Air-Ground Cooperative Access Algorithm for UAV-assisted Wireless Networks in Transmission Lines

To reduce the energy consumption of wireless network communication in the transmission line scenario and improve the service life of the network, this paper proposes an air-ground cooperative access algorithm. The algorithm aims to minimize the communication energy consumption of the base station serving transmission line nodes. Firstly, the user rate contribution weight provided by the air-ground base station is optimized, and then the air-ground coordinated power allocation is realized according to the optimal weight. The simulation results show that the proposed air-ground cooperative access algorithm achieves a reduction in energy consumption of over 50% compared to the traditional single base station access method.


INTRODUCTION
With the rapid development of China's economy and society, the electricity demand is increasing daily.Transmission lines are responsible for the long-distance transmission of electricity with a wide distribution and large spans.The communication coverage of transmission lines has always been a hot issue in the industry.Compared with traditional terrestrial wireless networks, UAV-assisted communication has the advantages of high mobility, line-of-sight communication, and low cost [1].It can be deployed on-demand in transmission line networks to meet high-speed, low-latency, and wide coverage needs.However, optimizing node access on the transmission line and reducing base station energy consumption is an urgent problem to be solved in the UAV-assisted wireless communication network [2], given the high energy consumption of the traditional base station access strategy in the transmission line scenario.In recent years, some scholars have considered joint optimization of node access [3], UAV location [4], power allocation [5], and other factors to minimize base station energy consumption.However, in scenarios where the energy of the UAV base station is limited, it cannot meet the user's differentiated speed requirements and maintain long service times, and the energy consumption of the base station remains high.Therefore, an air-ground cooperative access algorithm is proposed, which comprehensively considers the different speeds and service time requirements of transmission line nodes in scenarios where the UAV is used as an aerial base station to assist transmission line communication.Based on the obtained contribution weights, the optimal power allocation scheme of the base station is obtained through the boundary approximation method to minimize base station energy consumption.

SYSTEM MODEL
As shown in Figure 1, we consider a UAV-assisted downlink wireless communication network for transmission line scenarios.Among them, dense sensors are distributed on the transmission line to , When base station j represents a UAV base station, the channel power gain [6] is expressed as the first expression in Formula (1) [7].Since the height of the UAV base station is set high enough, the channel between UAV base station j and node i is a line-of-sight transmission link, where j i  q ω represents the distance when node i accesses UAV base station j and 0  represents the reference channel power gain when 0 d = 1 m.When base station j is GBS, the channel power [8] can be obtained from the second expression in Formula (1), and the distance between node i and base station j is expressed as ij d , where dB ( ) L d is the expression of path loss in dB Model [9].
Therefore, when the signal sent by UAV base station j to transmission line node i is ij s , the signal of node i received from base station j is expressed as follows: , , , where ij s represents the signal sent by base station j to node i and   , ij P represents the transmit power allocated by base station j to node i.In addition, the first term in Formula (2) is the signal that node i expects to receive, the second term is the interference from other UAV base stations, the subscript l represents the l-th UAV base station that generates interference, and the third term ij n is Gaussian White Noise.
When UAV base station j transmits a signal, the useful power received by node i is j ij P h .This chapter assumes that there is only interference between UAV base stations and no interference from GBS, so when node i accesses base station j, its SINR is expressed as: 2 , , where 2  represents the power of Gaussian white noise.Furthermore, it is assumed that the channel changes smoothly over the association process.The SINR takes the average value during the access process, where the fast-fading changes of the channel are ignored and the environmental conditions are determined.ij I represents the interference signal power received by node i from other UAV base stations, and it is expressed as follows: , , , When node i accesses GBS t and UAV base station u, the transmission rates provided by GBS t and UAV base station u to node i are expressed respectively as follows: 2 log ( 1), , where B represents the bandwidth of base station j that is occupied by node i. Considering the different service requirements of different nodes in actual scenarios, there is a minimum rate requirement min i R and service duration i  for node i in the network.
, m i n , , , Therefore, when UAV base station u and GBS t jointly provide services for node i, the actual transmission rate of node i need to meet the following constraints: The power allocation variables iu P and it P also indicate the access status of node i.For example, when Therefore, the total energy consumed by the base station in the transmission line communication system can be expressed as follows:

