A Parameter-Specific Retrieval and Knowledge-Guided Reasoning Framework for LLM-Based GPSR Optimization in FANETs

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A Parameter-Specific Retrieval and Knowledge-Guided Reasoning Framework for LLM-Based GPSR Optimization in FANETs

arXiv:2608.06760v1 [cs.NI] 7 Aug 2026

Zhipeng Lin, Bin Duo, Member, IEEE, Tong Liu, Jie Lin, Jianting Yuan and Xiaojun Yuan, Fellow, IEEE

Abstract—Existing Greedy Perimeter Stateless Routing (GPSR)-based protocols for Flying Ad-Hoc Networks (FANETs) struggle to adapt routing parameters, such as hello interval, multi-path number, and greedy forwarding weights, under highly dynamic environments. As an emerging artificial intelligence technology, large language models (LLMs) show potential for intelligent decision-making, providing new opportunities for adaptive adjustment of GPSR parameters to improve network performance. However, applying LLMs to GPSR remains challenging due to irrelevant experience retrieval and the absence of protocol constraints. To address these issues, we propose a ParameterSpecific Multi-Index Retrieval and Knowledge-Guided Reasoning framework for adaptive GPSR optimization (PMKR-GPSR), an LLM-based framework that enables protocol-consistent routing parameter adaptation. We design a parameter-specific multiindex retrieval mechanism to provide LLMs with parameterrelevant experiences while reducing interference from irrelevant information. We further construct a knowledge-guided constraint graph to enforce that the routing parameters satisfy dependency rules and optimization constraints. Simulation results demonstrate that PMKR-GPSR achieves higher packet delivery ratio and lower end-to-end delay under high-mobility FANETs. Index Terms—flying ad hoc networks, greedy perimeter stateless routing, large language models, parameter-specific retrieval, knowledge-guided reasoning.

I. I NTRODUCTION

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LYING Ad Hoc Networks (FANETs) have attracted considerable attention due to their rapid deployment capability and flexible topology. Compared to conventional mobile ad hoc networks, FANETs are characterized by rapidly changing topologies and unstable wireless links, significantly increasing the complexity of routing decisions [1]. Consequently, designing adaptive routing mechanisms capable of maintaining reliable communication remains a critical challenge. Among existing geographic routing protocols, Greedy Perimeter Stateless Routing (GPSR) has been widely adopted due to its low computational complexity and localized forwarding strategy. However, conventional GPSR employs fixed forwarding strategies. In highly dynamic FANETs, fixed routing parameters struggle to adapt to diverse communication scenarios, resulting in suboptimal forwarding decisions, unstable routes, and degraded packet delivery performance [2]. (Corresponding author: Bin Duo.) Zhipeng Lin, Bin Duo, Tong Liu, Jie Lin are with the College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China (e-mail: zhipenglin02@163.com; duobin@cdut.edu.cn; liutong1982@cdut.edu.cn; linjie07@cdut.edu.cn). Jianting Yuan is with the School of Software, Xinjiang University, Urumqi 830046, China (e-mail: steven@xju.edu.cn). Xiaojun Yuan is with the National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: xjyuan@uestc.edu.cn).

