A method for jointly adjusting the range extension bias and transmission power of fog access points

Abstract

The present invention discloses a method for jointly adjusting the range expansion bias and transmission power of fog access points, and the steps are as follows: constructing and initializing the fog access network; defining the system energy utility function; the user selects the service cell according to the maximum biased receiving power; calculating the initial system utility; adjusting the range expansion bias value of the fog access point by simulated annealing; dynamically adjusting the transmission power of the fog access point by simulated annealing; setting the bias value and transmission power of each fog access point to obtain the system energy efficiency value. The present invention can optimize the range expansion bias value and transmission power of the fog access point with lower complexity, so as to achieve the purpose of improving the system energy efficiency; the present invention can obtain the optimal solution of the system energy utility function by adjusting the range expansion bias and transmission power of each fog access point.

Description

Technical Field

[0001] The invention relates to wireless access technology in mobile communication, in particular to a method for jointly adjusting a fog access point range expansion bias and a transmission power. Background Art

[0002] As the most advanced mobile communication technology, 5G broadband wireless communication technology has been widely recognized for its high speed, low latency, high reliability and low power consumption. Under the increasing pressure of mobile network service demand, mobile operators first built new base stations to meet user needs in a cell splitting manner. However, this deployment method based on cell splitting has brought huge network deployment cost pressure to operators. In this case, the cloud radio access network (C-RAN) architecture and the heterogeneous cloud radio access network (H-CRAN) architecture have been introduced. C-RAN combines the advantages of cloud computing and wireless access network, and has the characteristics of centralization, cloudification and collaboration. It not only reduces the cost of network operation and deployment, but also realizes information sharing. However, due to the limitation of fronthaul link capacity, there is still the problem of limited network capacity. H-CRAN combines the advantages of heterogeneous network and C-RAN, establishes a connection between the existing high power node (HPN) and BBU pool, and realizes the functional separation of control and service planes, but there is also the problem of limited fronthaul link. In order to alleviate the fronthaul link overhead, fog computing, HPN and C-RAN are combined to propose a fog radio access network (F-RAN) architecture, which uses the special computing and storage functions of users and edge network devices to alleviate the pressure on the fronthaul link as well as the cloud computing and service pressure. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a method for jointly adjusting the range extension bias and transmit power of a fog access point, so as to optimize the range extension bias value and transmit power of the fog access point with lower complexity and improve the energy efficiency of the system.

[0004] Technical solution: A method for jointly adjusting the range extension bias and transmission power of a fog access point according to the present invention comprises the following steps:

[0005] (1) Initialize the network, set up the macro cells, fog access points and users in the network, initialize the range extension bias, transmit power and iteration number, and initialize the user's serving cell;

[0006] (2) Define the system energy utility function;

[0007] (3) The user selects the serving cell based on the maximum offset received power;

[0008] (4) Calculate the user's achievable rate and then calculate the system utility;

[0009] (5) Fix the transmission power set of all fog access points and use simulated annealing to update the bias value of fog access point l;

[0010] (6) Fix the bias value set of all fog access points and use simulated annealing to update the transmit power of fog access point l;

[0011] (7) Change the target fog access point and repeat steps (5) to (6). Use simulated annealing to update the bias value and transmit power of the target fog access point respectively until the stop condition is met. Then stop the iteration, set the bias value and transmit power of each fog access point, and use the system energy utility function formula to obtain the final system energy efficiency value.

[0012] The step (1) is specifically:

[0013] (1.1) Assume that there are M macro cells, L fog access points and K users in the system. The macro cell set is denoted as The fog access point set is denoted as Macro cells and fog access points are collectively referred to as cells, and the set of all cells is denoted as Where ∪ represents the union of two sets, and the user set is recorded as

[0014] (1.2) Only fog access points can change the user service cell by adjusting their range extension bias, and then jointly adjust the transmission power to optimize the network utility. The set of available range extension biases for where b min is the minimum bias, b max is the maximum offset, Δ is the offset interval, which is defined by the operator; the range extension offset of any macro cell m

[0015] (1.3) To ensure coverage, each macro cell m transmits at a maximum power of P max Send downlink signals, and the fog access point can dynamically change and adjust its transmission power to improve network performance;

