The heterogeneous cloud radio access network (H-CRAN) is considered a promising solution to expand the coverage and capacity required by fifth-generation (5G) networks. UAV, also known as wireless aerial platforms, can be employed to improve both the network coverage and capacity. In this paper, we integrate small drone cells into a H-CRAN. However, new complications and challenges, including 3D drone deployment, user association, admission control, and power allocation, emerge. In order to address these issues, we formulate the problem by maximizing the network throughput through jointly optimizing UAV 3D positions, user association, admission control, and power allocation in H-CRAN networks. However, the formulated problem is a mixed integer nonlinear problem (MINLP), which is NP-hard. In this regard, we propose an algorithm that combines the genetic convex optimization algorithm (GCOA) and particle swarm optimization (PSO) approach to obtain an accurate solution. Simulation results validate the feasibility of our proposed algorithm, and it outperforms the traditional genetic and K-means algorithms.

Mobile data traffic demand is growing exponentially each year due to the increased mobile users and advances in traffic-intensive applications [

User associations between HPN and RRH are the key for improving energy and spectral efficiency. HPN exhibits greater transmission energy compared to RRH. Based on the received power strength, the user tends to associate with the RRH more frequently than the HPN. This creates strong downlink interference for the user equipment (UE), while resulting in uplink interference to nearby UE. Numerous approaches have been proposed in order to reduce this interference. For example, the connection of several RRHs to just one BBU pool allows for more efficient interference management and resource allocation optimization [

There have been many researches focusing on the resource allocation of cloud radio access networks. For example, [

Comparison between different references.

Scenario | Ref. | User allocation | Access control | Power allocation | Objective function | Constraint |
---|---|---|---|---|---|---|

HetNet | [ | Yes | No | No | Minimal data transfer cost and macro utilization | Throughput, QoS |

HetNet | [ | Yes | No | No | Maximum throughput, minimum power, and cost | - |

HetNet | [ | Yes | No | Yes | Maximum performance gain | Power, bandwidth |

HetNet | [ | Yes | No | Yes | Maximum proportional fair throughput | Power, SINR, link rate, throughput |

HetNet | [ | Yes | No | No | Maximum throughput | Link rate |

HetNet | [ | Yes | No | No | Maximum grid utility | User rate, SINR, frequency reuse factor |

HetNet | This paper | Yes | Yes | Yes | Maximum throughput | User rate, UAV, power, user allocation |

In particular, wireless communication technology allows for the rapid application of UAVs as airborne base stations (BSs). Aerial deployment is considered as a promising approach for the universal access from air to ground user equipment (UE) in designated areas during temporary events (e.g., hot spots and events in large public spaces) [

The main bottlenecks related to UAV communications include the 3D deployment of UAVs, energy efficiency, resource allocation, and cell association. Existing research on UAV communications generally focuses on ATG channel model. For example, [

In order to overcome the challenges associated with UAV deployment, [

Different from the aforementioned works, UAV-assisted H-CRAN is employed to optimize network throughput via ground-based macro BSs, RRHs, and aerial UAV platforms. In this paper, we propose an approach that optimizes 3D positions of multiple UAVs, user association, admission control, and power allocation in order to maximize system throughput and user numbers. In order to solve this radio resource management problem, we introduce a two-layer optimization framework. First, the position of the UAV is fixed, and the optimum user association matrix and connection power are then determined in order to maximize a single UAV position value. The UAV position is optimized, and the optimal value is subsequently derived for different UAV positions, thus obtaining the approximate optimal solution of the system.

The main contributions of this work are summarized as follows.

In order to improve the user service quality, we consider a UAV-assisted resource allocation in the H-CRAN system. The system model is established by jointly determining the optimal 3D UAV position, user association, admission, and power distribution. By adjusting the 3D position of the UAV and the user association and combining the power distribution of RRH and UAV, the maximum system throughput is achieved

We formulate a mixed integer nonlinear programming problem (MINLP). More specifically, the 0-1 integer constraints of user association and power allocation in the model are integrated with the branch and bound method to propose an approach based on genetic convex optimization that maximizes user association and resource allocation. In addition, a particle swarm optimization algorithm is implemented to optimize the 3D UAV position

We prove that the proposed algorithm exhibits polynomial-level computational complexity, with an improved performance compared to that of traditional genetic algorithms, and the greedy heuristic and discrete particle swarm optimization. Furthermore, we prove that the performance of the proposed particle swarm optimization algorithm is superior to that of the uniform distribution of UAV and base station and K-means algorithm

The rest of the paper is organized as follows. The system model and MINLP problem formulation are introduced in Section

Table

Summary of abbreviations and notations.

