CN114172774B - Industrial Internet of things equipment power distribution method based on outdated gradient feedback - Google Patents
Industrial Internet of things equipment power distribution method based on outdated gradient feedback Download PDFInfo
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Abstract
The invention provides an industrial Internet of things equipment power distribution method based on outdated gradient feedback, which comprises the steps of S1, network parameter initialization, S2, capacity gradient calculation, S3, emission power calculation, S4 and repetition of S2 and S3. The power distribution method provided by the invention does not need instantaneous and distributed information of CSI, the capacity performance of the obtained network is far superior to that of an EPA method, and when the network operation time is long enough, the method can effectively ensure the long-term power constraint of equipment.
Description
Technical Field
The invention relates to the field of Internet of things, in particular to an industrial Internet of things equipment power distribution method based on outdated gradient feedback.
Background
In the time-varying large-scale uplink industrial internet of things, channel State Information (CSI) of an uplink transmitting end of internet of things equipment is usually obtained through processes such as estimation, quantization, feedback and the like performed by a receiving end. However, when the size of the internet of things becomes huge, CSI estimation will face a huge pilot resource overhead, resulting in CSI acquisition time far higher than the coherence time of the channel, i.e. CSI received by the transmitter is lagging/outdated. In addition, quantized CSI has quantization errors compared to its true value. In this case, the conventional power control algorithm cannot adapt to the time-varying channel.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide an industrial internet of things device power allocation method based on outdated gradient feedback, including
S1, initializing the maximum long-term power of Internet of things equipment nAnd maximum instantaneous power P n The internet of things device n maintains a virtual queue Q n (t) and setting its initial value Q n (0) =0, initialTransmit power vector p for each internet of things device n (0),p n (1),...,p n (T-1)∈P n Initializing weight parameters V, V>0，n∈[1,N]N represents the total number of the devices of the Internet of things; p is p n Representing feasible set of power distribution of internet of things equipment n, p n (T-1) represents the transmitting power of the Internet of things device n at the moment T-1;
s2, in a time slot T epsilon { T-1, T, T+1, … }, the base station measures the transmitting power p (T) of all the Internet of things equipment, wherein T represents the time size of the outdated CSI information h, and then calculates the capacity gradient at p=p (T) for the Internet of things equipment n by combining the CSI information h (T-T+1):
wherein,,
m∈[1,M]m represents the total number of sub-carriers into which the system band is divided, B m Representing the bandwidth of sub-carrier m, p refers to the transmit power variation of all internet of things devices,indicating channel power gain of the Internet of things device n on the mth subcarrier channel in a time slot T-T+1, < >>Representing the transmitting power of the time slot t on the mth subcarrier channel of the Internet of things device n, sigma 2 Representing gaussian white noise power; r (p (T), h (T-T+1)) represents a capacity function;
then the calculation resultFeedback to the internet of things device n;
s3, at the end time of each time slot T epsilon { T-1, T, T+1, … }, the Internet of thingsThe network device n determines the transmit power p of the time slot t+1 by n (t+1)：
For the unit column vector of M dimension, +.>p n (t) represents uplink transmission power vector of the internet of things device n in the time slot t, < + >>Representing the transmit power of the slot t internet of things device n on the mth subcarrier channel,
representing the transmission power of the time slot t on the mth subcarrier channel of the internet of things device n +.>The method comprises the steps that channel power gain of a time slot T-T+1 Internet of things device n on an mth subcarrier channel is represented;
the internet of things device n updates its own virtual queue by:
s4, repeating S2 and S3.
In the invention, a dynamic uplink power distribution method based on outdated gradient feedback is provided by means of the technical principle of online convex optimization. Specifically, the network traversal capacity is maximized as an optimization target, and the instantaneous power constraint and the long-term power constraint of the Internet of things equipment are taken as constraint conditions. The instantaneous power constraint of the Internet of things equipment is related to the hardware circuit design of the equipment, and the long-term power constraint is used for guaranteeing that the average power consumption of the Internet of things equipment does not exceed a certain threshold so as to improve the average survival time of the equipment. In order to guarantee long-term power constraint of the internet of things equipment, each internet of things equipment maintains a virtual queue locally. When a specific power distribution decision is made, the receiving end (base station) only needs to feed back outdated network capacity gradient information to the Internet of things equipment, and the Internet of things equipment autonomously makes the power distribution decision by adopting a dichotomy method in combination with local instantaneous queue backlog information.
The power distribution method provided by the invention does not need instantaneous and distributed information of CSI, the capacity performance of the obtained network is far superior to that of an EPA method, and when the network operation time is long enough, the method can effectively ensure the long-term power constraint of equipment.