[0016] (1.4) Definition is the power interval, where N p is the number of power levels, then the available transmission power set of any fog access point is P L ={0,1·Δ p ,2·Δ p ,....,P l max}, let the range extension bias of fog access point l be b l , the transmission power is P l , the range extension bias set of all fog access points is b, and the transmission power set of all fog access points is P;

[0017] (1.5) During initialization, the range extension bias of any fog access point l Transmit power Where P l max is the maximum transmission power of fog access point l;

[0018] (1.6) User k receives the signal from the cell The received signal power is recorded as During initialization, each user selects the cell with the largest received signal power as its initial serving cell. The serving cell of user k is denoted as S k ;

[0019] (1.7) Define the number of external iterations t out , number of bias value iterations t b , power value iteration times t p , at initialization, t out =t b =t p =0.

[0020] The step (2) is specifically as follows: first, define the system throughput T, that is, T is the achievable rate R of all users k k of and, The utility function of the system is in, is the total power loss of the fog access point.

[0021] The step (3) is specifically as follows: c represents the transmission power of cell c, G c,k represents the average channel gain between cell c and user k, then the received signal power User k selects serving cell S according to the maximum offset received power criterion k ,so That is, user k selects the received signal power plus the bias value in all cells The largest cell is used as its serving cell; at initialization, the range extension bias of any fog access point l Therefore, during initialization, each user selects the cell with the largest received signal power as its initial serving cell.

[0022] The step (4) is specifically as follows: during the optimization process, the serving cell of the user changes with the change of the transmission power. When the serving cell of user k is S k When , the received signal from the serving cell and the interference plus noise ratio are:

[0023]

[0024] in, Indicates the serving cell S k The average channel gain between user k and N0 is the noise power value. According to Shannon's formula, the achievable rate of user k is:

[0025] R k =W k log(1+γ k )

[0026] Among them, W k is the bandwidth of user k; the system utility is defined as the system energy utility, and its function can be specifically written as:

[0027]

[0028] Among them, T is the system throughput, and the energy utility H depends on the user's serving cell and the transmit power of each cell. Therefore, H is a function of the fog access point range extension bias value set and the fog access point transmit power set, which can be written as H(b,P).

[0029] The step (5) is specifically as follows:

[0030] (5.1) First, set the maximum bias value iteration number Select the current target fog access point l and set the initial temperature parameter of the bias value for bias value adjustment Temperature displacement coefficient Temperature drop coefficient According to the initial temperature parameter of the offset value and the temperature drop coefficient, the current offset value is calculated to adjust the potential temperature.

[0031]

[0032] (5.2) Pass Determine the average displacement distance of the bias value at the current potential temperature

[0033]

[0034] Use t b Represents the number of iterations corresponding to this bias value iteration, that is, the number of iterations of the current bias value. For the first iteration, t b = 0, the potential offset displacement value of fog access point l in this iteration for:

[0035]

[0036] Among them, randn is a randomly generated random number in the interval [-1,1] centered on 0 that satisfies the normal distribution. is the average displacement distance with the offset value A random bias displacement value that satisfies the normal distribution with centered at , [] indicates rounding;

[0037] (5.3) The potential bias value obtained by cell l in this iteration is:

[0038]

[0039] Among them, b l (t b -) is the previous bias value iteration of fog access point l (i.e., the t b -1 iterations) to obtain the bias value, t b = 0 when b l (t b -)=0, increase With reduction The probability of the symbol [x] is 50%. →B Indicates mapping the value of x to a bias value closest to x in the available range extension bias set B;

[0040] (5.4) Let t be b The set of potential bias values ​​of all fog access points in this iteration is in is the set of bias values ​​of all fog access points except the fog access point l in the previous iteration, and the set of bias values ​​of all fog access points in the previous iteration is recorded as b(t b -), then the bias value update principle of fog access point l in this iteration is:

[0041]

[0042] That is, only the potential bias value set b obtained in this iteration temp (t b ) corresponds to the system energy efficiency value H(b temp (t b )) is greater than the system energy efficiency value H(b(t b -)), the bias value b of the fog access point in this iteration l (t b )according to set up;

[0043] (5.5) Update t b =t b +1, Update and Start the next iteration and get a new and b l (t b ), update the bias value of fog access point l according to the bias value update principle;

[0044] (5.6) Continue iterating until the number of iterations or b l (t b ) remains unchanged for a number of times greater than the preset bias iteration threshold ξ b Stop updating the bias value of fog access point l, and record the bias value set of all fog access points at this time as b.