To improve GPSR adaptability, numerous optimization methods have been proposed. Some studies enhanced greedy forwarding by considering multiple routing metrics, enabling a more comprehensive evaluation of forwarding nodes and improving forwarding reliability [3]. Other approaches adopted reinforcement learning (RL) to learn adaptive routing strategies through continuous interaction with the network environment [4]. However, RL-based methods generally require extensive exploration and carefully designed reward functions to achieve stable convergence [5]. As network topologies evolve, previously learned policies may become suboptimal, requiring additional adaptation to maintain routing performance in highly dynamic scenarios. Recently, large language models (LLMs) have demonstrated potential for intelligent decision-making in wireless communications due to their knowledge utilization and reasoning capabilities [6]. However, existing studies have shown that LLMs without explicit knowledge constraints and relevant experience may generate hallucinated or infeasible outputs [7]. Therefore, directly applying LLMs to GPSR optimization remains challenging. First, different routing parameters, such as hello interval, multi-path number, and greedy forwarding weights, have heterogeneous dependencies on different network features, causing irrelevant information to interfere with LLM reasoning. Second, the lack of protocol knowledge constraints prevents LLMs from understanding the relationships among network conditions, routing parameters, and performance objectives, which may lead to unreasonable parameter adjustments that violate GPSR principles and deteriorate routing performance. To address these challenges, we propose a ParameterSpecific Multi-Index Retrieval and Knowledge-Guided Reasoning framework for adaptive GPSR optimization (PMKRGPSR). First, to address the heterogeneous feature dependencies of different routing parameters and the interference caused by unified experience retrieval, we design a parameterspecific multi-index retrieval mechanism. Network features are extracted to query corresponding parameter-specific indexes, providing the LLM with relevant experiences for routing parameter generation. Second, to address the lack of protocol knowledge constraints, we construct a knowledge-guided constraint graph. The graph incorporates parameter dependency rules and optimization constraints for LLM reasoning, guiding the generation of routing parameters. Simulation results demonstrate that PMKR-GPSR achieves improved packet delivery ratio (PDR) and reduced end-to-end (E2E) delay under high-mobility FANET scenarios, validating its effectiveness in enhancing routing adaptability and robustness in dynamic network environments.

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Fig. 1: Architecture of the Proposed PMKR-GPSR Framework II. P ROBLEM F ORMULATION A. UAV Network Model We consider a highly dynamic UAV ad hoc network, modeled as an undirected graph G = (U, L), where U = {ui | 1 ≤ i ≤ N } denotes the set of N UAVs and L ⊆ U × U represents the communication links established between neighboring UAVs within the transmission range. B. GPSR Routing Protocol

A. Offline Knowledge Construction

The proposed framework is built on the GPSR protocol [8]. Each node periodically broadcasts Hello packets and updates its neighbor table by receiving Hello packets from onehop neighbors. Based on the neighbor table, the forwarding node selects the neighbor closest to the destination for packet forwarding. Following existing studies on GPSR optimization, this work adopts an adaptive Hello strategy and a multi-path forwarding strategy to improve transmission reliability during packet delivery [1]. In addition, a multi-parameter greedy forwarding strategy is employed to enhance the accuracy of next-hop selection [2]. The forwarding priority of neighbor node uj is calculated as Sj = wd D̂j + wl L̂j + wv V̂j + wn N̂j ,

parameters, and updates experiences through feedback. The proposed framework enhances the reasoning process of an LLM rather than fine-tuning its model parameters. This design avoids the need for large-scale routing-specific training while enabling adaptation to unseen network scenarios. Moreover, PMKR-GPSR explicitly incorporates protocol knowledge into the reasoning process, thereby improving the reliability and interpretability of the generated routing decisions.

(1)

where D̂j , L̂j , V̂j , and N̂j denote the normalized distance to destination, link lifetime, relative velocity, and neighbor degree of candidate node uj , with theirP corresponding weights wd , wl , wv and wn satisfying wi ≥ 0, i wi = 1. III. P ROPOSED PMKR-GPSR F RAMEWORK Fig. 1 illustrates the overall PMKR-GPSR framework. In the offline stage, routing experiences are organized into parameterspecific retrieval indexes, and domain knowledge is encoded into a knowledge-guided constraint graph. During online optimization, the framework retrieves relevant experiences, performs knowledge-guided reasoning, generates adaptive routing

1) Domain Expert Knowledge: Domain expert knowledge is represented as K = (KD , KC ), where KD denotes the parameter dependency rules and KC denotes the optimization constraints. Parameter dependency rules specify the scenario features relevant to each routing parameter. Formally, KD = {(pk , Fk ) | pk ∈ P}, where P = {H, M, w} denotes the set of routing parameters, H represents the hello interval, M denotes the number of paths, and w = [wd , wl , wv , wn ] represents the weights vector of the multi-parameter greedy forwarding strategy in Eq.(1). Fk denotes the scenario feature subset relevant to the optimization of routing parameter pk , which is determined according to the parameter dependency rules summarized in Table I. Table I summarizes the parameter dependency rules for routing parameter optimization. The hello interval is determined by relative mobility and energy condition, balancing neighborhood freshness and communication overhead [9]. The number of forwarding paths is associated with forwarding candidates, link stability, and network congestion to improve routing reliability while limiting unnecessary transmissions [10]. The forwarding weight mainly depends on geographic progress, relative mobility, and neighbor connectivity, enabling the protocol to balance forwarding efficiency, link stability, and robustness under different network conditions [11]. Optimization constraints specify the feasible parameter c adaptation rules. They are represented as KC = {ψj }N j=1 , where Nc denotes the number of constraint rules. Each con-