Notation | Description |
---|---|

H-CRAN | Heterogeneous cloud radio access network |

HPN | High power node |

RRH | Radio frequency remote head |

UAV | Unmanned aerial vehicle |

User set | |

RRH set | |

UAV set | |

LoS | Line of sight |

NLoS | None line of sight |

Number of subcarriers | |

Uplink power of user | |

Downlink power of user | |

Uplink power of user | |

Downlink power of user | |

Uplink power of user | |

Downlink power of user | |

Maximum service distance of RF unit | |

Antenna gain of macro cell, RRH, and UAV | |

Channel gain when the | |

Path loss when users connect to the macro cell and RRH | |

Distance between user and RRH | |

Distance between user and RRH | |

The | |

2D distance between user | |

LoS probability of user | |

Average path loss under drone LoS conditions | |

Average path loss under drone NLoS conditions | |

Average additional loss in LoS or NLoS links relative to free space propagation loss | |

Average path loss when the user is connected to the drone | |

Channel capacity when user connects to eNB | |

Channel capacity when user connects to RRH | |

Channel capacity when user connects to UAV | |

User channel capacity matrix | |

User associated cell indicator matrix | |

Association indicator | |

Minimum channel capacity requirement of user | |

Minimum channel capacity requirement matrix | |

User antenna height factor |

The system model is shown in Figure

System model.

We assume that the cloud radio access to heterogeneous cellular networks forms a heterogeneous cloud radio access network (H-CRAN). The network includes macro cell eNB,

We can express the power of the uplink and downlink from the

Two types of channel models are employed in the network, namely, the air-to-ground (A2G) channel model (including UAV-to-UE channels) and the ground-to-ground (G2G) channel model (including user-to-RRH and user-to-eNB channels).

When the distance between user

The wireless link between the ground nodes and UAVs in the proposed problem exhibits both LoS and nonline-of-sight (NLoS) elements. This is attributed to multipath and shadow effects. Note that we only consider the LoS components for the UAV system due to the high flight altitude and limited number of obstacles. Such components can be modelled with free space path loss methods. Two approaches can be employed to characterize the NLoS components: (i) the probabilistic line-of-sight channel model, whereby elevation and altitude are used to express the space-to-ground line-of-sight channel probability. Research has demonstrated higher LoS link probabilities for high UAV altitudes and minimal ground obstacles (e.g., rural areas), and consequently, the NLoS component can be ignored. (ii) The Rician channel model. The relationship between the Rician factor and several environment and the air-to-ground elevation angle variables were investigated in. The UAV channel model employed in this paper is established as follows.

The distance between user

The UAV path loss can thus be described as follows:

The average path loss between

Thus, the channel gain when user

The channel capacity when the user is connected to the macro eNB, RRH, and UAV can be, respectively, expressed as follows:

The user channel capacity matrix can then be determined as

The user can subsequently select the optimal channel for the required connection based on the channel information.

We assume that each user

If the minimum channel capacity requirement for user

The goal of this paper is to determine the optimal 3D layout of the UAV under the user QoS and power allocation constraints and to jointly consider user association and power allocation to maximize the system throughput. The optimization problem can be written as

The solution to this problem is a function of the user association matrix, the UAV 3D position, and the power allocation. Furthermore, the problem is of the NP-complete nonconvex mixed integer type, and thus, exhaustive searching is needed. The calculation costs of the exhaustive search algorithm increase significantly with both the number of users and RRH. In order to overcome this, we employ an intelligent search algorithm to determine an approximate optimal solution, with a relatively low computational complexity.

Determining a solution to the problem described the series of equations in (

Furthermore, the channel capacity and the user association matrices in the

Once the position of the UAVs is fixed, problem

It can be seen that the problem after conversion depends on the association matrix

Standard convex optimization can be used to solve the affine exponential sum as it is a typical convex optimization problem.

Thus, the problem

The flowchart of the optimization algorithm.

Assuming that the optimal power allocation scheme for each connection is known, the genetic algorithm optimizes the power association matrix. More specifically, the user association matrix is taken as a chromosome expression as follows:

In order to obtain excellent simulation results, the first generation chromosomes were carefully selected in this paper. It is foreseeable that connecting all users to the base station with the best channel conditions is an excellent method. And it is also a possible result that all users access the eNB. Therefore, when selecting the first generation chromosomes, these two schemes are regarded as two of the first generation chromosomes. The remaining chromosomes are randomly generated.