[0045] The step (6) is specifically as follows:

[0046] (6.1) First, set the maximum power value iteration number Set initial temperature parameters for power adjustment Temperature displacement coefficient Temperature drop coefficient The current power adjustment potential temperature is calculated based on the initial temperature parameters and temperature drop coefficient used for power adjustment

[0047]

[0048] (6.2) Pass Determine the average displacement distance of the power value at the current potential temperature

[0049]

[0050] Use t p Represents the number of iterations corresponding to this power iteration (i.e. the current number of power iterations, the first iteration t p =0), the potential power displacement value of fog access point l in this iteration for:

[0051]

[0052] The average displacement distance is the power value A random power displacement value that satisfies the normal distribution with the center as

[0053] (6.3) The potential power value obtained by fog access point l in this iteration is:

[0054]

[0055] Among them, P l (t p -) is the previous power iteration of fog access point l (i.e., the t p -1 iteration) to obtain the power value, t p =0 when P l (t p -)=P lmax ,Increase With reduction The probability of the symbol is 50%. Represents mapping the value of x to the available transmit power set P L The power value closest to the x value;

[0056] (6.4) p The potential power value set of all fog access points in the iteration is in is the set of power values ​​of all fog access points except fog access point l in the previous iteration, and the set of power values ​​of all fog access points in the previous iteration is recorded as P(t p -), then the power value update principle of fog access point l in this iteration is:

[0057]

[0058] That is, the potential power value set P obtained only in this iteration temp (t p ) corresponds to the system energy efficiency value H(P temp (t p )) is greater than the system energy efficiency value H(P(t p -)),the power value P of fog access point l in this iteration l (t p )According to P l temp (t p )set up;

[0059] (6.5) Update t p =t p +1, Update and Start the next iteration and get the new P l temp (t p ) and P l (t p ), update the power value of fog access point l according to the power value update principle;

[0060] (6.6) Repeat until the number of iterations is or P l (t p ) remains unchanged for a number of times greater than the preset power iteration threshold ξ p Stop updating the power value of fog access point l, and record the power value set of all fog access points at this time as P.

[0061] The step (7) is specifically as follows: out =tout +1 updates the external iteration number, changes the target fog access point, and repeats steps (5) to (6), that is, using simulated annealing to update the bias value and transmission power of the target fog access point respectively, until the external iteration number t out Greater than or equal to the preset external iteration threshold Or when the number of times that the bias value and power value set b and P of the fog access point remain unchanged during the iteration process is greater than the threshold η, the external iteration is stopped, and the bias value and transmission power of each fog access point are set according to the final fog access point bias value set b and power value set P, and the final system energy efficiency value H is obtained using the system energy utility function formula.

[0062] A computer storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for jointly adjusting the range extension bias and transmission power of a fog access point.

[0063] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for jointly adjusting the range extension bias and transmission power of a fog access point when executing the computer program.

[0064] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0065] 1. The present invention can optimize the range extension bias value and transmission power of fog access points with low complexity, thereby achieving the purpose of improving system energy efficiency;

[0066] 2. The present invention can obtain the optimal solution of the system energy utility function by adjusting the range expansion bias and transmission power of each fog access point. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0068] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0069] like Figure 1 As shown, a method for jointly adjusting the range extension bias and transmission power of a fog access point comprises the following steps:

[0070] (1) Initialize the network, set up the macro cells, fog access points and users in the network, initialize the range extension bias, transmit power and iteration number, and initialize the user's serving cell;

[0071] (2) Define the system energy utility function;

[0072] (3) The user selects the serving cell based on the maximum offset received power;

[0073] (4) Calculate the user's achievable rate and then calculate the system utility;

[0074] (5) Fix the transmission power set of all fog access points and use simulated annealing to update the bias value of fog access point l;

[0075] (6) Fix the bias value set of all fog access points and use simulated annealing to update the transmit power of fog access point l;

[0076] (7) Change the target fog access point and repeat steps (5) to (6). Use simulated annealing to update the bias value and transmit power of the target fog access point respectively until the stop condition is met. Then stop the iteration, set the bias value and transmit power of each fog access point, and use the system energy utility function formula to obtain the final system energy efficiency value.