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TABLE I: Parameter Dependency Rules for Adaptive GPSR Optimization. Routing Parameter Hello Interval

Path Number

Forwarding Weight

Scenario Feature

Key Metrics

Rationale

Relative mobility

vself , µ(vrel ), σ(vrel )

Topology changes induced by high mobility necessitate shorter hello intervals.

Energy condition

e

Low-energy nodes should reduce Hello overhead to preserve network lifetime.

Forwarding candidates

cc

Abundant forwarding candidates enable additional disjoint paths.

Link stability

t∗h , µ(th ), σ(th )

Frequent link failures require more paths to improve reliability.

Network congestion

qself , qmin , µ(q)

Congested nodes should restrict path counts to reduce overhead.

Geographic progress

rp∗ , µ(rp )

Maximizes geographic progress toward the destination.

Relative mobility

µ(vrel ), σ(vrel )

Focuses on relative velocity and link lifetime under high mobility.

Neighbor connectivity

nc , µ(nc )

Neighbor sets mitigate routing void risks emerging in sparse regions.

straint is formulated as ψj : Γj → ∆j , where Γj denotes the network state condition defined based on the scenario features, and ∆j represents the executable parameter adaptation constraints, including allowable ranges and adjustment directions. The constraint knowledge is used for knowledgeguided reasoning during online optimization. 2) Offline Experience Representation: This paper constructs an offline experience database D from simulation records: D = {Ei }N i=1 , where N denotes the total number of experiences and i is the experience index. Each experience is represented as E = (F, P, Y), where F = [Fs , Fn ]⊤ denotes the scenario feature representation. Fs represents self-node information, including node speed vself , candidate count cc , queue length qself and energy e. Here, the candidate nodes refer to neighboring nodes that are geographically closer to the destination than the current node. Fn describes neighboringnode information, including the number of neighbors nc , geographic progress ratio rp , relative speed vrel , link lifetime th , and neighbor queue length q. The geographic progress ratio rp represents the relative percentage of geographic advancement toward the destination achieved by a candidate forwarding node. To address the variable number of neighboring nodes and obtain a fixed-dimensional representation of neighbor node information, each neighbor attribute is summarized using statistical descriptors. According to attribute characteristics, different combinations of the best candidate (*), minimum (min), mean (µ), and standard deviation (σ) are adopted. The performance vector is defined as Y = {y1 , y2 , y3 , y4 }, including packet delivery ratio, average end-to-end delay, control overhead, and energy consumption. Since F have heterogeneous scales, we apply Z-score normalization before index construction. The normalization statistics {µnorm , σlnorm } are computed from the offline dataset and l remain fixed during online inference. Each feature dimension is normalized as xl − µnorm l , (2) zl = σlnorm where xl represents the raw value of the l-th feature, µnorm l and σlnorm denote the offline mean and standard deviation of the l-th feature. The normalized feature is denoted as F̂. B. Parameter-Specific Multi-Index Retrieval Due to heterogeneous dependencies between routing parameters and network states, unified retrieval may introduce irrelevant interference and degrade experience matching. Therefore,

parameter-specific retrieval indexes are constructed based on parameter dependency rules KD to improve retrieval relevance. Specifically, the feature mapping function Φk (·) is applied to extract the parameter-specific feature representation from the scenario feature vector: Φk (F̂i ) = F̂i,Fk , where F̂i,Fk denotes the feature subset selected according to KD . The extracted representation is stored together with the corresponding experience Ei to construct the parameter-specific retrieval index: n  o Sk = Φk (F̂i ), Ei | Ei = (Fi , Pi , Yi ) ∈ D , (3) where Sk denotes the retrieval index for parameter pk . During online optimization, the observation module monitors the current network state Et at time t and constructs the scenario representation Ft . After normalization in Eq.(2), F̂t is projected by Φk (·) to obtain the query representation Qk = Φk (F̂t ). The similarity between the query representation Qk and each projected feature representation in the corresponding parameter-specific retrieval index Sk is computed as dk (Qk , Φk (F̂i )) = − Qk − Φk (F̂i )

