CN118054888A - HARQ-based state updating method in cognitive Internet of things - Google Patents
HARQ-based state updating method in cognitive Internet of things Download PDFInfo
- Publication number
- CN118054888A CN118054888A CN202410297085.8A CN202410297085A CN118054888A CN 118054888 A CN118054888 A CN 118054888A CN 202410297085 A CN202410297085 A CN 202410297085A CN 118054888 A CN118054888 A CN 118054888A
- Authority
- CN
- China
- Prior art keywords
- state
- things
- secondary network
- relay
- transmission
- 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.)
- Pending
Links
- 230000001149 cognitive effect Effects 0.000 title claims abstract description 69
- 238000000034 method Methods 0.000 title claims abstract description 45
- 230000005540 biological transmission Effects 0.000 claims abstract description 85
- 238000005457 optimization Methods 0.000 claims abstract description 37
- 238000004891 communication Methods 0.000 claims abstract description 24
- 230000008569 process Effects 0.000 claims abstract description 13
- 230000006870 function Effects 0.000 claims description 30
- 230000009471 action Effects 0.000 claims description 15
- 238000001228 spectrum Methods 0.000 claims description 12
- 230000007704 transition Effects 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 6
- 230000008447 perception Effects 0.000 claims description 4
- 230000006399 behavior Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 230000000737 periodic effect Effects 0.000 claims description 2
- 238000006467 substitution reaction Methods 0.000 claims description 2
- 230000004044 response Effects 0.000 abstract description 2
- 230000007423 decrease Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005562 fading Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000002040 relaxant effect Effects 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
Landscapes
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention discloses a state updating method based on HARQ in a cognitive Internet of things, which comprises the steps of firstly constructing a transmission model of a cognitive Internet of things relay system, establishing a cognitive Internet of things state updating information age optimization function according to the transmission model, then solving the optimization function based on a constrained Markov decision problem to obtain an optimal transmission strategy of the cognitive Internet of things, and carrying out data transmission of the cognitive Internet of things relay system based on the optimal transmission strategy; and finally updating the system state information according to the data transmission result, and repeating the steps. According to the scheme, the sending strategy can be dynamically adjusted according to the method in the state updating process of the secondary network of the cognitive Internet of things, the state updating performance of the system can be improved while the communication service quality of the primary network is ensured, meanwhile, the HARQ protocol is utilized to correct the error packet generated by the short packet relay transmission in the state updating scene of the cognitive Internet of things, so that the transmission time delay is reduced, and the instantaneity and the response speed are improved.
Description
Technical Field
The invention belongs to the field of state updating of the cognitive Internet of things, and particularly relates to a HARQ-based state updating method in the cognitive Internet of things.
Background
The scarce spectrum resource becomes a bottleneck of the development of the internet of things, and the cognitive radio is considered as a potential solution, and can enable the secondary network internet of things equipment to share the spectrum with the primary network, so that the development of the cognitive internet of things is promoted. However, in many cognitive internet of things scenarios requiring remote communication (such as telemedicine, environmental monitoring and intelligent agriculture), due to cost and energy consumption limitations, the cognitive internet of things device often cannot directly transmit signals to the base station, and the relay transmission technology becomes a common communication solution, which can effectively prolong the signal transmission distance and improve the signal quality and coverage.
Ensuring the information freshness of the cognitive internet of things relay system in these emerging scenes is important, so that we can effectively monitor and react. In order to quantify the freshness of the information, the invention adopts the information age (S.K.Kaul,R.D.Yates,and M.Gruteser,"Real-time status:How often should one update?"in Proc.IEEE INFOCOM,2012,pp.2731–2735.) proposed by S.K. Kaul et al as a performance index. In addition, in the internet of things, short packet communication is more common, and unlike traditional communication, the state update process based on short packet communication can generate more false packets. The erroneous packet caused by the short packet communication will increase the information age of the system, making the information obsolete and invalid. Therefore, appropriate techniques must be employed to address the limitations of short packet communications and improve the reliability of the system.
