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Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission

Ruichen Zhang, Hongyang Du, Yinqiu Liu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Abbas Jamalipour, Dong In Kim

arXiv:2404.09134v2cs.NIcs.LG

TL;DR

Satellite communication networks require scalable modeling and resource allocation despite complex scenarios and interference. The paper uses an LLM-and-RAG generative AI agent for customized formulation, then applies MoE-PPO with specialized experts and gating for optimization. Simulations validate the formulation framework and report that MoE-PPO outperforms the cited benchmarks, including greedy and random baselines.

  • Problem

    Satellite communication networks face complex modeling requirements and difficult resource allocation under limited resources, diverse users, and serious transmission interference.

  • Method

    An LLM-and-RAG generative AI agent performs customized system problem formulation, while MoE-PPO uses specialized expert networks and a gating network to optimize the formulated problem.

  • Results

    MoE-PPO outperforms greedy and random baselines by about 220% and 38%, respectively, and achieves an 8.3% higher sum rate than PPO with 2 LGUs.

  • Takeaways & Limitations

    The simulations support using generative AI agents for adaptive satellite-network modeling and MoE-PPO for transmission strategy design across customized modeling problems.

Abstract

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In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by the complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregates them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated.

I. INTRODUCTION

Satellite communication networks face complex modeling requirements and difficult resource-allocation problems, motivating a generative AI agent for customized formulation and an MoE approach for solving the resulting optimization problems.

  • LEO satellite networks support global, low-latency connectivity but must scale to increasing user demand and capacity requirements.
  • Challenges: Challenge I arises from complex satellite modeling involving orbital geometry, atmospheric effects, traffic non-uniformity, scenarios, channels, access protocols, and optimization goals.
  • Challenges: Challenge II concerns resource allocation under wide coverage, distributed users, limited resources, fixed beam coverage, diverse requirements, and serious transmission interference.
  • Generative AI agent: The generative AI agent combines LLMs and RAG to support interactive, customized mathematical modeling for specific satellite communication scenarios.RAG retrieves relevant background knowledge while the LLM interprets natural-language requests and generates detailed responses.
  • MoE-PPO: The proposed MoE-PPO method assigns specialized optimization variables to expert networks and aggregates their outputs through a gating network for joint transmission-strategy optimization.The approach targets formulated problems and is intended to improve spectrum-resource utilization while meeting communication-quality requirements.
  • Research gap: Prior MoE networking studies mainly enhance model architectures, whereas this work focuses on using MoE for resource optimization after generative-agent-based problem formulation.

III. GENERATIVE AI AGENT FRAMEWORK

The framework constructs customized satellite communication models across scenarios, access protocols, channel models, and optimization goals. It represents homogeneous and heterogeneous networks with protocol- and channel-specific formulations.

  • The generative AI agent customizes satellite communication model formulation across four aspects: scenarios, access protocols, channel models, and optimization goals.A specialized database supports generation because satellite modeling is difficult and complex.
  • 1) Scenarios: The framework considers homogeneous and heterogeneous satellite communication scenarios.The homogeneous case uses a downlink LEO satellite serving LEO ground users, while the heterogeneous case combines GEO and LEO satellites sharing spectrum.
  • 2) Access Protocols: In the heterogeneous scenario, SDMA serves multiple ground users through spatial separation, while 1-layer RSMA mitigates cochannel interference at the LEO satellite.The corresponding SINR expressions depend on the selected access protocol and channel vectors.
  • 3) Channel Models: Fixed channels assume unchanged statistical characteristics, whereas time-varying channels model rapidly changing effects such as Doppler shift.The time-varying model represents small-scale fading as a first-order complex Gauss Markov process.

4) Optimization Goals:

The framework defines spectral-efficiency and energy-efficiency objectives and uses conversational LLM-based modeling with RAG to configure them. It addresses modeling errors caused by mismatched protocol or channel choices.

  • 4) Optimization Goals:: Satellite communication optimization goals include maximizing spectral efficiency and energy efficiency.Energy efficiency is defined as the ratio of sum achievable rate to total power consumption.
  • The achievable information-rate expressions define spectral-efficiency objectives for GGU and LGU signals, including common and private message parts.The common-message rate is constrained by the minimum achievable common rate across LGUs.
  • Incorrect rate ordering for RSMA or using stable channels for rapidly moving satellites can significantly affect model correctness and communication efficiency.These examples illustrate why protocol and channel selections must match the communication setting.
  • The agent uses LLMs to establish a conversational modeling procedure that extracts task-relevant semantics from users’ natural-language descriptions.The semantic router invokes corresponding function calls during interaction.
  • RAG retrieves satellite-communication expertise so the LLM can generate configurations for each modeling aspect.The retrieved knowledge supports complicated mathematical modeling because general LLMs lack satellite expertise.

B. Semantic Router

The semantic router narrows natural-language queries to relevant satellite-communication expertise before RAG generation. Its two-layer organization routes queries first to one of four blocks and then to one of two sub-blocks.