, 2

(4)

where dk (·, ·) denotes the similarity function. The retrieved experience set is defined as the Top-M experiences with the highest similarity scores:  


Ak = Ei Φk (F̂i ), Ei ∈ TopM Sk , dk Qk , · , (5) where Ak ⊆ D contains the Top-M most similar historical experiences for optimizing parameter pk . Here, TopM (Sk , dk (Qk , ·)) selects the M retrieval entries in Sk with the highest similarity scores computed by (4). The retrieved experience set provides parameter-specific knowledge for reasoning and routing parameter optimization. C. Knowledge-Guided Constrained Reasoning Although multi-index retrieval provides parameter-relevant experiences, it lacks explicit protocol constraints. Therefore, the domain expert knowledge K is encoded into a knowledgeguided constraint graph to guide LLM reasoning. The constraint graph is modeled as a directed heterogeneous multi-relational graph, formally represented as G = (V, E, R). The node set V = Vs ∪ Vpr ∪ Vp ∪ Vm contains network state, routing principle, routing parameter, and performance metric entities. The dependency rules in KD and the constraints in KC

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are mapped into two semantic relation types: Influence (rI ) and Constraint (rC ), defining the relation set R = {rI , rC }. Accordingly, the edge set is represented as E = EI ∪ EC , where EI and EC denote the sets of edges instantiated by the Influence and Constraint relations, respectively. Each semantic edge is formulated as a relational triplet e = (vi , r, vj ), where e ∈ E, vi , vj ∈ V, and r ∈ R. In the constraint graph, each relation type is represented by (r) an adjacency matrix: A(r) ∈ {0, 1}|V|×|V| , where Aij = 1 indicates the existence of a relation r from vi to vj . During online optimization, a parameter-specific subgraph is extracted by traversing the constraint graph from the target parameter entity vpk . For each relation type r, the neighboring entities are obtained as (r) (r) Nk = {vj | Aji = 1, vi = vpk }.

The node set of the parameter-specific subgraph is: [ (r) Vk = {vpk } ∪ Nk ,

(6)

(7)

and the corresponding edge set is defined as Ek = {(vi , r, vj ) ∈ E | vi , vj ∈ Vk }. Therefore, the retrieved subgraph is represented as Gk = (Vk , Ek ). The LLM performs routing optimization based on an integrated reasoning context: C = {Et , Ak , Gk }. The integrated reasoning context is provided to the LLM, which performs constrained reasoning to generate a routing parameters: P(0) = LLM(C) where P(0) represents the initial routing parameters generated. During the iterative refinement process, P(l) represents the routing parameter set at the l-th iteration. The activated constraint set is obtained as: (8)

The consistency score is defined as: act Ω(P(l) , KC )=

1 act | |KC

X

I(P(l) |= ψj ),

(9)

act ψj ∈KC

where I(·) indicates whether the generated configuration satisfies the corresponding constraint. It is defined as: ( 1, if P(l) |= ψj , I(P(l) |= ψj ) = (10) 0, otherwise. act If Ω(P(l) , KC ) < 1, the violated constraints are summarized act into a violation set Kvio = {ψj ∈ KC | P(l) ̸|= ψj }. Kvio are incorporated into the interaction history and provided to the LLM as additional reasoning context for iterative refinement:

P(l+1) = LLM(C, P(l) , Kvio ),

After knowledge-guided constrained reasoning, the optimized routing parameters P∗t are adopted as adaptive actions. The subsequent network response is monitored to form a new experience: Et+1 . To maintain high-quality experiences in the experience database D, the newly optimized routing parameters are evaluated based on the subsequent routing performance Yt+1 . If the performance is improved, the experience generated from the previous normalized scenario representation F̂t , optimized configuration P∗t , and performance Yt+1 is retained: D ← D ∪ {(F̂t , P∗t , Yt+1 )}.