[0077] The step (1) is specifically:

[0078] (1.1) Assume that there are M macro cells, L fog access points and K users in the system. The macro cell set is denoted as The fog access point set is denoted as Macro cells and fog access points are collectively referred to as cells, and the set of all cells is denoted as Where ∪ represents the union of two sets, and the user set is recorded as

[0079] (1.2) Only fog access points can change the user service cell by adjusting their range extension bias, and then jointly adjust the transmission power to optimize the network utility. The set of available range extension biases for where b min is the minimum bias, b max is the maximum offset, Δ is the offset interval, which is defined by the operator; the range extension offset of any macro cell m

[0080] (1.3) To ensure coverage, each macro cell m transmits at a maximum power of P max Send downlink signals, and the fog access point can dynamically change and adjust its transmission power to improve network performance;

[0081] (1.4)Define Δ p =P l max / (N p -1) is the power interval, where N p is the number of power levels, then the available transmission power set of any fog access point is P L ={0,1·Δ p ,2·Δ p,....,P l max}, let the range extension bias of fog access point l be b l , the transmission power is P l , the range extension bias set of all fog access points is b, and the transmission power set of all fog access points is P;

[0082] (1.5) During initialization, the range extension bias of any fog access point l Transmit power Where P l max is the maximum transmission power of fog access point l;

[0083] (1.6) User k receives the signal from the cell The received signal power is recorded as During initialization, each user selects the cell with the largest received signal power as its initial serving cell. The serving cell of user k is denoted as S k ;

[0084] (1.7) Define the number of external iterations t out , number of bias value iterations t b , power value iteration times t p , at initialization, t out =t b =t p =0.

[0085] The step (2) is specifically as follows: first, define the system throughput T, that is, T is the achievable rate R of all users k k of and, The utility function of the system is in, is the total power loss of the fog access point.

[0086] The step (3) is specifically as follows: c represents the transmission power of cell c, G c,k represents the average channel gain between cell c and user k, then the received signal power User k selects serving cell S according to the maximum offset received power criterion k ,so That is, user k selects the received signal power plus the bias value in all cells The largest cell is used as its serving cell; at initialization, the range extension bias of any fog access point l Therefore, during initialization, each user selects the cell with the largest received signal power as its initial serving cell.

[0087] The step (4) is specifically as follows: during the optimization process, the serving cell of the user changes with the change of the transmission power. When the serving cell of user k is S k When , the received signal from the serving cell and the interference plus noise ratio are:

[0088]

[0089] in, Indicates the serving cell S k The average channel gain between user k and N0 is the noise power value. According to Shannon's formula, the achievable rate of user k is:

[0090] R k =W k log(1+γ k )

[0091] Among them, W k is the bandwidth of user k; the system utility is defined as the system energy utility, and its function can be specifically written as:

[0092]

[0093] Among them, T is the system throughput, and the energy utility H depends on the user's serving cell and the transmit power of each cell. Therefore, H is a function of the fog access point range extension bias value and the fog access point transmit power, which can be written as H(b,P).

[0094] The step (5) is specifically as follows:

[0095] (5.1) First, set the maximum bias value iteration number Select the current target fog access point l and set the initial temperature parameter of the bias value for bias value adjustment Temperature displacement coefficient Temperature drop coefficient According to the initial temperature parameter of the offset value and the temperature drop coefficient, the current offset value is calculated to adjust the potential temperature.

[0096]

[0097] (5.2) Pass Determine the average displacement distance of the bias value at the current potential temperature

[0098]

[0099] Use t b Represents the number of iterations corresponding to this bias value iteration, that is, the number of iterations of the current bias value. For the first iteration, t b= 0, the potential offset displacement value of fog access point l in this iteration for:

[0100]

[0101] Among them, randn is a randomly generated random number in the interval [-1,1] centered on 0 that satisfies the normal distribution. is the average displacement distance with the offset value A random bias displacement value that satisfies the normal distribution with centered at , [] indicates rounding;

[0102] (5.3) The potential bias value obtained by cell l in this iteration is:

[0103]

