CN-122001442-A - Low-orbit satellite wave beam hopping and power distribution method

CN122001442ACN 122001442 ACN122001442 ACN 122001442ACN-122001442-A

Abstract

The invention discloses a low-orbit satellite wave beam hopping and power distribution method. The method comprises the steps of establishing a system model of a low-orbit satellite beam communication system, formulating an optimal beam scheduling target in the system model into a utility function G, solving the maximum value of the utility function G through two cooperative agents, wherein the first cooperative agent is a beam hopping agent and is used for optimizing a global beam hopping mode, the second cooperative agent is a power distribution agent and is used for determining communication power of each satellite according to a beam hopping decision, and determining an optimal beam scheduling method of each satellite in the low-orbit satellite beam communication system according to the maximum value of the utility function G. The invention can match limited satellite resources with uneven service demands, balance the load demands among satellites, ensure fair service provision and lighten the mutual interference among wave beams. The results of simulation experiments show that the method is obviously superior to the existing method in a plurality of key performance indexes.

Inventors

LI ZHICHENG
CHENG ZIJING
LIU SHUAIJUN
LIU LIXIANG
YAN WENLI
LI YUANPENG

Assignees

中国科学院软件研究所

Dates

Publication Date: 20260508
Application Date: 20251223

Claims (9)

1. A low orbit satellite wave beam jump and power distribution method comprises the following steps: Establishing a system model of the low orbit satellite beam communication system, and formulating an optimal beam scheduling target in the system model into a utility function G; The maximum value of the utility function G is solved through two cooperative agents, wherein the first cooperative agent is a beam hopping agent and is used for optimizing a global beam hopping mode; and determining an optimal beam scheduling method of each satellite in the low-orbit satellite beam communication system according to the maximum value of the utility function G.
2. The method of claim 1, wherein the system model comprises Low orbit satellite Virtual cells distributed on the ground The low orbit satellites form a set , The virtual cells distributed on the ground form a set N, vi is a covered virtual cell set of satellites i, each satellite i is provided with a phased array antenna for dynamically generating a plurality of high-gain beams, and each time slot is provided with a plurality of high-gain wave beams The satellite i activates at the same time at most K beams to serve K different cells which are located in a virtual cell set Vi covered by the satellite i, the satellite uses a beam hopping technique to select a cell to be served by a current time slot and a power allocation technique to allocate transmission power to each activated beam, all beams share a total bandwidth B and a full frequency multiplexing technique to maximize the utilization of beam frequencies, the total signal to noise ratio of a cell n covered by the satellite i at a time t Wherein, the method comprises the steps of, Is the boltzmann constant, Is the noise temperature, B is the channel bandwidth, Representing interference channel gains from beam m activated by satellite i to cell n at time t; Indicating whether the satellite i serves the cell n at the time t, if so, the value is 1, otherwise, the value is 0; indicating the power allocated by satellite i to cell n at time t, only if When the number of the particles is 1, the particles are, A value of greater than 0; Indicating the channel gain of satellite i when communicating with cell n at time t, the theoretical rate of cell n at time t Actual transmissible data volume of cell n at time t Wherein, the method comprises the steps of, For the maximum amount of data that can be accommodated by cell n at time t, o represents the average packet size, Representing the slot length, the arriving traffic of each cell n at slot t is represented as , Obeying mean value of Each cell is provided with a Poisson distribution with maximum length of At most, each data packet can only stay in the queue Determining resource scheduling decision according to queue state by using first-in first-out principle for each time slot and queue, and recursion relation followed by queue evolution Wherein, the method comprises the steps of, Is the number of packets that cell n successfully transmits in time slot t, Is the number of packets that cell n discards due to timeout or overflow at t time slot, the actual amount of data that can be transmitted Cannot exceed 。
3. The method of claim 2, wherein the utility function And satisfying the constraint condition: 、、、、 and when Time of day ; For long-term network throughput Is used for the normalization of the values of (c), For long-term cumulative average delay Is a normalized value of (2); The total available transmitting power of the satellite i is given by alpha, which is the weight factor of the value 0, 1.
4. The method of claim 1, 2 or 3 wherein the multi-satellite cooperative beam hopping and power allocation process of the low-orbit satellite beam communication system is modeled as a structured markov decision process, wherein the beam hopping agent determines an optimal joint beam irradiation pattern for all Ns satellites at each time slot t based on a current network state, wherein the power allocation agent determines an optimal power allocation scheme for all active beams based on the decision result and the network state of the beam hopping agent, wherein the beam hopping agent shares state space environmental state information of the markov decision process with the power allocation agent and uses a unified global reward signal Collaborative learning, together with the pursuit of optimal system performance.
5. The method of claim 4, wherein the markov decision process includes a state space, an action space, and a reward function; The state space includes global states Wherein Representing the set of queue lengths for each cell at time t, Is the channel gain matrix at time t, The average waiting time of the data packets in each queue at the moment t is set; the action space comprises beam hopping agent actions Power distribution agent action The beam hopping agent action is formed by a matrix Representation, matrix The ith row and nth column elements of (b) Indicating whether satellite i selects cell n at time t, satisfies 、 The power distribution agent acts From a matrix Representation, matrix The ith row and nth column elements of (b) Indicating that satellite i allocates power to cell n at time t, satisfies 、 When (when) Time of day ; The bonus function ; For the throughput bonus function, In order to delay the bonus function, For a constraint penalty for limiting the degree of beam hopping constraint violations, For constraint penalty to limit the degree of power allocation constraint violation, the parameter alpha is a weight factor of the value 0,1, And Is a factor for controlling the strength of the constraint execution.
6. The method of claim 5, wherein the step of determining the position of the probe is performed, , , , , , , , 。
7. The method of claim 1, wherein the markov decision process is trained using an Adam optimizer to optimize parameters of the beam hopping agent and the power allocation agent.
8. A server comprising a memory and a processor, the memory storing a computer program configured to be executed by the processor, the computer program comprising instructions for performing the method of any of claims 1 to 7.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1 to 7.