The automatic repeat request (Automatic Repeat request, ARQ) protocol is considered an important technique to improve the reliability of the system. Under ARQ protocol, the sender waits for an Acknowledgement (ACK) to be received for a certain period of time, and if no acknowledgement is received, retransmits. In fact, some previous studies have discussed the information age performance under ARQ protocols. In literature (B.Yu,Y.Cai,D.Wu,and Z.Xiang,"Average age of information in short packet based machine type communication,"IEEE Trans.Veh.Technol.,vol.69,no.9,pp.10306-10319,Sep.2020;Y.Gu,H.Chen,Y.Zhou,Y.Li and B.Vucetic,"Timely status update in Internet of Things monitoring systems:An age-energy tradeoff,"IEEE Internet Things J.,vol.6,no.3,pp.5324-5335,Jun.2019;D.Li,S.Wu,Y.Wang,J.Jiao,and Q.Zhang,"Age-optimal HARQ design for freshness-critical satellite-IoT systems,"IEEE Internet Things J.,vol.7,no.3,pp.2066-2076,Mar.2020.), authors devised ARQ, TARQ (TruncatedARQ) and HARQ (HybridARQ), respectively, to optimize system information age. Although these papers have studied the above protocols, there are still few studies on more complex cognitive internet of things relay systems, and the transmission strategies of the above documents are fixed and are not applicable to cognitive internet of things with rapidly changing spectrum environments.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a state updating method based on HARQ in the cognitive Internet of things, which fully utilizes the information age of a system, the busyness of a main network and the retransmission times of an HARQ protocol and optimizes the sending strategy of a secondary network in each time slot. Under the cognitive internet of things relay transmission framework, the method models the minimum system average information age problem as a constrained Markov decision process, further converts the transmission strategy design problem into a linear programming problem through variable replacement, and obtains the optimal transmission strategy. Based on the strategy, the secondary network can dynamically adjust the sending strategy according to the instruction of the control center in the actual state updating process, so that the information freshness of the control receiving end is effectively improved while the collision of the system data packet is relieved, and the average information age of the system is reduced.
The specific technical scheme for realizing the purpose of the invention is as follows:
a HARQ-based state updating method in the cognitive Internet of things comprises the following steps:
Step 1, constructing a transmission model of a cognitive Internet of things relay system, and accordingly establishing an optimization function of the state update information age of the cognitive Internet of things;
Step 2, solving the optimization function in the step 1 to obtain an optimized transmission strategy of the cognitive Internet of things;
step 3, carrying out data transmission of the cognitive Internet of things relay system according to the optimized transmission strategy obtained in the step 2;
and 4, updating the system state information according to the data transmission result, and repeating the contents of the steps 3 to 4.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the scheme, the HARQ protocol is utilized to correct the error packet generated by the short packet relay transmission in the state updating scene of the cognitive Internet of things, so that the transmission time delay is reduced, and the instantaneity and the response speed are improved;
(2) According to the method, the sending strategy can be dynamically adjusted according to the method in the state updating process of the secondary network of the cognitive Internet of things, so that the state updating performance of the system can be improved while the communication service quality of the primary network is ensured.
The invention is further described in connection with the following detailed description.
Drawings
Fig. 1 is a schematic diagram of a cognitive internet of things relay system architecture according to the present invention.
Fig. 2 is a flow chart of a state updating method based on HARQ in the cognitive internet of things according to the present invention.
Fig. 3 is a schematic diagram showing the variation of average information age with collision constraint under different ARQ schemes according to an embodiment of the present invention.
Fig. 4 is a schematic diagram showing the average information age according to the packet length of the secondary network according to the embodiment of the present invention.
Detailed Description
Referring to fig. 1 and 2, a state updating method based on HARQ in a cognitive internet of things includes the following steps:
step 1, constructing a transmission model of a cognitive Internet of things relay system, and accordingly establishing an optimization function of the state update information age of the cognitive Internet of things:
the cognitive Internet of things relay system comprises a main network and a secondary network, wherein the secondary network comprises a secondary network transmitting end, a relay and a secondary network receiving end;
The secondary network monitors the channel before sending the data packet each time, and when the channel is idle, namely the primary network does not communicate, the secondary network can access the channel and periodically send the state update data packet; the main network can access the channel at any time;
because the secondary network does not sense the channel in the transmitting process and the primary network can access the channel at any time, when the secondary network accesses the channel in the transmitting process, the secondary network collides with the primary network, and the receiving end cannot decode the information in the data packet.