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The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
Fig. 1 is a diagram of an exemplary embodiment of a network scenario of an industrial internet of things device power distribution method based on outdated gradient feedback according to the present invention.
Fig. 2 is a schematic diagram showing a comparison of average virtual queue length variation trend of the POPA method and the DPP method according to the present invention.
FIG. 3 is a graph showing the sliding capacity (and rate) trend of the POPA method and DPP and EPA methods.
FIG. 4 is a graph showing the sliding transmission power variation trend of the POPA method and the DPP and EPA method according to the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
As electromagnetic spectrum becomes increasingly scarce, in future large-scale internet of things, non-orthogonal multiple access technologies will be widely used, taking a single cell scenario as an example, as shown in fig. 1, consider one information aggregation node, called a base station, and N internet of things devices, in fig. 1, ioTD represents an internet of things device, BS represents a base station, SC represents a subcarrier,
the system band is partitioned into M subcarriers, denoted as M e {1,2,..m }.
The internet of things device is denoted N, N e {1, 2..n }.
Assuming that the network is a discrete-time system, the entire time axis is divided into time slots of equal time length, denoted t e {0,1,2, … }.
The channel between the Internet of things equipment and the base station is assumed to be a flat block fading channel, namely the coherence time of the channel is longer than the length of a time slot, and the channel can only jump in an independent and same-distribution mode in different time slots.
Order theThe channel power gain/CSI on all subcarriers of the Internet of things device n is obtained.
Let h (t) = [ h ] 1 (t) T ,h 2 (t) T ,…,h N (t) T ] T Channel power gain/CSI for all devices for time slot t.
In the uplink non-orthogonal multiple access network, the base station adopts a serial interference cancellation technology to decode signals of the internet of things equipment, and the decoding process is usually in descending order of received signal power. This means that in the base station decoding process, the internet of things device signal with smaller receiving power will interfere with the internet of things device signal with larger receiving power.
Vector for uplink transmitting power of each Internet of things device nRepresenting the transmission power of all the internet of things devices in the time slot t as p (t) = [ p ] 1 (t) T ,p 2 (t) T ,...,p N (t) T ] T 。
On each carrier m, all the internet of things devices are ordered in ascending order of received signal power, i.eWhere i, j e N. Accordingly, the signal-to-interference-and-noise ratio of the internet of things device n' on the subcarrier m can be written as
Wherein sigma 2 Is the power of additive white gaussian noise. According to shannon's formula, the instantaneous capacity of the network can be expressed as
Wherein B is m For the bandwidth of subcarrier m, equation (a) is based on the fact thatThe result is that equation (b) is to change the subscript n' to n. It is readily found that R (p (t), h (t)) is a dimpled function.
The network capacity optimization problem is set as
Wherein,,for the unit column vector of M dimension, +.>Maximum long-term average power, P, of an Internet of things device n n For a static feasible set of the Internet of things equipment n, namely
R M For M dimension real space, P n The maximum instantaneous power of the internet of things device n. In general, there are
As shown in an embodiment of FIG. 1, the invention provides an industrial Internet of things device power distribution method based on outdated gradient feedback, which comprises the following steps of
S1, initializing the maximum long-term power of Internet of things equipment nAnd maximum instantaneous power P n The internet of things device n maintains a virtual queue Q n (t) and setting its initial value Q n (0) =0, initializing the transmit power vector p of each internet of things device n (0),p n (1),...,p n (T-1)∈P n Initializing weight parameters V, V>0，n∈[1,N]N represents the total number of the devices of the Internet of things; p is p n Representing feasible set of power distribution of internet of things equipment n, p n (T-1) represents the transmitting power of the Internet of things device n at the moment T-1;
s2, in a time slot T epsilon { T-1, T, T+1, … }, the base station measures the transmitting power p (T) of all the Internet of things equipment, wherein T represents the time size of the outdated CSI information h, and then calculates the capacity gradient at p=p (T) for the Internet of things equipment n by combining the CSI information h (T-T+1):
wherein,,
m∈[1,M]m represents the total number of sub-carriers into which the system band is divided, B m Representing the bandwidth of sub-carrier m, p refers to the transmit power variation of all internet of things devices,indicating channel power gain of the Internet of things device n on the mth subcarrier channel in a time slot T-T+1, < >>Representing the transmitting power of the time slot t on the mth subcarrier channel of the Internet of things device n, sigma 2 Representing gaussian white noise power; r (p (T), h (T-T+1)) represents a capacity function;
then the calculation resultFeedback to the internet of things device n;
p refers to the transmission power variable of all the devices of the Internet of things, which is the optimized variable of the capacity function R (p, h (T-T+1)), and the step is to find the gradient of R (p, h (T-T+1)) at p=p (T)
Where T denotes the time size when the network information h is outdated, i.e. for the T-th time slot, the network can only take the information at the time T-t+1, h (T-t+1), and the lower case T denotes the T-th time slot.
