## 1 Introduction
This method reduces the average delay of users on the basis of effectively improving system performance but does not consider the minimum bandwidth request of the service, resulting in some users not being served and reducing user satisfaction. Literature [18] builds a bandwidth allocation framework by considering factors, such as user business information and channel state information, and proposes a bee colony-based bandwidth allocation method (BO-CL-DBA). This method can achieve high allocation efficiency but ignores the minimum bandwidth request of the business and does not consider the bandwidth allocation of users in the previous cycle so that the fairness among users cannot be satisfied. Literature [19] proposes a cross layer dynamic bandwidth allocation method that balances fairness and utility, applying the resource allocation results of the previous service as an influencing factor in the next allocation to ensure fair allocation of business resources. However, the algorithm complexity is high, and the default number of users meets antenna constraints, which does not meet the access situation of users in practical applications. Reference [20] used swarm intelligence optimization algorithms to study resource allocation problems in communication systems, and the results showed that swarm intelligence optimization algorithms performed well in the field of resource allocation. Literature [21] proposed a bald eagle search algorithm (BES), which can effectively solve various complex numerical optimization problems. Compared with the bee colony algorithm and genetic algorithm, it has faster convergence speed and search ability, but it has the disadvantages of incoordination between local search and global optimization and is easy to fall into local optimum. Aiming at the above problems, this paper proposes a multi-service based adaptive bandwidth allocation method for massive MIMO systems, which combines service information, channel state information, and user bandwidth allocation in the previous cycle. First, it uses factors, such as QoS delay requirements and channel state information, to set dynamic priorities for users and then performs user scheduling according to the dynamic priorities. Then, it considers the minimum guaranteed time slot, assigns the minimum guaranteed time slot to each user first, and then allocates bandwidth according to the principle of maximizing the objective function. Finally, the improved bald eagle search algorithm (IBES) is used to solve the problem, taking into account both system throughput and fairness in user allocation while considering utility. Comparing the above literature with the algorithm proposed in this article, and the comparison results are summarized in Table 1.  
The main contributions of this article are as follows: introducing business priority, channel state information, and the number of unsatisfied request time slots in the previous scheduling cycle to calculate the dynamic priority of users, and scheduling users based on this to improve satisfaction. reassign bandwidth to selected users while ensuring the minimum user demand and establish an objective function to maximize system utility value; introducing chaos theory in the initialization phase of the bald eagle algorithm to make the population search the space more comprehensively and solve the problem that the search time is prolonged due to inappropriate initial value selection; introducing Levy flight in the important stage of optimization to improve the global search and local optimization capabilities of the bald eagle algorithm; introducing reverse learning after the dive phase to improve the quality of the final solution; applying the improved bald eagle algorithm to the bandwidth allocation process to improve the system's throughput, bandwidth utilization, and user relative fairness.