The objective function of our framework must satisfy the user QoS and power allocation constraints to maximize the system throughput and can thus be directly applied as the evaluation function

The chromosome inheritance can either originate from preservation or cross mutation. For our framework, we retain the top 25% of the chromosomes.

According to the roulette selection method, chromosomes are selected for cross mutation. The selection is based on the size of the objective function value. Make the chromosome with the larger objective function more likely to be selected, and make the chromosome with smaller objective function less likely to be selected.

The stopping condition of the algorithm is set to stop automatically when the number of iterations reaches a predetermined value. In addition, if the algorithm has not updated the optimal value for 15 consecutive iterations, it is considered that the iteration can be stopped.

Algorithm

1: Initialization:

2: Calculate the channel gain.

3: Produce the first generation genetic factors.

4: Use convex optimization to calculate the best power allocation

5: Calculate the fitness function

6: Set the maximum number of iterations

7: while

8: Calculate the selection probability of each genetic factor.

9: Select the best 1/4 of genetic factors to retain into the next generation.

10: According to

11: Cross the selected genetic factors in pairs.

12: Generate mutated random integer

13: if

14: No changed.

15: else

16: if

17: Randomly select a gene point on the chromosome for 0,1 transformation.

18: else

19: Flip the whole chromosome.

20: end if

21: end if

22: Calculate the optimal power allocation

23: Calculate the fitness function

24:

25: end while

26: Output:

27: Calculate maximum sum rate.

Since the position of the drone has a crucial influence on the result, in addition to determining the user association matrix, the optimal position of the drone also needs to be determined. In this paper, particle swarm optimization is used to find the optimal 3D position of the drone. Specific steps are as follows.

This initial step includes the establishment of the maximum iteration number and the initialization of the population position and maximum particle speed. The space to be searched is taken as the position information, and random points on the speed range and search space are chosen for the initialization. Each particle is then initialized at a random flying speed, with

As the goal of this algorithm is to determine the optimal UAV 3D position, the initial population is set as the initial position of the UAV. It is foreseeable that the uniform distribution of drones and base stations in the entire area is an excellent choice. Therefore, take it as one of the initial positions, and randomly generate positions in M-1 areas as the initial positions of the drone.

The objective function must satisfy the user QoS and power allocation constraints in order to maximize the system throughput. Therefore, the objective function can be employed as the evaluation function, such that

The fitness value of each particle is then calculated based on the fitness function. This enables the updating of the speed and position based on the following formula:

Algorithm

1: Initialization:

2: Randomly generate the position of the first generation particles

3: Initial velocity of randomly generated particles.

4: Set the number of iterations

5: while

6: Calculate the fitness function of each particle according to Algorithm

7: Update speed and position according to formula (

8:

9: end while

10: Output: Best 3D position of drone.

11: Calculated maximum sum rate.

The computational complexity of genetic algorithms and particle swarm optimization is generally a function of the number of iterations. In particular, each gene in the genetic algorithm must solve an NLP problem once per iteration. If

In this section, we compare the UAV-integrated H-CRAN with the traditional terrestrial BS based H-CRAN. We then simulate scenarios with varying UAV 3D positions.

For the simulations, the cell radius is set to 1 km and the maximum user power is 1 W. Users are randomly distributed in a cell of 1 km radius. Two fixed RRHs are located in the cell.

Table

Primary simulation parameters.

Parameter | Symbol | Value | Reference number |
---|---|---|---|

Communication range of RRH | 1000 m | 6 | |

Total allocable power of eNB | 20 W | 6 | |

Total allocable power of RRH | 10 W | 6 | |

Total allocable power of UAV | 10 W | 30 | |

Maximum transmission power of user | 1 W | 6 | |

Carrier frequency | 2 GHz | 6 | |

- | 20 | 28 | |

- | 0.3 | 28 | |

Height of RRHs | - | 70 m | 6 |

Additional path loss under LoS | 1.6 dB | 30 | |

Additional path loss under NLoS | 23 dB | 30 | |

Power of Gaussian white noise | -169 dBm | 30 | |

SINR threshold | -9 dB | 6 | |

Antenna gain of eNB | 50 dB/20 dB | 6 | |

Antenna gain of RRH | 20 dB | 6 | |

Antenna gain of UAV | 20 dB | 28 |

In this section, we investigate the impact of UAVs on user association. Figure

User associations with 0 and 2 UAVs (users: 10-50, and

User associations without UAVs (

User associations with 2 UAVs (

In order to further explore the impact of UAVs on user association, we perform simulations with an eNB antenna gain equal to that of the RRHs and UAVs. Figure