The set { T-1, T, T+1, … } here represents the set of slots in which the step S2 is performed, that is, the step S2 is performed from the time t=T-1;
s3, at the end time of each time slot T epsilon { T-1, T, T+1, … }, the internet of things device n determines the transmission power p of the time slot t+1 by the following method n (t+1)：
For the unit column vector of M dimension, +.>p n (t) represents uplink transmission power vector of the internet of things device n in the time slot t, < + >>Representing the transmit power of the slot t internet of things device n on the mth subcarrier channel,
representing the transmission power of the time slot t on the mth subcarrier channel of the internet of things device n +.>The method comprises the steps that channel power gain of a time slot T-T+1 Internet of things device n on an mth subcarrier channel is represented;
the internet of things device n updates its own virtual queue by:
s4, repeating S2 and S3.
In the invention, a dynamic uplink power distribution method based on outdated gradient feedback is provided by means of the technical principle of online convex optimization. Specifically, the network traversal capacity is maximized as an optimization target, and the instantaneous power constraint and the long-term power constraint of the Internet of things equipment are taken as constraint conditions. The instantaneous power constraint of the Internet of things equipment is related to the hardware circuit design of the equipment, and the long-term power constraint is used for guaranteeing that the average power consumption of the Internet of things equipment does not exceed a certain threshold so as to improve the average survival time of the equipment. In order to guarantee long-term power constraint of the internet of things equipment, each internet of things equipment maintains a virtual queue locally. When a specific power distribution decision is made, the receiving end (base station) only needs to feed back outdated network capacity gradient information to the Internet of things equipment, and the Internet of things equipment autonomously makes the power distribution decision by adopting a dichotomy method in combination with local instantaneous queue backlog information.
When the device transmitting end knows the instant information of the CSI but does not know the distribution information of the instant information, an effective method is a Drift Plus Penalty (DPP) (Lyapunov optimization) method, and the algorithm flow of the method is a dynamic method similar to online secondary gradient descent.
When the device transmitting end does not know the instant information and the distribution information of the CSI, the most direct way is to use the equal (fixed) power allocation (EPA) method, that is, to evenly allocate the available power on each occupied channel bandwidth (subcarrier).
Although the DPP method can cope with dynamic fading of CSI, it requires instantaneous state information of CSI, which is difficult to do in industrial internet of things scenarios with huge network specifications. In general, the DPP method may be used as a comparison (reference) algorithm independent of the CSI instant message algorithm.
The EPA method is a static power distribution method, the statistical rule of the CSI is not fully utilized, and the algorithm performance is low.
The power distribution method provided by the invention does not need instantaneous and distributed information of CSI, the capacity performance of the obtained network is far superior to that of an EPA method, and when the network operation time is long enough, the method can effectively ensure the long-term power constraint of equipment.
To illustrate the difference between the proposed method and the drift penalty algorithm, the following provides a drift penalty (DPP) algorithm flow:
step one: initializing maximum long-term power of each Internet of things deviceAnd maximum instantaneous power P n The base station maintains a virtual queue Q for each device n (t) and setting its initial value Q n (0) =0, initializing the transmit power vector p for each user n (0)∈P n Initializing a weight parameter V>0。
Step two: at the beginning of each time slot t e {0,1,2, … }, the base station measures the CSI information h (t), and then determines the transmit power p (t) for all internet of things devices by solving the following problem
Then p is n And (t) feeding back the information to each Internet of things device n. The base station updates the virtual queue length for each internet of things device n by:
step three: in each time slot t epsilon {0,1,2, … }, each internet of things device transmits data according to the power scheduled by the base station.
Step four: repeating the second and third steps.
As can be seen from the algorithm flow of DPP, the transmit power allocation p (t) is strictly dependent on CSI information h (t), and requires unified scheduling by the base station. When the network is large in size, the base station needs to solve a complex optimization problem to determine the power allocation variable, which consumes intolerable time. From the measurement of the CSI information h (t), the power allocation scheduling message is received by the internet of things device, and the whole time length may exceed the length of each system time slot, so that the effective transmission time is reduced.