[0104] Among them, b l (t b -) is the previous bias value iteration of fog access point l (i.e., the t b -1 iterations) to obtain the bias value, t b = 0 when b l (t b -)=0, increase With reduction The probability of the symbol [x] is 50%. →B Indicates mapping the value of x to a bias value closest to x in the available range extension bias set B;

[0105] (5.4) Let t be b The set of potential bias values ​​of all fog access points in this iteration is in is the set of bias values ​​of all fog access points except the fog access point l in the previous iteration, and the set of bias values ​​of all fog access points in the previous iteration is recorded as b(t b -), then the bias value update principle of fog access point l in this iteration is:

[0106]

[0107] That is, only the potential bias value set b obtained in this iteration temp (t b ) corresponds to the system energy efficiency value H(b temp (t b )) is greater than the system energy efficiency value H(b(t b -)), the bias value b of the fog access point in this iteration l (t b )according to set up;

[0108] (5.5) Update t b =t b +1, Update and Start the next iteration and get a new and b l (t b ), update the bias value of fog access point l according to the bias value update principle;

[0109] (5.6) Continue iterating until the number of iterations or b l (t b ) remains unchanged for a number of times greater than the preset bias iteration threshold ξ b Stop updating the bias value of fog access point l, and record the bias value set of all fog access points at this time as b.

[0110] The step (6) is specifically as follows:

[0111] (6.1) First, set the maximum power value iteration number Set initial temperature parameters for power adjustment Temperature displacement coefficient Temperature drop coefficient The current power adjustment potential temperature is calculated based on the initial temperature parameters and temperature drop coefficient used for power adjustment

[0112]

[0113] (6.2) Pass Determine the average displacement distance of the power value at the current potential temperature

[0114]

[0115] Use t p Represents the number of iterations corresponding to this power iteration (i.e. the current number of power iterations, the first iteration t p =0), the potential power displacement value of fog access point l in this iteration for:

[0116]

[0117] The average displacement distance is the power value A random power displacement value that satisfies the normal distribution with the center as

[0118] (6.3) The potential power value obtained by fog access point l in this iteration is:

[0119]

[0120] Among them, P l (t p -) is the previous power iteration of fog access point l (i.e., the t p -1 iteration) to obtain the power value, t p =0 when P l (t p -)=P l max ,Increase With reduction The probability of the symbol is 50%. Represents mapping the value of x to the available transmit power set P L The power value closest to the x value;

[0121] (6.4) p The potential power value set of all fog access points in the iteration is in is the set of power values ​​of all fog access points except fog access point l in the previous iteration, and the set of power values ​​of all fog access points in the previous iteration is recorded as P(t p -), then the power value update principle of fog access point l in this iteration is:

[0122]

[0123] That is, the potential power value set P obtained only in this iteration temp (t p ) corresponds to the system energy efficiency value H(P temp (t p )) is greater than the system energy efficiency value H(P(t p -)),the power value P of fog access point l in this iteration l (t p )According to P l temp (t p )set up;

[0124] (6.5) Update t p =t p +1, Update and Start the next iteration and get a new and P l (t p ), update the power value of fog access point l according to the power value update principle;

[0125] (6.6) Repeat until the number of iterations is or Pl (t p ) remains unchanged for a number of times greater than the preset power iteration threshold ξ p Stop updating the power value of fog access point l, and record the power value set of all fog access points at this time as P.

[0126] The step (7) is specifically as follows: out =t out +1 updates the external iteration number, changes the target fog access point, and repeats steps (5) to (6), that is, using simulated annealing to update the bias value and transmission power of the target fog access point respectively, until the external iteration number t out Greater than or equal to the preset external iteration threshold Or when the number of times that the bias value and power value set b and P of the fog access point remain unchanged during the iteration process is greater than the threshold η, the external iteration is stopped, and the bias value and transmission power of each fog access point are set according to the final fog access point bias value set b and power value set P, and the final system energy efficiency value H is obtained using the system energy utility function formula.

[0127] A computer storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for jointly adjusting the range extension bias and transmission power of a fog access point.

[0128] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for jointly adjusting the range extension bias and transmission power of a fog access point when executing the computer program.