When the secondary network transmitting end detects that the channel is idle and the transmitting strategy is transmitting, the secondary network transmitting end transmits periodic perception state data short packets to the relay in a first hop, and transmits the perception state data short packets successfully received by the relay to the secondary network receiving end in a second hop by adopting an HARQ protocol;
in the process of updating the state, in order to ensure the communication service quality of the main network, a sending strategy is determined according to the information age of the current system and the retransmission times of the second hop; if the secondary network receiving end successfully decodes the received state update short packet, updating the state information of the secondary network receiving end about the target;
Because the secondary network does not sense the channel in the transmitting process and the primary network can access the channel at any time, when the secondary network accesses the channel in the transmitting process, the secondary network collides with the primary network, and the receiving end cannot decode the information in the data packet. To ensure the communication service quality of the primary network, we need to constrain the transmission behavior of the secondary network.
The main network can access the channel at any moment, the idle time and the time for transmitting the data packet are respectively subjected to exponential distribution with the coefficients of alpha and beta, and as the main network occupies sparse frequency spectrum, namely beta is more than alpha, the secondary user can transmit the data packet by using blank frequency spectrum resources.
In order to evaluate the impact of a collision on the primary network, the present invention is used toRepresenting the average collision probability of the primary network, for the secondary network, the collision probability refers to the probability of the secondary network colliding during communication. The primary network communication can be protected by constraining the collision probability.
Average collision probability of a primary network in a systemAnd average collision probability of secondary network/>The method comprises the following steps:
Where N c denotes the total number of collisions that occur during the total time slot N, N n denotes the number of busy-idle periods, L p denotes the length of the busy-idle period of one of the primary networks, Representing mathematical expectations;
By the above two formulas, it is possible to obtain:
It may be noted that the number of the elements, And/>There is a certain relationship between them. Accordingly, the constraint on the collision probability of the primary network (defined as η p) can be translated into a constraint on the collision probability in the secondary network (defined as η s), i.e./>
In order to ensure the reliability of the system and obtain fresher information at the receiving end, in the cognitive internet of things relay system, an HARQ protocol is used for processing error packets generated by short packet communication. In this scheme, the relay system can operate in full duplex mode, which means that two-hop transmission can be performed simultaneously. When the channel is idle in the first hop, it periodically generates and transmits status update short packets.
When the secondary network sends a short packet, the packet error rate epsilon 1 of the first-hop short packet communication is as follows:
In the second hop, when the relay successfully receives the status update short packet, the HARQ protocol is adopted for transmission. If the receiving end cannot decode successfully, it will not send an acknowledgement signal, and the repeater will resend the same short packet; due to the use of the HARQ protocol, the packet error rate of the second hop short packet communication is:
Wherein, N represents the data packet length, gamma 1 and gamma 2 represent the signal to noise ratio of the secondary network relay and the secondary network receiving end respectively, l represents the retransmission times, D represents the information content of the short packet, and Q (-) represents the right tail function of standard normal distribution;
under the HARQ protocol, if the transmission fails, the relay will send the same data packet, so the receiving end may combine the received retransmission data packets by using the maximum ratio combining method.
Considering the course of the system relay and receiver information age, when the case of erroneous decoding per slot occurs, the information age (Δ r(n),Δd (n)) of the relay and receiver receiving packet increases linearly by 1. This erroneous decoding is caused by: 1) Collisions of primary and secondary networks during transmission; 2) The state updating short packet is sent to generate an error packet; 3) The primary network occupies the channel and the secondary network remains silent.
The average information age of the secondary network receiving end is as follows:
since different transmission strategies will lead to different collision probabilities of the system, the average collision probability of the system is:
Where T represents the total time slot of transmission, Δ d represents the information age of the receiving end, and d (n) is the collision probability at time slot n.
The control center can adjust the transmission strategy according to the received feedback information. In particular, it may send simple instructions to the secondary network sender and relay according to the current state of the secondary network device, and then the secondary network device may take action according to these instructions, the information age optimization problem in the system may be expressed as:
where η s represents a constraint on collision probability in the secondary network;
And solving the optimization objective function to obtain an optimal transmission strategy F, and optimizing the information age and simultaneously meeting the communication service quality of the main network.
