## 1 Introduction  
Multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G communication [1]. By configuring a large number of antennas at the base station transmitting end, it can effectively improve spectrum utilization, energy efficiency, transmission rate, and communication quality [2, 3], which has the potential to meet the rising demand for mobile communication in the future. With the improvement of the quality of life, the types of services are also increasing. In order to enable the massive MIMO system to provide different services for various application scenarios, it is urgent to carry out research on bandwidth allocation that comprehensively considers multiple requirements, such as resource allocation efficiency, throughput, and fairness [4]. Massive MIMO systems transmit a wide variety of data and limited bandwidth resources, making it difficult to guarantee user service requirements. Therefore, reasonable bandwidth allocation has become a major factor limiting the performance of massive MIMO. To sum up, when performing the bandwidth allocation in a massive MIMO system, it is necessary to take business as the premise and comprehensively consider multiple requirements. Bandwidth allocation is mainly divided into channel-aware schemes and other schemes [5]. The former gives priority to users with good channel conditions when allocating bandwidth, while users with poor channel conditions are likely to be starved to death, and the allocation result is unfair. The latter generally allocates bandwidth under ideal channel conditions to ensure minimum traffic, maximum delay boundary,and fairness. Literature [6] proposed a bandwidth allocation method based on user quality of service (QoS) awareness (IHQB), which transformed the optimal scheduling problem into a mixed integer nonlinear programming problem and solved the problem using a heuristic algorithm. It has good performance in terms of the weighted sum of flows and system throughput, but the method simply takes the reciprocal of traffic priority as the weight, ignoring the impact of channel state information on the results. Literature [7] studies the efficient multiplexing of massive multi-user MIMO downlink eMBB and URLLC services. When URLLC data packets arrive, the base station reallocates bandwidth resources but does not consider channel state information. Literature [8] proposes a bandwidth allocation algorithm based on minimum rate requirements to maximize energy efficiency (BABEE), which uses each user's energy efficiency and minimum rate requirements to determine user bandwidth allocation. However, this method only considers the user's minimum rate requirement in the allocation process and does not consider the service type, so it is difficult to meet the user's QoS requirements. Literature [9] proposes a joint optimization framework for wireless networks, which can satisfy more user transmissions, thereby achieving higher link capacity utilization, but only considering the throughput of the system, it is difficult to meet the requirements of the user end. Literature [10] proposes an intelligent dynamic bandwidth allocation algorithm based on machine learning, which divides bandwidth utilization devices into three categories: key programs, medium-level, and low-level, and shows better reliability in a low-bandwidth environment. However, this method only starts from a single cycle, and the fairness of users cannot be guaranteed. Facts have proved that only considering service information or channel information cannot really meet the needs of users, and joint service and channel information allocation has become the focus of research [11; 12; 13; 14; 15]. Literature [16] pointed out that the resource allocation effect of MIMO system is affected by the user's channel conditions and QoS requirements. Literature [17] proposed a bandwidth allocation method combining channel information and user service information and used the improved genetic algorithm to solve the problem.