Claims

1. A method for jointly adjusting the range extension bias and transmission power of a fog access point, characterized in that: The following steps are involved: (1) Initialize the network, set up the macro cells, fog access points and users in the network, initialize the range extension bias, transmit power and number of iterations, and initialize the user's serving cell; specifically: (1.1) Assume that there are M macro cells, L fog access points and K users in the system. The macro cell set is denoted as The fog access point set is denoted as Macro cells and fog access points are collectively referred to as cells, and the set of all cells is denoted as Where ∪ represents the union of two sets, and the user set is recorded as (1.2) Only fog access points can change the user service cell by adjusting their range extension bias, and then jointly adjust the transmission power to optimize the network utility. The set of available range extension biases for where b min is the minimum bias, b max is the maximum offset, Δ is the offset interval, which is defined by the operator; the range extension offset of any macro cell m (1.3) To ensure coverage, each macro cell m transmits at a maximum power of P max Send downlink signals, and the fog access point can dynamically change and adjust its transmission power to improve network performance; (1.4)Define Δ p =P l max / (N p -1) is the power interval, where N p is the number of power levels, then the available transmission power set of any fog access point is P L ={0,1·Δ p ,2·Δ p ,....,P l max }, let the range extension bias of fog access point l be b l , the transmission power is P l , the range extension bias set of all fog access points is b, and the transmission power set of all fog access points is P; (1.5) During initialization, the range extension bias of any fog access point l Transmit power Where P l max is the maximum transmission power of fog access point l; (1.6) User k receives the signal from the cell The received signal power is recorded as During initialization, each user selects the cell with the largest received signal power as its initial serving cell. The serving cell of user k is denoted as S k ; (1.7) Define the number of external iterations t out , number of bias value iterations t b , power value iteration times t p , at initialization, t out =t b =t p =0; (2) Define the system energy utility function; specifically: First, define the system throughput T, where T is the achievable rate R of all users k. k of and, The utility function of the system is in, is the total power loss of the fog access point; (3) The user selects the serving cell based on the maximum offset received power; specifically: c represents the transmission power of cell c, G k,c represents the average channel gain between cell c and user k, then the received signal power User k selects serving cell S according to the maximum offset received power criterion k ,so That is, user k selects the received signal power plus the bias value in all cells The largest cell is used as its serving cell; at initialization, the range extension bias of any fog access point l Therefore, during initialization, each user selects the cell with the largest received signal power as its initial serving cell; (4) Calculate the user's achievable rate and then calculate the system utility; Specifically: During the optimization process, the user's serving cell changes with the change of the transmission power. When the serving cell of user k is S k When , the received signal from the serving cell and the interference plus noise ratio are: in, Indicates the serving cell S k The average channel gain between user k and N0 is the noise power value. According to Shannon's formula, the achievable rate of user k is: R k =W k log(1+γ k ) Among them, W k is the bandwidth of user k; the system utility is defined as the system energy utility, and its function can be specifically written as: Where T is the system throughput, and the energy utility H depends on the user's serving cell and the transmit power of each cell. Therefore, H is a function of the fog access point range extension bias value set and the fog access point transmit power set, which can be written as H(b,P); (5) The transmit power set of all fog access points is fixed, and the bias value of fog access point l is updated using simulated annealing; specifically: (5.1) First, set the maximum bias value iteration number Select the current target fog access point l and set the initial temperature parameter of the bias value for bias value adjustment Temperature displacement coefficient Temperature drop coefficient According to the initial temperature parameter of the offset value and the temperature drop coefficient, the current offset value is calculated to adjust the potential temperature. (5.2) Pass Determine the average displacement distance of the bias value at the current potential temperature Use t b Represents the number of iterations corresponding to this bias value iteration, that is, the number of iterations of the current bias value. For the first iteration, t b = 0, the potential offset displacement value of fog access point l in this iteration for: Among them, randn is a randomly generated random number in the interval [-1,1] centered on 0 that satisfies the normal distribution. is the average displacement distance with the offset value A random bias displacement value that satisfies the normal distribution with centered at , [] indicates rounding; (5.3) The potential bias value obtained by cell l in this iteration is: Among them, b l (t b -) is the previous bias value iteration of fog access point l (i.e., the t b -1 iterations) to obtain the bias value, t b = 0 when b l (t b -)=0, increase With reduction The probability of the