Description

Low-orbit satellite wave beam hopping and power distribution method Technical Field The invention belongs to the field of satellite communication application, relates to a satellite beam hopping and power distribution method, and in particular relates to a low-orbit satellite beam hopping and power distribution method. Background With the growing global demand for high-speed, low-latency network coverage, low-earth orbit (LEO) satellite communication systems play an increasingly important role in new space-to-earth integrated networks. Multi-beam technology and Beam Hopping (BH) enable satellites to respond more flexibly to the dynamic traffic demands of terrestrial users. By dynamically adjusting the beam coverage position, these techniques significantly improve the efficiency of spectrum resource utilization and enhance the service capabilities of the system. However, due to the uneven distribution of terrestrial users and the high mobility of satellites, relying on BH strategies alone has not been able to meet increasingly complex service scenarios. Furthermore, due to the limited satellite power, how to reasonably distribute the transmit power among the different beams has become another key factor affecting system performance. If only static or uniform Power Allocation (PA) is used, it is often difficult to maximize the overall throughput and ensure service fairness, which results in resource waste in certain areas and service restrictions for certain beams. Thus, joint optimization of BH and PA has become a central challenge in improving performance of multi-beam satellite communication systems. Efficient resource scheduling in LEO systems presents significant challenges due to its inherent complexity. In particular, the beam allocation decision is typically located in a discrete space, as it involves selecting which beams to activate from a limited set at each time slot. Each beam can only be in an "on" or "off" state, or directed to a specific area, which makes it a clear and enumeratable discrete choice. In contrast, the Power Allocation (PA) belongs to the continuous domain, since the transmit power allocated to each active beam can take any real value within the power constraints, thus constituting a continuous optimization problem. When these two aspects are combined, the overall decision space includes discrete combinations of active beams and continuous power allocation values, forming a typical discrete-continuous hybrid action space. This hybrid architecture significantly increases the difficulty of system optimization because neither conventional discrete optimization methods nor conventional continuous control algorithms can be directly applied. In addition, the network topology of satellites varies continuously with orbital motion, and the interference between beams and satellites also varies dynamically, which requires strong adaptability for resource scheduling. In addition, the actual system is concerned not only with the overall throughput, but also with delay and fairness among users. This further increases the diversity and complexity of the optimization objective. Various solutions for Beam Hopping (BH) and Power Allocation (PA) optimization have been proposed in the academia and industry, but the existing methods face some limitations. The traditional optimization method is mainly designed for single satellite scenes or static environments, and is therefore not suitable for dynamic multi-satellite and multi-beam networks. In addition, conventional optimization and heuristic algorithms face computational complexity problems when dealing with large-scale hybrid motion spaces, and thus cannot respond in real-time to system changes. Recent advances in Deep Reinforcement Learning (DRL) have shown potential in resource management, indicating that it can improve adaptability and generalization ability. However, most DRL-based approaches are limited to single satellite scenarios or fail to address inter-satellite cooperation issues, resulting in non-ideal interference management and difficulty in balancing multiple system objectives. Disclosure of Invention The invention aims to provide a beam coordination jump and power distribution method in a multi-beam low earth orbit satellite constellation so as to match limited satellite resources with uneven service requirements. This approach balances the load demands between satellites, ensures fair service provision, and mitigates interference. Secondly, the power distribution is dynamically managed through a cooperative competition mechanism among beams inside each satellite so as to meet the real-time requirement. The invention provides a novel combined optimization framework for Beam Hopping (BH) and Power Allocation (PA) in a multi-LEO satellite multi-beam communication system. The overall problem is expressed as a multi-objective optimization model in which complex joint decision tasks are broken down into two modular sub-problems, beam ho