Step 2, solving the optimization function in the step 1 to obtain an optimized transmission strategy of the cognitive Internet of things:
Considering that the system channel state changes slowly, the sending strategy can be selected according to actual requirements, and the state updating performance can be improved by dynamically adjusting the sending strategy, so that the optimization objective function is converted into a constrained Markov decision problem to be solved;
Wherein, the state space is: a state s (n) = (l, u, delta r,Δd) at the beginning of the nth time slot, wherein delta r and delta d are respectively represented as information ages of a secondary network relay and a secondary network receiving end, u (n) represents a busy state of a primary network, and l represents retransmission times adopting an HARQ protocol;
the action space is that in each time slot, the secondary network control center decides whether to send data packet according to the current system state; there are two possibilities of the transmission behavior a (n) of the secondary network in the nth time slot, a (n) =1 indicates that the secondary network transmits a data packet in the nth time slot, and a (n) =0 indicates that the secondary network is silent in the nth time slot;
the transition probability is Pr (s (n+1) |s (n), a (n)) indicates the probability that the secondary network takes action a (n) when the n-th slot system state is s (n), and the (n+1) -th slot system state becomes s (n+1), wherein:
the state transition probability when no packet is relayed is expressed as:
the state transition probability when a relay has a packet is expressed as:
wherein, when the relay does not have a buffer, the information age delta r = -1 of the relay side, The transition probability is expressed as the channel use condition in adjacent time slots of the main network, p ij, I, j E { I, B }, wherein I represents that the frequency spectrum of the main network system is idle, and B represents that the frequency spectrum of the main network system is busy;
the cost function is: taking the cost d (s (n), a (n)) =a (n) u (n) +a (n) (1-u (n)) (1-e -α) of action a (n) in the nth slot, where α is a coefficient of the idle time index distribution;
based on this, the average information age and collision probability of the system are expressed as
Wherein pi is the steady state probability that the system is in a steady state after a long time transmission process, S is a state space, pi s ε pi is the probability of state S, f s a is the probability of taking action a in state S, and d (S, a) is the collision probability of taking action a in state S;
The objective of this optimization problem is to find an optimal transmission strategy that minimizes the average information age while meeting constraints. This can be accomplished by converting constrained Markov decisions into a mathematical optimization problem, which can be expressed as:
in order to make the optimization problem easy to calculate, we can convert the optimization problem into a linear programming problem through variable substitution, and the problem after conversion is equivalent to the original optimization problem:
And performing variable replacement on the optimized objective function, so that the replaced optimized objective function is solved by using a linear programming method:
||π||＝1.
By solving the above-mentioned optimization problem, the optimal values pi and μ of pi and μ can be obtained for each state s, and then the optimal transmission policy F can be obtained.
The complexity of solving the optimization problem is thatThe complexity of the algorithm is lower than an exponential complexity exhaustive search algorithm.
Step 3, carrying out data transmission of the cognitive internet of things relay system according to the optimized transmission strategy obtained in the step 2:
The control center can learn the information age of the system and the retransmission times of the HARQ protocol through the decoding conditions fed back by the secondary network relay and the receiving end, and before each time slot starts, the control center perceives the channel spectrum state and sends corresponding instructions to the transmitting end and the relay of the secondary network according to the state of the system at the moment and the optimized transmission strategy obtained in the step 2.
And 4, updating the system state information according to the data transmission result, and repeating the contents of the steps 3 to 4.
When the time slot starts, the sending end and the relay of the secondary network can select actions and execute the actions according to the instructions;
If the sending operation is selected, in the first hop, the secondary network periodically senses the surrounding state and sends updated information to the relay by short packet coding;
In the second hop, the system uses the HARQ protocol, the sending end retransmits the short packet with failed decoding, and the secondary network receiving end uses the maximum ratio to combine the information of repeated transmission to decode and correct errors;
in the process, if the relay originally has a data packet, the data packet is transmitted first by adopting a full duplex working mode, so that the efficiency of data transmission can be improved;
And (3) the relay and receiving end decodes the sent state update data packet, if the data packet is successfully received, the original data packet is replaced, and the step (3) is returned.
A state updating system based on HARQ in the cognitive Internet of things comprises the following modules:
Model construction module: the method comprises the steps of constructing a transmission model of a cognitive Internet of things relay system, and accordingly establishing an optimization function of the cognitive Internet of things;
And a strategy solving module: the optimization function is used for solving the optimization function to obtain an optimization transmission strategy of the cognitive Internet of things;
The execution module: and the data transmission method is used for carrying out data transmission of the cognitive Internet of things relay system according to the acquired optimized transmission strategy.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of:
step 1, constructing a transmission model of a cognitive Internet of things relay system, and accordingly establishing an optimization function of the cognitive Internet of things;
Step 2, solving the optimization function in the step 1 to obtain an optimized transmission strategy of the cognitive Internet of things;
step 3, carrying out data transmission of the cognitive Internet of things relay system according to the optimized transmission strategy obtained in the step 2;
and 4, updating the system state information according to the data transmission result, and repeating the contents of the steps 3 to 4.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps of:
step 1, constructing a transmission model of a cognitive Internet of things relay system, and accordingly establishing an optimization function of the cognitive Internet of things;
Step 2, solving the optimization function in the step 1 to obtain an optimized transmission strategy of the cognitive Internet of things;
step 3, carrying out data transmission of the cognitive Internet of things relay system according to the optimized transmission strategy obtained in the step 2;
and 4, updating the system state information according to the data transmission result, and repeating the contents of the steps 3 to 4.