symbol [x] is 50%. →B Indicates mapping the value of x to a bias value closest to x in the available range extension bias set B; (5.4) Let t be b The set of potential bias values ​​of all fog access points in this iteration is in is the set of bias values ​​of all fog access points except the fog access point l in the previous iteration, and the set of bias values ​​of all fog access points in the previous iteration is recorded as b(t b -), then the bias value update principle of fog access point l in this iteration is: That is, only the potential bias value set b obtained in this iteration temp (t b ) corresponds to the system energy efficiency value H(b temp (t b )) is greater than the system energy efficiency value H(b(t b -)), the bias value b of the fog access point in this iteration l (t b )according to set up; (5.5) Update t b =t b +1, Update and Start the next iteration and get a new and b l (t b ), update the bias value of fog access point l according to the bias value update principle; (5.6) Continue iterating until the number of iterations or b l (t b ) remains unchanged for a number of times greater than the preset bias iteration threshold ξ b Stop updating the bias value of fog access point l, and record the bias value set of all fog access points as b; (6) Fix the bias value set of all fog access points and use simulated annealing to update the transmit power of fog access point l; specifically: (6.1) First, set the maximum power value iteration number Set the initial temperature parameter T0 for power adjustment p , Temperature displacement coefficient Temperature drop coefficient T v p , calculate the current power adjustment potential temperature based on the initial temperature parameters and temperature drop coefficient used for power adjustment (6.2) Pass Determine the average displacement distance of the power value at the current potential temperature Use t p Represents the number of iterations corresponding to this power iteration (i.e. the current number of power iterations, the first iteration t p =0), the potential power displacement value of fog access point l in this iteration for: The average displacement distance is based on the power value A random power displacement value that satisfies the normal distribution with centered at ; (6.3) The potential power value obtained by fog access point l in this iteration is: Among them, P l (t p -) is the previous power iteration of fog access point l (i.e., the t p -1 iteration) to obtain the power value, t p =0 when P l (t p -)=P l max ,Increase With reduction The probability of the symbol is 50%. Represents mapping the value of x to the available transmit power set P L The power value closest to the x value; (6.4) p The potential power value set of all fog access points in the iteration is in is the set of power values ​​of all fog access points except fog access point l in the previous iteration, and the set of power values ​​of all fog access points in the previous iteration is recorded as P(t p -), then the power value update principle of fog access point l in this iteration is: That is, the potential power value set P obtained only in this iteration temp (t p ) corresponds to the system energy efficiency value H(P temp (t p )) is greater than the system energy efficiency value H(P(t p -)),the power value P of fog access point l in this iteration l (t p )According to P l temp (t p )set up; (6.5) Update t p =t p +1, Update and Start the next iteration and get the new P l temp (t p ) and P l (t p ), update the power value of fog access point 1 according to the power value update principle; (6.6) Repeat until the number of iterations is or P l (t p ) remains unchanged for a number of times greater than the preset power iteration threshold ξ p When , stop updating the power value of fog access point l, and record the power value set of all fog access points at this time as P; (7) Change the target fog access point and repeat steps (5) to (6). Use simulated annealing to update the bias value and transmit power of the target fog access point respectively until the stop condition is met. Then stop the iteration, set the bias value and transmit power of each fog access point, and use the system energy utility function formula to obtain the final system energy efficiency value.

2. A method for jointly adjusting the range extension bias and transmission power of a fog access point according to claim 1, characterized in that: The step (7) is specifically as follows: out =t out +1 updates the external iteration number, changes the target fog access point, and repeats steps (5) to (6), that is, using simulated annealing to update the bias value and transmission power of the target fog access point respectively, until the external iteration number t out Greater than or equal to the preset external iteration threshold Or when the number of times that the bias value and power value set b and P of the fog access point remain unchanged during the iteration process is greater than the threshold η, the external iteration is stopped, and the bias value and transmission power of each fog access point are set according to the final fog access point bias value set b and power value set P, and the final system energy efficiency value H is obtained using the system energy utility function formula.

3. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a method for jointly adjusting a fog access point range extension bias and a transmission power is implemented as described in any one of claims 1 to 2.

4. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, a method for jointly adjusting a fog access point range extension bias and a transmission power according to any one of claims 1 to 2 is implemented.

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