(11)

until all active constraints are satisfied or the maximum number of iterations Lmax is reached. The final routing parameters are obtained as: P⋆t = P(l) . Consequently, the reasoning process is constrained to protocol-consistent routing configurations, improving the reliability of adaptive routing decisions.

(12)

Specifically, parameter-specific features are extracted using the projection operators Φk (·), and the corresponding retrieval indexes are updated with the new experience: Sk ← Sk ∪ {(Φk (F̂t ), (F̂t , P∗t , Yt+1 ))}.

r∈R

act KC = {ψj ∈ KC | Ft |= Γj }.

D. Adaptive Action and Experience Update

(13)

Otherwise, if no performance improvement is observed, the new configuration is discarded or the system rolls back to the previous routing parameters. The latest state, action, and performance are stored in the interaction history to provide temporal context for future optimization decisions rather than an additional reasoning loop. IV. P ERFORMANCE E VALUATION The proposed framework is evaluated using the NS-3.34 network simulator. A three-dimensional FANET with 200 UAVs is deployed in a 2000 × 2000 × 200 m3 area, where the communication range is set to 250 m . The UAV mobility follows the Gaussian-Markov mobility model, and the node speed varies from 10 to 80 m/s. Each simulation lasts for 600 s. The traffic model is the constant bit rate model (CBR) with a bit rate equal to 2 Mbps, and each data packet is 1024 bytes. The proposed optimization framework is triggered every 10 s on the source node and forwarding nodes. The offline experience database contains N = 16, 800 routing experiences, with M = 5 retrieved experiences and Lmax = 3 maximum LLM refinement iterations. The proposed framework is compared with GPSR (original geographic protocol) [8], CF-GPSR (multi-metric geographic protocol) [3], and QLGR (reinforcement learning-based protocol) [4]. To evaluate component contributions, the full model and four ablation variants are evaluated: w/o Multi-Index, w/o KG, w/o Multi-Index & KG, and LLM-only. Specifically, w/o Multi-Index replaces parameter-specific retrieval indexes with a unified index; w/o KG removes the knowledge-guided constraint graph; w/o Multi-Index & KG removes knowledgeguided constraints from the single-index retrieval; and LLMonly directly uses the LLM for routing parameters generation without retrieval or knowledge constraints. Table II evaluates the inference latency and routing performance of different Qwen2.5-Instruct model scales under a UAV speed of 40 m/s. The results show that smaller models (0.5B and 1.5B) exhibit limited reasoning capability for analyzing routing parameter dependencies and protocol constraints, resulting in insufficient optimization effectiveness

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Fig. 2: PDR: Baselines.

Fig. 3: Delay: Baselines

TABLE II: Performance evaluation of different model scales. Model Scales PDR (%) Delay (ms) Inference latency (ms)

0.5B 35.6101 326.8720 1838.770

1.5B 37.4435 277.3431 2126.063

3B 67.8925 232.1994 2661.467

7B 68.4435 238.2675 3664.793

and performance close to the original GPSR. Although 3B and 7B models achieve better routing performance, the 7B model introduces substantially higher inference latency with limited performance improvement. Considering the trade-off between reasoning capability and computational overhead, Qwen2.53B-Instruct is selected as the simulation model. Fig.2 illustrates that the PDR of all protocols decreases as the mobility increases. In particular, when the speed reaches 80 m/s, the PDR of the baseline protocols experiences a significant degradation. In contrast, PMKR-GPSR maintains a relatively stable PDR. This improvement benefits from the parameter-specific retrieval mechanism and knowledge-guided reasoning strategy, which adapt routing parameters to network states and reduce the impact of rapid topology variations. CF-GPSR achieves better stability than GPSR by integrating multiple forwarding metrics for more comprehensive neighbor evaluation. QLGR improves routing decisions through reinforcement learning, but its performance is constrained by the requirements of interaction and policy convergence. GPSR exhibits the most significant performance degradation because it relies primarily on geographic distance and does not consider dynamic link characteristics. As shown in Fig.3, the E2E delay of all protocols increases with mobility. Other protocols suffer from more significant degradation due to increased environmental complexity and frequent topology changes. PMKR-GPSR achieves the lowest delay under high mobility scenarios by leveraging knowledgeguided reasoning and parameter-specific retrieval. PMKRGPSR can identify routing strategies that better match current network states and avoid inappropriate forwarding decisions. Consequently, it reduces route failures, retransmissions, and inefficient forwarding operations under dynamic conditions. Fig.4 and Fig.5 present the PDR and E2E delay performance of different ablation variants, respectively. Compared with LLM-only, w/o Multi-Index & KG achieves improved performance, indicating that retrieved routing experiences provide useful guidance beyond LLM reasoning alone. The comparison between w/o Multi-Index and PMKR-GPSR demonstrates that parameter-specific retrieval provides more relevant experiences for each routing parameter, reducing irrelevant information interference and improving optimization accuracy. Compared with w/o KG, PMKR-GPSR achieves better