Examples
The simulation parameters in this embodiment are set as follows:
The transmission power of the secondary network transmitting end and the relay is 0.01W, the distance between the secondary user receiving and transmitting end is 220m, the small-scale fading factor is 3.3, shadow fading χ 0 = -50dB, the power spectrum density of noise is-174 dBm/Hz, the network available bandwidth B=180 kHz, the data packet length of each time slot is 100 channels users (cu), the quantity of state information is D=120 nats, the information age upper limit is 15 time slots, and the maximum retransmission times are 5. If not specified, the simulation sets the busy period negative exponent distribution coefficient to 0.02, the idle period negative exponent distribution coefficient to 0.4, and the maximum collision probability of the secondary network to 0.002.
In fig. 3, by relaxing the collision constraint, a decrease in average information age can be observed. This is because in the cognitive internet of things relay system, collision constraints limit transmission opportunities, thereby affecting the ability to update status information, resulting in an increase in information age.
When the collision constraint is stricter, i.e. the collision probability is higher, the secondary network has less chance to send a status update, and thus the information age increases. However, there is a threshold beyond which increasing the collision constraint will not further decrease the average information age.
In this regard, the original problem may be converted into a problem of a Markov decision process. Furthermore, by comparing the HARQ protocol with the ARQ protocol, we find that the HARQ protocol can achieve lower AoI because the use of maximum ratio combining in HARQ can effectively reduce the packet error rate of short packet transmissions.
In fig. 4, we have studied the relationship between the average information age at the receiving end of the system and the short packet length. It can be found that a certain trade-off relation exists between the information age of the system and the packet length of the short packet; increasing the short packet length can reduce the packet error rate and improve the reliability, but at the same time may also lead to a reduction in freshness.
The influence of the relay on the system information age is also studied, and simulation results show that when the state update short packet is short, the system packet error rate is close to 1, communication interruption is caused by lack of the relay, and the system reliability is weak. However, as the packet length increases, the packet error rate of the system decreases, and at the same time, the transmission time of one hop can be reduced by using single hop transmission, at this time, HARQ cannot significantly improve the performance, which finally results in that when the packet block length is longer, the performance of the single hop is better than that of the relay transmission.
In addition, we also analyze the influence of the active frequency of the primary network on the information age of the secondary network receiving end, and can know that increasing the busy time of the primary network can cause the information freshness to be poor by changing the average duration of the busy state. In addition, the longer the busy time of the main network, the greater the impact on the relay transmission compared to single hop transmission.
Because the information amount in the state updating scene is smaller, the packet length is generally shorter, and by comparing with other schemes, the scheme can be found to obtain more reliable communication and more credible information in the state updating scene.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. The HARQ-based state updating method in the cognitive Internet of things is characterized by comprising the following steps of:
Step 1, constructing a transmission model of a cognitive Internet of things relay system, and accordingly establishing an optimization function of the state update information age of the cognitive Internet of things;
Step 2, solving the optimization function in the step 1 to obtain an optimized transmission strategy of the cognitive Internet of things;
step 3, carrying out data transmission of the cognitive Internet of things relay system according to the optimized transmission strategy obtained in the step 2;
and 4, updating the system state information according to the data transmission result, and repeating the contents of the steps 3 to 4.
