Throughput Optimization of Secondary Link in Cognitive UAV Network
-
摘要:
无人机(UAV)的便携性和高机动性使其与认知无线电(CR)结合的应用场景更加实用。在构建的无人机认知无线网络(CRN)模型中，该文提出UAV单弧度吞吐量优化方案，在确保检测概率的前提下优化感知弧度最大化UAV平均吞吐量。考虑在信道条件不理想情况下进一步改善感知性能，提出基于协作频谱感知(CSS)的多弧度吞吐量优化方案，利用交替迭代优化(AIO)算法对感知弧度和弧度数量进行联合优化以最大化吞吐量。仿真结果表明，该文提出的多弧度协作频谱感知方案在信道衰落严重时，对于主用户(PU)服务质量(QoS)和UAV吞吐量有明显提升。
Abstract:The application of Unmanned Air Vehicles (UAV)-enabled Cognitive Radio (CR) is widely used due to the convenience and high mobility of the UAV. In the UAV-based Cognitive Radio Network (CRN), the throughput optimization scheme in single radian is firstly investigated, in which the sensing radian is optimized to maximize the average throughput of UAV. Then, a multi-radian throughput optimization scheme based on Cooperative Spectrum Sensing (CSS) is studied to improve the sensing performance under the non-ideal channel, and the throughput of the UAV is maximized by utilizing an Alternative Iterative Optimization (AIO) algorithm. The simulation results show that the proposed scheme has better performance on improving the throughput of the UAV and ensuring the Quality-of-Service (QoS) of the Primary User (PU) when the channel fading is serious.
-
Key words:
- Cognitive Radio (CR) /
- Unmanned Air Vehicle (UAV) /
- Spectrum Sensing (SS) /
- Frame structure /
- Throughput
-
表 1 交替迭代优化算法
初始条件：$k = 0,i = 0,N = {N_i}$，误差精度为$\delta $； 1：while $\left| {{R_{\rm{A}}}({\beta _0}_{_k},{N_i}) - {R_{\rm{A}}}({\beta _0}_{_{k - 1}},{N_{^{i - 1}}})} \right| > \delta $ do 2： 利用二分法，求出$N = {N_{^i}}$时的最优弧度${\beta _0}^*$ 3： 令${\beta _0}_{_{^{k + 1}}} = {\beta _0}^*$ 4： 利用枚举法，求出${\beta _0}_{_{^{k + 1}}}$对应的最优数量${N^*}$ 5： 令${N_{^{i + 1}}} = {N^*}$ 6： 求出${R_{\rm{A}}}({\beta _0}_{_{^{k + 1}}},{N_{^{i + 1}}})$ 7： 令$k = k + 1,\;\;\;i = i + 1$ 8：end 输出：${\beta _0}^* = {\beta _0}_{_k},{N^*} = {N_{^i}}$ 
下载: 导出CSV
表 2 仿真参数
参数 数值 参数 数值 参数 数值 ${R_{\rm{P}}}$(m) 320 $B$(rad) $\pi /3$ ${P_{\rm{r}}}(\mu = 1)$ 0.2 ${R_{\rm{S}}}$(m) 50 ${\omega _1}$ 9.6 ${L_{{\rm{LoS}}}}$ 3 $H$(m) 60 ${\omega _{\rm{2}}}$ 0.28 ${L_{{\rm{NLoS}}}}$ 10 $f$(kHz) 500 ${f_{\rm{s}}}$(kHz) 60 ${\bar P_{\rm{d}}}$ 0.9 ${P_{\rm{S}}}$(W) 10 ${P_{\rm{P}}}$(W) 10 ${\bar Q_{\rm{d}}}$ 0.9
下载: 导出CSV
-
NIU Haoran, GONZALEZ-PRELCIC N, and HEATH R W. A UAV–based traffic monitoring system–invited paper[C]. The 87th IEEE Vehicular Technology Conference (VTC Spring). Porto, Portugal, 2018: 1–5. doi: 10.1109/vtcspring.2018.8417546. 高杨, 李东生, 程泽新. 无人机分布式集群态势感知模型研究[J]. 电子与信息学报, 2018, 40(6): 1271–1278. doi: 10.11999/JEIT170877GAO Yang, LI Dongsheng, and CHENG Zexin. UAV distributed swarm situation awareness model[J]. Journal of Electronics &Information Technology, 2018, 40(6): 1271–1278. doi: 10.11999/JEIT170877 倪磊, 达新宇, 王舒, 等. 基于物理层信息加密的卫星隐蔽通信研究[J]. 工程科学与技术, 2018, 50(1): 133–139. doi: 10.15961/j.jsuese.201700160NI Lei, DA Xinyu, WANG Shu, et al. Research on satellite covert communication based on the information encryption of physical layer[J]. Advanced Engineering Sciences, 2018, 50(1): 133–139. doi: 10.15961/j.jsuese.201700160 赵太飞, 许杉, 屈瑶, 等. 