PROBLEM FORMULATION
The goal of the above system is to reduce energy consumption in communication.We propose an airground collaborative access algorithm to optimize the contribution weight and power allocation of transmission line nodes, considering different node speed requirements and service time to improve the service time of UAVs.UAV base stations offer better communication link quality due to line-ofsight communication than GBS.The specific function for the contribution weight is as follows.
Based on the cooperative access strategy of UAV base station and GBS, node i can access UAV base station u and GBS t in the same time slot.In this paper, i  is set to represent the ratio between the receiving rate iu R of node i provided by UAV base station u to the actual transmission rate , . Therefore, the contribution weight of GBS t to node i is expressed as It can be seen that the contribution weight function we proposed reflects the proportion of the two base stations in the node transmission rate.From the perspective of contribution weight, to minimize the communication energy consumption of the network, it is necessary for the contribution weight of UAV base station to provide services for nodes which is higher than that of GBS.The optimization problem can be formulated as follows.
, min min Constraints (10-a) represent the constraints on power allocation, (10-b) represent the constraints on the transmission rate of transmission line nodes, (10-c) and (10-d) indicate that the UAV base stations need to be located in the system.
To solve the target problem, we adopt a heuristic algorithm to find the optimal contribution weight.First, the received SINR of the serving node of the base station is taken as the factor of the contribution weight, which can be expressed as follows.
Since the UAV base station and GBS are fixed in position, when the transmission line node i accesses the UAV base station and GBS with the closest distance, the path loss is the minimum and the contribution weight obtains the optimal value * i  , expressed as follows: When UAV base station * u and GBS * t jointly provide services for node i, the node transmission rate requirement Formula (7) can be divided into two parts: UAV base station contribution part and GBS contribution part min ( 1) . For the UAV base station, the transmission rate provided for node i needs to satisfy: * min , , For GBS, the transmission rate provided for node i needs to satisfy: According to Formulas (13) and ( 14) and the boundary approximation method [10], the optimal power allocation of the base station can be obtained as follows: As can be seen that the weight function constructed reflects the proportion of the two base stations in the node transmission rate, from the perspective of contribution weight, it is necessary for the contribution weight of UAV base station to provide services for nodes which is higher than that of GBS to minimize the communication energy consumption of the network.The goal of this paper is to minimize the communication energy consumption of the base station by jointly optimizing the contribution weight and power allocation of transmission line nodes, so the problem can be expressed as follows.

AIR-GROUND COOPERATIVE ACCESS ALGORITHM
The air-ground cooperative access algorithm proposed in this paper jointly optimizing the contribution weight and power allocation is shown in Table 1.

Table 1. Air-Ground Cooperative Access Algorithm
Air-Ground Cooperative Access Algorithm 1: Initialize transmission line nodes, UAV base stations, GBS coordinates and Monte Carlo cycle times.Input node transmission rate requirements and service time 2: Step 1: 3: For I = 1: M do 4: Select the best UAV base station * u and GBS * t according to SINR 5: Calculate the optimal contribution weight * i  according to Formula (12) 6: End for 7: Step 2: 8: For u = 1:U do 9: Calculate the optimal power allocation * iu P of the UAV base station according to Formula (17) 10: end for 11: for t = 1:T do 12: Calculate the optimal power allocation * it P of GBS according to Formula (18) 13: end for 14: Output * * * , , iu it i P P  First, the position coordinates of transmission line nodes, GBS and UAV base stations are initialized.Then, the transmission rate requirements and service time requirements of each node are input.In step 1, each node selects the best UAV base station and GBS to access according to SINR and calculates the corresponding optimal contribution weight.Step 2 is to calculate the rate threshold that the base station needs to provide according to the optimal contribution weight.Finally, the power IOP Publishing doi:10.1088/1742-6596/2625/1/0120646 allocation scheme of UAV base station and GBS is calculated based on the boundary approximation method.