2. The method for updating the state based on HARQ in the cognitive internet of things according to claim 1, wherein the transmission model of the cognitive internet of things relay system in step 1 specifically comprises:
the cognitive Internet of things relay system comprises a main network and a secondary network, wherein the secondary network comprises a secondary network transmitting end, a relay and a secondary network receiving end;
The secondary network monitors the channel before sending the data packet each time, and when the channel is idle, namely the primary network does not communicate, the secondary network can access the channel and periodically send the state update data packet; the main network can access the channel at any time;
When the secondary network transmitting end detects that the channel is idle and the transmitting strategy is transmitting, the secondary network transmitting end transmits periodic perception state data short packets to the relay in a first hop, and transmits the perception state data short packets successfully received by the relay to the secondary network receiving end in a second hop by adopting an HARQ protocol;
In order to ensure the communication service quality of the main network, a sending strategy is determined according to the information age of the current system and the retransmission times of the second hop; if the secondary network receiving end successfully decodes the received state update short packet, updating the state information of the secondary network receiving end about the target;
Average collision probability of a primary network in a system And average collision probability of secondary network/>The method comprises the following steps:
Where N c denotes the total number of collisions that occur during the total time slot N, N n denotes the number of busy-idle periods, L p denotes the length of the busy-idle period of one of the primary networks, Representing mathematical expectations;
When the secondary network sends short packets, the packet error rate epsilon 1 of the first-hop short packet communication and the packet error rate epsilon 2 of the second-hop short packet communication are as follows:
Wherein, N represents the data packet length, gamma 1 and gamma 2 represent the signal to noise ratio of the secondary network relay and the secondary network receiving end respectively, l represents the retransmission times, D represents the information content of the short packet, and Q (-) represents the right tail function of standard normal distribution;
The average information age of the secondary network receiving end and the average collision probability of the secondary network are expected to be:
Where T represents the total time slot of transmission, Δ d represents the information age of the receiving end, and d (n) is the collision probability at time slot n.
3. The method for updating state based on HARQ in the cognitive internet of things according to claim 2, wherein the optimization objective function in step 1 is:
where η s represents a secondary network collision constraint;
And solving the optimization objective function to obtain an optimal transmission strategy F, and optimizing the information age and simultaneously meeting the communication service quality of the main network.
4. The state updating method based on HARQ in the cognitive internet of things according to claim 3, wherein the optimization objective function is converted into a constrained Markov decision problem to be solved;
Wherein, the state space is: a state s (n) = (l, u, delta r,Δd) at the beginning of the nth time slot, wherein delta r and delta d are respectively represented as information ages of a secondary network relay and a secondary network receiving end, u (n) represents a busy state of a primary network, and l represents retransmission times adopting an HARQ protocol;
the action space is that in each time slot, the secondary network control center decides whether to send data packet according to the current system state; there are two possibilities of the transmission behavior a (n) of the secondary network in the nth time slot, a (n) =1 indicates that the secondary network transmits a data packet in the nth time slot, and a (n) =0 indicates that the secondary network is silent in the nth time slot;
the transition probability is Pr (s (n+1) |s (n), a (n)) indicates the probability that the secondary network takes action a (n) when the n-th slot system state is s (n), and the (n+1) -th slot system state becomes s (n+1), wherein:
the state transition probability when no packet is relayed is expressed as:
the state transition probability when a relay has a packet is expressed as:
wherein, when the relay does not have a buffer, the information age delta r = -1 of the relay side, The transition probability is expressed as the channel use condition in adjacent time slots of the main network, p ij, I, j E { I, B }, wherein I represents that the frequency spectrum of the main network system is idle, and B represents that the frequency spectrum of the main network system is busy;
the cost function is: taking the cost d (s (n), a (n)) =a (n) u (n) +a (n) (1-u (n)) (1-e -α) of action a (n) in the nth slot, where α is a coefficient of the idle time index distribution;
based on this, the average information age and collision probability of the system are expressed as
Wherein pi is the steady state probability that the system is in a steady state after a long time transmission process, S is a state space, pi s ε pi is the probability of state S, f s a is the probability of taking action a in state S, and d (S, a) is the collision probability of taking action a in state S;
The optimization objective function is expressed as:
5. The state updating method based on HARQ in the cognitive internet of things according to claim 4, wherein the optimization objective function is subjected to variable substitution, so that the substituted optimization objective function is solved by using a linear programming method:
||π||＝1.
By solving the above-mentioned optimization problem, the optimal values pi and μ of pi and μ can be obtained for each state s, and then the optimal transmission policy F can be obtained.
6. The method for updating the state based on HARQ in the cognitive internet of things according to claim 1, wherein the data transmission of the cognitive internet of things relay system in step 3 specifically comprises:
Before each time slot starts, sensing the channel spectrum state, and sending corresponding instructions to the sending end and the relay of the secondary network according to the state of the system at the moment and the optimized transmission strategy obtained in the step 2.