Fig. 4: PDR: Ablation.

Fig. 5: Delay: Ablation.

performance, indicating that knowledge-guided reasoning incorporates routing constraints into the optimization process and prevents unreasonable parameter configurations. These results validate the necessity of the proposed framework, which integrates parameter-specific retrieval and knowledgeguided reasoning to achieve adaptive routing optimization under highly dynamic network conditions. V. C ONCLUSION This paper proposed PMKR-GPSR, an LLM-based framework for GPSR optimization. A parameter-specific multiindex retrieval mechanism was developed to improve the relevance of retrieved routing experiences, and a knowledgeguided constraint graph was introduced to regulate routing parameter optimization. Simulation results demonstrate that PMKR-GPSR achieves higher PDR and lower E2E delay than existing methods under high mobility scenarios. These results indicate that the proposed framework can effectively perceive dynamic network variations and timely adjust routing strategies, select more suitable forwarding schemes in highly dynamic FANET environments. R EFERENCES [1] A. M. Rahmani et al., “An adaptive and multi-path greedy perimeter stateless routing protocol in flying ad hoc networks,” Veh. Commun., vol. 50, p. 100838, Dec. 2024. [2] S. Dong and Z. Tang, “LPMD-GPSR: LSTM-based link stability prediction and multi-parameter decision-making for adaptive routing in FANETs,” Comput. Commun., vol. 250, p. 108458, Mar. 2026. [3] A. M. Rahmani et al., “A novel cylindrical filtering-based greedy perimeter stateless routing scheme in flying ad hoc networks,” Veh. Commun., vol. 52, p. 100879, Apr. 2025. [4] X. Qiu et al., “QLGR: A Q-learning-based geographic FANET routing algorithm based on multiagent reinforcement learning.” KSII Trans. Internet Inf. Syst, vol. 15, no. 11, Nov. 2021. [5] W. Lee and J. Park, “LLM-empowered resource allocation in wireless communications systems,” IEEE Access, vol. 14, pp. 15 260–15 272, Jan. 2026. [6] X. Wang et al., “Chain-of-Thought for large language model (LLM)empowered wireless communications,” IEEE Commun. Mag., pp. 1–8, May 2026. [7] D. Yuan et al., “Enhancing large language models (LLMs) for telecom using dynamic knowledge graphs and explainable retrieval-augmented generation,” IEEE Wirel. Commun, Mar. 2026. [8] B. Karp and H. T. Kung, “GPSR: greedy perimeter stateless routing for wireless networks,” Proc. 6th Annu. Int. Conf. Mobile Comput. Netw., p. 243–254, Aug. 2000. [9] O. M. Alsalami et al., “A novel optimized link-state routing scheme with greedy and perimeter forwarding capability in flying ad hoc networks,” Mathematics, vol. 12, no. 7, p. 1016, Mar. 2024. [10] A. A. Ateya et al., “QL-AODV: Q-learning-enhanced multi-path routing protocol for 6G-enabled autonomous aerial vehicle networks,” Future Internet, vol. 17, no. 10, p. 473, Oct. 2025. [11] M. Hosseinzadeh et al., “A new version of the greedy perimeter stateless routing scheme in flying ad hoc networks,” J. King Saud Univ. Comput. Inf. Sci., vol. 36, no. 5, p. 102066, May 2024.

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