  • B. Semantic Router: Expertise knowledge is organized into four blocks, each containing two configuration-specific sub-blocks.The blocks correspond to modeling aspects, while sub-blocks represent configurations within each aspect.
  • B. Semantic Router: The router converts users’ natural-language descriptions into calls for specific expertise sub-blocks.This routing step is the prerequisite for retrieving accurate system-model knowledge.
  • B. Semantic Router: The two-layer semantic router uses cosine similarity to route a query to the most relevant block and then the most relevant sub-block.Layer 1 selects the aspect; layer 2 repeats the process for a configuration.
  • B. Semantic Router: Organizing expertise into eight sub-blocks reduces the retrieval space by eight times.The block and sub-block configuration can be customized by adding or removing components for application-specific scenarios.
  • B. Semantic Router: After routing, relevant chunks are retrieved and supplied to the LLM to generate the satellite communications model.The generated output contains the scenario, access protocol, channel, and optimization goal.

1) Retrieval Mechanism:

The retrieval mechanism combines dense query–knowledge matching with a pluggable LLM generator. Retrieved chunks provide contextual support for modeling answers, while the formulated optimization problem remains computationally difficult.

  • 1) Retrieval Mechanism:: The retriever uses a dense passage retrieval bi-encoder to match query and knowledge representations.Query and knowledge text are separately encoded into dense representations for retrieval.
  • 1) Retrieval Mechanism:: Knowledge in the selected sub-block is split into chunks, and the most relevant chunks are retrieved to support satellite communication model generation.The retrieval process supplies supplementary context for the LLM.
  • 1) Retrieval Mechanism:: A pluggable GPT-based generative module uses retrieved knowledge and prior generated tokens to produce coherent modeling answers.The generator can be replaced by any mainstream LLM.
  • 1) Retrieval Mechanism:: RAG training jointly optimizes the retriever and generator by minimizing negative marginal log-likelihood over input/output pairs.The training procedure uses stochastic-gradient-descent Adam.
  • The framework generates a joint optimization problem over private beamforming vectors, common beamforming vectors, and common rates under power and rate constraints.The heterogeneous RSMA time-varying-channel sum-rate problem is presented as an open issue that the agent can formulate.
  • For any customized modeling across the four aspects, the formulated problem is NP-hard.The paper supports this by reducing the problem, with selected variables fixed, to multi-ratio fractional programming.

IV. PROPOSED PPO WITH MOE APPROACH

The paper introduces MoE-PPO for an NP-hard formulated satellite-network problem, combining specialized expert networks with a gating network and PPO. Experts optimize variables suited to their training, while back-propagation maximization refines the joint strategy.

  • Problem (23) is NP-hard because it contains heterogeneous variables and a complex optimization environment.
  • MoE architecture: MoE integrates specialized expert networks under a gating network that weights experts according to the current input.Each expert processes the input independently, while the gating network generates the expert-weight vector.
  • MoE-PPO framework: MoE-PPO combines an actor-critic structure with a dynamic ensemble of expert policies and a critic that evaluates state values.The actor makes decisions through weighted expert policies, while the critic guides policy improvement through VθC(s).
  • Policy optimization: The gating function selects and aggregates expert policies by current state, but its categorical decisions complicate PPO gradient optimization.The paper addresses this difficulty with a new actor-gradient estimation approach.
  • Policy optimization: Back-propagation maximization updates experts using the action with the highest advantage among experts.An indicator assigns the gradient to the expert whose action has the greatest advantage in the current state.
  • Policy optimization: The proposed MoE-PPO refines the overall strategy from multiple expert policies while mitigating the influence of poorly performing experts through gating weights.

3) Objective Function with MOE:

The objective-function formulation adapts PPO’s surrogate objective to the MoE policy while retaining trust-region control and clipped updates. Actor and critic parameters are then refined with mini-batch stochastic gradient descent.

  • The MoE-PPO surrogate objective adapts PPO’s objective to the mixture-of-experts policy.The formulation preserves PPO’s core principles while incorporating expert contributions.
  • PPO compares the current and old policy probabilities through an importance-sampling ratio weighted by the advantage function.
  • The policy update satisfies a trust-region constraint based on KL divergence between the current and old policies.ε is the positive hyperparameter controlling the constraint.
  • The constrained objective is approximated with a clipped ratio that restricts ρt(θA) to [1 − ε, 1 + ε].The clipping hyperparameter ε decays during training.
  • The proposed MoE-PPO pipeline is summarized in Fig. 3, while back-propagation maximization and gating have precedent in existing studies.

C. MDP Formulation

The MDP formulation defines states, actions, rewards, and discounting for the satellite-network control problem. The action design represents beamforming and rate decisions while enforcing constraints through parameterizations and activation functions.