The equal power distribution method is a static power distribution method, and each internet of things device sets the power on each subcarrier as
Compared with the DPP method, the POPA method provided by the invention has the following main advantages:
the POPA method does not need real-time CSI information of a network, only depends on outdated capacity gradient information fed back by a base station, and compared with the heavy calculated amount of a DPP algorithm, the POPA method only needs to solve a quadratic programming problem, namely a transmitting power calculation formula, by each piece of equipment of the Internet of things, and when the problem is solved by adopting a dichotomy, the time complexity is about O (M log (M)), so that the complexity of the method is lower.
The POPA method is a non-centralized dynamic power distribution method, the decision-making calculation of the transmitting power of each Internet of things device is completed by the POPA method, the base station is only responsible for the calculation of the network capacity gradient information, the decision-making mode of dispersing the calculated amount in each network node is more beneficial to the expansion of a network, in contrast, the DPP method needs the base station to centrally determine the power distribution for all users in each time slot, and when the network scale is gradually enlarged, the calculated amount of the base station side is too heavy, and the paralysis of the network is easily caused.
3. As shown in fig. 2, the average virtual queue length of the POPA method of the present invention is shorter, which is favorable for avoiding network congestion, as shown in fig. 3, the sliding capacity (and rate) of the POPA method of the present invention is smaller, as shown in fig. 4, the sliding transmitting power of the POPA method of the present invention is lower, which is favorable for saving energy.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (1)
1. An industrial internet of things device power distribution method based on outdated gradient feedback is characterized by comprising the following steps:
s1, initializing the maximum long-term power of Internet of things equipment nAnd maximum instantaneous power P n The internet of things device n maintains a virtual queue Q n (t) and setting its initial value Q n (0) =0, initializing the transmit power vector p of each internet of things device n (0),p n (1),…,p n (T-1)∈P n Initializing weight parameters V, V>0，n∈[1,N]N represents the total number of the devices of the Internet of things; p is p n Representing feasible set of power distribution of internet of things equipment n, p n (T-1) represents the transmitting power of the Internet of things device n at the moment T-1;
s2, in a time slot T epsilon { T-1, T, T+1, … }, the base station measures the transmitting power p (T) of all the Internet of things equipment, wherein T represents the time size when the CSI information h is outdated, and then calculates the capacity gradient at p=p (T) for the Internet of things equipment n by combining the CSI information h (T-T+1):
wherein,,
m∈[1,M]m represents the total number of sub-carriers into which the system band is divided, B m Representing the bandwidth of sub-carrier m, p refers to the transmit power variation of all internet of things devices,indicating channel power gain of the Internet of things device n on the mth subcarrier channel in time slot T-T+1, < >>Representing the transmitting power of the time slot t on the mth subcarrier channel of the Internet of things device n, sigma 2 Representing gaussian white noise power; r (p (T), h (T-T+1)) represents a capacity function;
then the calculation resultFeedback to the internet of things device n;
s3, at the end time of each time slot T epsilon { T-1, T, T+1, … }, the Internet of things device n determines the transmitting power p of the time slot t+1 by the following method n (t+1)：
For the unit column vector of M dimension, +.>p n (t) represents uplink transmission power vector of the internet of things device n in the time slot t, < + >>Representing the transmit power of the slot t internet of things device n on the mth subcarrier channel,
representing the transmission power of the time slot t on the mth subcarrier channel of the internet of things device n +.>The method comprises the steps that channel power gain of a time slot T-T+1 Internet of things device n on an mth subcarrier channel is represented;
the internet of things device n updates its own virtual queue by:
s4, repeating S2 and S3.
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| GB0902742D0 (en) * | 2009-02-18 | 2009-04-01 | Toshiba Res Europ Ltd | Wireless communications methods and apparatus |
| CN101478814A (en) * | 2009-01-08 | 2009-07-08 | 上海交通大学 | Combined pre-coding and power distribution method in multicast network based on network coding |
| CN110247691A (en) * | 2019-06-14 | 2019-09-17 | 中国矿业大学 | A kind of safe transmission method for downlink NOMA visible light communication network |
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| CN101478814A (en) * | 2009-01-08 | 2009-07-08 | 上海交通大学 | Combined pre-coding and power distribution method in multicast network based on network coding |
| GB0902742D0 (en) * | 2009-02-18 | 2009-04-01 | Toshiba Res Europ Ltd | Wireless communications methods and apparatus |
| CN110247691A (en) * | 2019-06-14 | 2019-09-17 | 中国矿业大学 | A kind of safe transmission method for downlink NOMA visible light communication network |
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