User associations with 0 and 2 UAVs (users: 10-50, and

User associations without UAVs (

User associations with 2 UAVs (

For the 2-UAV system, when the number of users is low, they are more inclined to connect with the UAVs. This is attributed to the greater line-of-sight probability of the UAV compared to the ground base station, as well as the potentially better channel conditions. However, this preference changes when the number of users connected to the drone reaches 20 due to the insufficient power allocation of the UAVs. In order to display the user association more intuitively, Figure

Examples of user associations under different UAV positions (users: 40, UAV: 2, RRH: 2).

UAVs and RRHs are evenly distributed

UAV position is determined by K-means

UAV position is determined by PSO

This is more obvious when the system includes 4 UAVs and no RRH. As shown in Figure

Examples of user associations under different UAV positions (users: 40, UAV: 4, RRH: 0).

UAV position is determined by K-means

UAV position is determined by PSO

In this section, we evaluate the impact of drones on the total communication rate of the user. Figure

Sum rate comparison of systems with and without UAV assistance (user: 10-50).

The total user rate for UAV-assisted systems is significantly higher than that of the system with no UAV. This is a result of the higher line-of-sight probability for the UAV, as well as improved channel conditions compared to the RRH. With the increase in the number of UAVs, this is more obvious, because through the PSO algorithm, drones find more suitable hovering locations. However, as the number of drones increases, the channel gain brought by the increase of drones is getting lower and lower. Therefore, the increase in the total user rate becomes insignificant.

In order to eliminate the possible rise in the total rate due to differences in BS numbers, simulations are performed with the total number of pico-BSs (RRH and UAV) set to 4. The following systems were used: 4 RRHs and 0 UAVs, 2 RRHs and 2 UAVs, and 4 UAVs and 0 RRH.

Figure

Sum rate comparison under different UAV numbers (user: 10-50).

In this section, we compare the performance of the two proposed algorithms, namely, the genetic convex optimization algorithm (GCOA) and particle swarm optimization (PSO). We simulate the convergence speed for the algorithms and compare them with those of commonly used algorithms.

The user association problem of the GCOA is of the MINLP type. Genetic algorithms exhibit an improved performance compared to other approaches for discrete problems. Figure

Variation of fitness function with iteration number (user: 40).

Figure

Performance comparison of different algorithms for user association.

We next analyze the performance of the particle swarm optimization method. Figure

Fitness function variations with iteration number (user: 20).

Figure

Performance comparison across algorithms in terms of optimal UAV 3D positions (2 RRHs and 2 UAVs).

Note that for the 2 UAVs and 2 RRH systems, the K-means algorithm performance does not exceed that of the UAV uniform distribution approach. This is attributed to the absence of the RRH position during the K-means clustering. Some extent covers the same range of users as the RRH, so that the situation of some users with poor channel conditions has not improved. In order to eliminate the influence of this factor, we removed the RRHs and performed the simulations with just the UAVs.

Figure

Performance comparison across algorithms in terms of optimal UAV 3D positions (4 UAVs).

With just the UAV as a base station, the K-means algorithm converges rapidly to a suboptimal solution. However, the presence of ground base stations cannot be ignored in practical systems. Furthermore, our proposed algorithm presents promising results for all systems. And its performance is also due to the K-means algorithm, and the UAV is evenly distributed.

In this paper, we considered the location optimization and resource allocation for a H-CRAN system integrated with UAVs. First, we formulated the MINLP problem in order to maximize the total user rate. Then, a genetic particle swarm optimization algorithm that is able to determine the optimal UAV 3D position, user access, and power allocation was proposed to solve the optimization and allocation problem. Specifically, the genetic convex optimization algorithm was employed for a fixed UAV position in order to obtain the optimal user association and power allocation. Particle swarm optimization then determines the best UAV 3D position. The algorithm includes the advantages of genetic algorithms for discrete problems as well as the fast convergence of particle swarm optimization. Simulation results demonstrated the superiority of the proposed algorithm, with the UAV-assisted H-CRAN exhibiting a better system performance compared to the traditional H-CRAN. The time complexity of the proposed algorithm is completely dependent on the number of iterations and is of the polynomial-level time type. Therefore, the proposed solution can effectively solve problems of joint UAV deployment, user association, and power allocation for future 5G and beyond 5G networks.

All data are explained in the paper.

The authors declare that they have no conflicts of interest.