  • C. MDP Formulation: The MDP is defined by the tuple ⟨S, A, R, γ⟩, representing state space, action space, reward function, and discount factor.
  • 1) Action Space:: Each expert generates specific actions involving beamforming and common-message variables in the action space.
  • 1) Action Space:: Complex-valued beamforming vectors are decomposed into transmit-power magnitudes and unit-norm beam direction vectors.The magnitude represents transmit power, while the unit-norm vector represents beam direction.
  • 1) Action Space:: The hyperbolic tangent activation constrains selected transmit-power outputs to the interval [−1, +1].
  • 1) Action Space:: The neural-network implementation uses activation functions, weights, previous-layer outputs, and biases to produce action components.
  • 1) Action Space:: Maximum ratio transmission and zero forcing are used to construct beamforming strategies.The zero-forcing construction uses the channel-related matrix G and its associated beamforming matrix V.
  • 1) Action Space:: The common-message achievable rate is parameterized with a hyperbolic tangent function to satisfy common-rate constraints.
  • 1) Action Space:: The action space has cardinality (2K + 1) and includes users’ current information, the selected action vector, and the instantaneous reward.

2) State Space:

The state and reward design supplies the MoE-PPO agent with user information, prior actions, and constraint-aware feedback. Rewards combine an energy-efficiency objective with penalties, while positive reward requires satisfying all constraints.

  • User information in the state includes SINR values for GGU and LGU from the previous time step.
  • The state contains current user information, the selected action, and the instantaneous reward generated from that state-action pair.
  • The state space is defined from these components and has cardinality (4K+M+1).
  • The reward function combines an unconstrained energy-efficiency term with penalties for constraint violations.
  • Penalty terms represent violations of power-budget, common-message decoding, LEO QoS, and GEO QoS requirements.
  • The method applies penalties to complicated constraints while using an activation function to guarantee the simple constraint.The paper notes that penalties can induce sparse rewards and high variance.
  • Positive reward is awarded only when all constraints are satisfied, requiring simultaneous optimization of sum rate and feasibility.
  • The simulations evaluate both the generative AI agent framework and the MoE-PPO approach.

A. Simulation Parameters and Setup

The simulations use a heterogeneous one-LEO/one-GEO scenario and implement the generative agent and MoE-PPO algorithm with specified neural-network and interaction procedures.

  • Simulation scenario: The simulated heterogeneous scenario contains one LEO satellite, one GEO satellite, two GGUs, and two LGUs, with both satellite power budgets set to 50 dBm.GGU and LGU antennas each have 8 antennas; rate requirements are 1 b/s/Hz and 0.1 b/s/Hz, respectively.
  • Generative AI agent: The generative AI agent uses OpenAIEmbeddings for user and expertise encoders, GPT-3.5 as the LLM, and LangChain for routing, memory, and conversation management.
  • MoE-PPO procedure: Each MoE-PPO expert produces an action from the current state and gating mechanism, while transitions are stored and sampled for subsequent updates.The procedure observes states, obtains rewards, stores transitions, samples mini-batches, and updates using stochastic gradient descent.
  • MoE-PPO procedure: The algorithm calculates advantage functions for all experts and obtains a maximization value through back-propagation before producing actions.

3) MoE-PPO Approach Settings:

The evaluation studies agent-generation accuracy and MoE-PPO behavior across expert counts, protocols, user loads, and optimization objectives. Results identify retrieval settings and expert specialization as important design choices.

  • Satellite communications modeling: The agent captures semantic keys from user descriptions, retrieves corresponding expertise, and constructs a complete satellite communication model through interactive interactions.In the illustrated case, six interaction rounds accomplish five-step modeling, including one role-assignment round.
  • Generation accuracy: Retrieval rate measures the ratio of correct knowledge retrieval and answer construction, while generation accuracy is defined as n_c/5 across five queries.
  • Generation accuracy: Chunk size and retrieved-chunk number that are too small or too large reduce generation accuracy; the selected settings are chunk size 500 and number 5.Insufficient retrieval lacks expertise, whereas excessive redundant expertise makes analysis difficult.
  • Convergence: MoE-PPO and PPO converge as episodes increase, with MoE-PPO achieving about 5% higher sum rates than PPO in each iteration.
  • Different number of experts: Three experts achieve the highest sum rate, whereas five experts reduce performance through task oversegmentation and increase running time exponentially.With three experts, variable categories receive distinct specialization before gating aggregates the optimized variables.

3) Different number of LGUs:

The evaluation examines user-load scaling and alternative optimization goals. MoE-PPO consistently outperforms PPO, although increasing LGUs lowers achievable sum rate.

  • Different number of LGUs: Achievable sum rate decreases as the number of LGUs increases for both MoE-PPO and PPO because more resources must serve users with poor channel quality.
  • Different number of LGUs: With 2 LGUs, MoE-PPO achieves an 8.3% higher sum rate than PPO.
  • Different optimization goals: Under time-varying conditions, MoE-PPO outperforms traditional PPO by about 10% for energy-efficiency maximization and 12% for transmit-power minimization.The evaluation tests adaptability across different optimization goals.
  • Overall evaluation: The paper combines a generative AI agent for adaptive system modeling with MoE-PPO for resource allocation and interference management in satellite networks.
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