發(fā)布時間：2024-06-04 00:22

　　在流量密集的無人機(jī)ad-hoc網(wǎng)絡(luò)中,流量擁塞會增加網(wǎng)絡(luò)時延和丟包,大大限制網(wǎng)絡(luò)性能。因此,需要一個流量平衡策略控制流量。本文提出TQNGPSR,一個基于GPSR和Q網(wǎng)絡(luò)的流量感知無人機(jī)ad-hoc網(wǎng)絡(luò)路由協(xié)議。該協(xié)議利用鄰居節(jié)點的擁塞信息實現(xiàn)流量平衡,并用一種強(qiáng)化學(xué)習(xí)算法—Q網(wǎng)絡(luò)算法—評價當(dāng)前節(jié)點每條無線鏈接的質(zhì)量�；�于對這些鏈接的評估,該協(xié)議可在多個選擇中做出合理決定,降低網(wǎng)絡(luò)時延和丟包率。在仿真環(huán)境中測試TQNGPSR、AODV、OLSR、GPSR和QNGPSR。結(jié)果表明,相比于GPSR和QNGPSR, TQNGPSR有更高包到達(dá)率和更低端到端時延。在高節(jié)點密度場景中,TQNGPSR在包到達(dá)率、端到端時延和吞吐量上優(yōu)于AODV和OLSR。

【文章頁數(shù)】：14 頁

【文章目錄】：
1 Introduction
2 Background and related works
    2.1 Traffic balancing
    2.2 Reinforcement learning
    2.3 Reinforcement learning based routing protocols
3 Traffic-aware Q-network enhanced geographic routing
    3.1 Traffic balancing
    3.2 Q-network based route selection
4 Simulation results
    4.1 Simulation results and comparison with other protocols
    4.2 Simulation results under different penalty factors
    4.3 Complexity analysis and comparison with other protocols
5 Conclusions



本文編號：3988600