SIMULATION RESULTS AND ANALYSIS
To verify the effectiveness of the proposed air-ground coordination algorithm, we consider area D as a circular area with a radius of 500 m, in which there are 6 GBSs and 6 UAV base stations and set the minimum distance between GBSs as 100 m.Among them, the path loss of GBS is expressed as 38.6+27.2log10(d)+0.2and indicates that the shadow effect attenuation value is 10 dB.Fully considering the random distribution of nodes in the area, we first perform 1000 Monte Carlo cycles on the simulation results to observe the change of the average total network energy consumption with the different numbers of nodes.Secondly, to further evaluate the accuracy and effectiveness of the proposed air-ground cooperative access algorithm, we introduce the existing single base station access method for comparison.
The simulation experiment in this paper is set up as follows.Each node occupies a bandwidth of 1 MHz and has an air-to-ground channel power gain of -50 dB.The flying altitude of the UAV base station is set to 150 m, and the Gaussian white noise power is set to -114 dBm.The minimum communication rate range for nodes is between 0.1 Mbps and 10 Mbps.There are six UAV base stations, and each node requires a service time of 10 to 100 minutes.
The base station energy consumed by each node in area D during its service time is shown in Figure 2. In Figure 2, the blue and green histograms represent the total energy consumed when using the air-ground coordinated access algorithm and the single base station access method respectively.It can be seen that, with the air-ground cooperative access algorithm proposed in this paper, the energy consumption of the base station to provide services for each node is much lower than that of the single base station access algorithm.After obtaining the optimal base station contribution weight through the heuristic algorithm, the weight of each node served by the UAV base station will be higher than that of GBS.Therefore, the energy consumed by the base station serving transmission line nodes will be greatly reduced, and the overall energy consumption of the network will be effectively reduced.The base station energy consumed by each node in area D during its service time is shown in Figure 2. In Figure 2, the blue and green histograms represent the total energy consumed when using the air-ground coordinated access algorithm and the single base station access method respectively.It can be seen that with the air-ground cooperative access algorithm proposed in this paper, the energy consumption of the base station to provide services for each node is much lower than that of the single base station access algorithm.After obtaining the optimal base station contribution weight through the heuristic algorithm, the weight of each node served by the UAV base station will be higher than that of GBS.Therefore, the energy consumed by the base station serving transmission line nodes will be greatly reduced, and the overall energy consumption of the network will be effectively reduced.
To evaluate the impact of UAV base stations on network energy consumption, this paper analyzes the energy consumption of a network of 50 nodes.The number and location of GBSs are kept constant while the number of UAV base stations is varied.The paper studies the total network energy consumed by using both single base station access and the air-ground cooperative access algorithm proposed in this paper.Figure 3 shows the simulation results.The red and blue curves represent the average total network energy consumption by using the single base station access algorithm and the air-ground coordinated access algorithm respectively.The changing trend of the curve shows that as the number of UAV base stations increases, the average network energy consumption increases nonlinearly.However, the air-ground cooperative access algorithm has a lower energy consumption growth rate than the single base station access algorithm.This is because the contribution weight of UAV base stations serving nodes increases as the number of UAV base stations increases.Under the same conditions, the contribution weight of GBS service nodes gradually decreases, while the UAV base station, which has the advantage of line-of-sight communication, consumes less energy and its contribution weight increases, resulting in slower growth in the total network energy consumption.

CONCLUSION
This paper proposes a UAV base station and GBS collaborative service access approach for power transmission line nodes to reduce energy consumption and improve network lifetime.The proposed algorithm considers different rate and service time requirements of the nodes and evaluates SINR to obtain optimal contribution weight.Based on the obtained weight, a boundary approximation method is used to determine the base station's optimal power allocation scheme.Simulation results show that the proposed air-ground collaborative access algorithm significantly reduces energy consumption compared to the traditional single-base station method.The simulation results show that the proposed air-ground cooperative access algorithm achieves a reduction in energy consumption of over 50% compared to the traditional single base station access method.

Figure 1 .
Figure 1.UAV-assisted communication system model When the communication node   1,2,..., i M accesses the base station j  N , the channel power gain ij h can be expressed as follows:

U
node i accesses UAV base station u and GBS t at the same time, otherwise 0 represents the energy consumed by the UAV base station, and it i t P    T represents the energy consumed by the GBS.

.
At this point node i can obtain the best SINR jointly provided by UAV base station and GBS.

Figure 2 . 7 Figure 3 .
Figure 2. Energy of the base station consumed by each node