7. The method for updating the state based on HARQ in the cognitive internet of things according to claim 1, wherein the updating the system state information according to the data transmission result in step 4 specifically includes:
When the time slot starts, the sending end and the relay of the secondary network can select actions and execute the actions according to the instructions;
If the sending operation is selected, in the first hop, the secondary network periodically senses the surrounding state and sends updated information to the relay by short packet coding;
In the second hop, the system uses the HARQ protocol, the sending end retransmits the short packet with failed decoding, and the secondary network receiving end uses the maximum ratio to combine the information of repeated transmission to decode and correct errors;
In the process, if the relay originally has a data packet, adopting a full duplex working mode to perform initial transmission at the same time;
And (3) the relay and receiving end decodes the sent state update data packet, if the data packet is successfully received, the original data packet is replaced, and the step (3) is returned.
8. The HARQ-based state updating system in the cognitive Internet of things is characterized by comprising the following modules:
Model construction module: the method comprises the steps of constructing a transmission model of a cognitive Internet of things relay system, and accordingly establishing an optimization function of the state update information age of the cognitive Internet of things;
And a strategy solving module: the optimization function is used for solving the optimization function to obtain an optimization transmission strategy of the cognitive Internet of things;
The execution module: and the data transmission method is used for carrying out data transmission of the cognitive Internet of things relay system according to the acquired optimized transmission strategy.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-7 when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410297085.8A CN118054888A (en) | 2024-03-15 | 2024-03-15 | HARQ-based state updating method in cognitive Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410297085.8A CN118054888A (en) | 2024-03-15 | 2024-03-15 | HARQ-based state updating method in cognitive Internet of things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118054888A true CN118054888A (en) | 2024-05-17 |
Family
ID=91048139
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410297085.8A Pending CN118054888A (en) | 2024-03-15 | 2024-03-15 | HARQ-based state updating method in cognitive Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118054888A (en) |
-
2024
- 2024-03-15 CN CN202410297085.8A patent/CN118054888A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10524200B2 (en) | System and method for dynamically configurable air interfaces | |
| WO2018082505A1 (en) | System and method for reliable transmission in communications systems | |
| US8611417B2 (en) | Using decoding progress to adapt transmission rate in a multicast transmission | |
| US8199693B2 (en) | Method and apparatus for controlling power in a decode-and-forward relay system | |
| CN102208962B (en) | Wireless data transmission method | |
| EP4210411A1 (en) | Method for selecting modulation and coding scheme (mcs), and communication apparatus | |
| CN112512109B (en) | End-to-end power distribution method and device, full-duplex relay system and computer readable storage medium | |
| CN116847453B (en) | Satellite data transmission and satellite Internet of things access time management method | |
| Hung et al. | Low latency communication for Internet of Things | |
| KR100953567B1 (en) | Data reception acknowledge signal transmission/reception apparatus and method in mobile communication system | |
| Liu et al. | Cooperative beamforming aided incremental relaying in cognitive radios | |
| Tyrovolas et al. | Slotted ALOHA with code combining for IoT networks | |
| CN118054888A (en) | HARQ-based state updating method in cognitive Internet of things | |
| CN113115233B (en) | Opportunistic NOMA (non-access-point) cooperative multicast relay selection method | |
| CN109379226A (en) | A kind of short package transmission method of combination Predictive Control System | |
| CN110933708B (en) | Resource allocation method and device for relay-assisted intelligent factory communication | |
| Ganhão et al. | Performance of hybrid ARQ for network diversity multiple access schemes | |
| Butt et al. | Energy efficient scheduling for loss tolerant IoT applications with uninformed transmitter | |
| JP2023504350A (en) | Uplink data transmission method and uplink data transmission device | |
| Ganhão et al. | Performance of hybrid ARQ for NDMA access schemes with uniform average power control | |
| WO2024131831A1 (en) | Information transmission method, terminal, device and medium | |
| Butt et al. | Effective capacity of cognitive radio links: Accessing primary feedback erroneously | |
| KR101165544B1 (en) | Optimization method for wireless multicast service with cooperative harq, system using the same and recording medium for the same | |
| US20130051386A1 (en) | Methods and Apparatus for Multicast Transmission | |
| Wu et al. | DRL-Based Energy Efficient Power Adaptation for Fast HARQ in the Finite Blocklength Regime |
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 |