according to the transmission schedule, where the three-dimensional matrix S ═ S_i，c，tDenotes the transmission scheduling model, if S_i，c，tJ is greater than 0, which means that the t-slot node i transmits a message to the node j by using the c channel; if S_i，c，tIf j is less than 0, it means that t time slot node i receives the message from node j using c channel; if S_i，c，tAnd 0, it means that the t-slot node i is in an idle state.

4. The method for scheduling multi-channel transmission based on time domain interference alignment in underwater acoustic network according to claim 1, wherein said step S3 includes:

s31, modeling a sequential decision problem;

s32, performing qualified decision judgment by using a time domain interference alignment idea and a network multichannel transmission condition;

and S33, determining a value function, solving a sequential decision problem by using a dynamic programming algorithm, and finding an optimal transmission decision.

5. The method for scheduling multi-channel transmission based on time domain interference alignment in underwater acoustic network according to claim 1, wherein said step S4 includes:

s41, allocating channels for the optimal transmission decision;

s42, adding the optimal transmission decision into the current scheduling S^{t}In (1), a new schedule S is formed^{t，1}；

S43, continuing to search for new optimal scheduling in the step S32, and carrying out the step S41 until no qualified transmission decision is made;

and S44, finishing the scheduling updating of the t time slot, and updating the t +1 time slot.

6. The method according to claim 4, wherein the method comprises: each transmission schedule in said step S31 can be regarded as a decision, denoted by x^{t，h}Question of decisionThe status of the question is represented by S^{t}Indicating that all transmissions made until t slot are involved, a new decision and the current state together determine a new state, and all decisions of t slot and states of t slot determine the state of t +1 slot, as shown in equation (3),

S^{t+1}＝Π(S^{t}，x^{t})#(3)

wherein x^{t}Is the set of all transmissions in the t slot, pi () is the state transfer function;

since the state of the next slot is determined by the state of the present slot and the transmission decision, it becomes critical to find the optimal transmission decision within each slot.

7. The method according to claim 4, wherein the method comprises: the transmission satisfying the time domain interference alignment and the multi-channel network transmission condition in the step S32 is regarded as a qualified decision, and S is scheduled for a part of a t slot^{t}The eligible transmission decision at t 'slot requires the following constraints to be met (t' ═ t + δ):

the node can not transmit by itself, i.e. j is not equal to k;

the source node j needs to keep idle state in t' time slot, and the node can only use one channel to transmit message in one time slot, so that S is scheduled^{t}The source node j of the middle t' time slot needs to be in an idle state in any channel, namely

Due to half-duplex and single-packet reception limitations of the node, at scheduling S^{t}Middle T + T_jkThe destination node k of the time slot is in idle state on any channel, i.e. it is in idle state

The node can only receive one message in one time slot, and all the nodes need to ensure that the destination node k is at T' + T_jkNo other messages arrive when a message from node j is received, meaning that any node i is at T' + T_jk-T_ikThe time slot being unable to transmit messages to k, i.e.

The nodes in the network are single-packet reception models, when two or more messages arrive at a node at the same time, all messages cannot be received, and in order to ensure that the message transmitted from the node j to the node k does not affect the ongoing transmission (m, n), the simultaneous arrival of the message from the node j and the message from the node m at the node n, that is, the simultaneous arrival of the message from the node j and the message from the node m at the node n should be avoided, that is, the node n is a node with a single-packet reception model

When transmitting

If the above-mentioned limiting conditions are satisfied, the function is indicated

Indicating S for scheduling^{t}Transmission of

Qualified transmission is carried out; otherwise when transmitting

Indicating function when the above-mentioned limiting condition is not satisfied

8. The method according to claim 4, wherein the method comprises: the above-mentionedIn step S33, the dynamic programming algorithm for solving the sequential decision problem needs to determine a cost function for finding the optimal decision, and different new decisions are added to the current scheduling S^{t}Different new schedules will be formed

By judging new schedules

The number of medium qualified decisions evaluates the new decision, so the cost function can be expressed as:

the optimal decision can maximize the cost function, and because new interference is added every time a new decision is added, the optimal decision can ensure that the brought interference and the previous interference are overlapped as much as possible, and the influence on scheduling transmission potential is reduced.

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