基于无线紫外光隐秘通信的侦察无人机蜂群分簇算法[J]. 电子与信息学报, 2019, 41(4): 967–972. doi: 10.11999/JEIT180491ZHAO Taifei, XU Shan, QU Yao, et al. Cluster–based algorithm of reconnaissance UAV swarm based on wireless ultraviolet secret communication[J]. Journal of Electronics &Information Technology, 2019, 41(4): 967–972. doi: 10.11999/JEIT180491 GUPTA A and JHA R K. A survey of 5G network: Architecture and emerging technologies[J]. IEEE Access, 2015, 3: 1206–1232. doi: 10.1109/ACCESS.2015.2461602 SULTANA A, ZHAO Lian, and FERNANDO X. Energy–efficient power allocation in underlay and overlay cognitive device–to–device communications[J]. IET Communications, 2019, 13(2): 162–170. doi: 10.1049/iet-com.2018.5464 LI He, OTA K, and DONG Mianxiong. Learning IoT in Edge: Deep learning for the internet of things with edge computing[J]. IEEE Network, 2018, 32(1): 96–101. doi: 10.1109/MNET.2018.1700202 SALEEM Y, REHMANI M H, and ZEADALLY S. Integration of cognitive radio technology with unmanned aerial vehicles: Issues, opportunities, and future research challenges[J]. Journal of Network and Computer Applications, 2015, 50: 15–31. doi: 10.1016/j.jnca.2014.12.002 NI Lei, Da Xinyu, HU Hang, et al. Outage constrained robust transmit design for secure cognitive radio with practical energy harvesting[J]. IEEE Access, 2018, 6: 71444–71454. doi: 10.1109/ACCESS.2018.2881477 XU Wenbo, WANG Shu, YAN Shu, et al. An efficient wideband spectrum sensing algorithm for unmanned aerial vehicle communication networks[J]. IEEE Internet of Things Journal, 2019, 6(2): 1768–1780. doi: 10.1109/JIOT.2018.2882532 AQUINO G P, GUIMARÃES D A, MENDES L L, et al. Combined pre–distortion and censoring for bandwidth–efficient and energy–efficient fusion of spectrum sensing information[J]. Sensors, 2017, 17(3): 654. doi: 10.3390/s17030654 FAN Lisheng, LEI Xianfu, YANG Nan, et al. Secrecy cooperative networks with outdated relay selection over correlated fading channels[J]. IEEE Transactions on Vehicular Technology, 2017, 66(8): 7599–7603. doi: 10.1109/TVT.2017.2669240 KISHORE R, GURUGOPINATH S, MUHAIDAT S, et al. Sensing–throughput tradeoff for superior selective reporting–based spectrum sensing in energy harvesting HCRNs[J]. IEEE Transactions on Cognitive Communications and Networking, 2019, 5(2): 330–341. doi: 10.1109/TCCN.2019.2906915 SANTANA G M D, CRISTO R S, DEZAN C, et al. Cognitive radio for UAV communications: Opportunities and future challenges[C]. 2018 International Conference on Unmanned Aircraft Systems (ICUAS). Dallas, USA, 2018: 760–768. doi: 10.1109/ICUAS.2018.8453329. SBOUI L, GHAZZAI H, REZKI Z, et al. Achievable rates of UAV–relayed cooperative cognitive radio MIMO systems[J]. IEEE Access, 2017, 5: 5190–5204. doi: 10.1109/ACCESS.2017.2695586 ZHENG Yi, WANG Yuwen, and MENG Fanji. Modeling and simulation of pathloss and fading for air–ground link of HAPs within a network simulator[C]. 2013 International Conference on Cyber–Enabled Distributed Computing and Knowledge Discovery. Beijing, China, 2013: 421–426. doi: 10.1109/CyberC.2013.78. AL–HOURANI A, KANDEEPAN S, and LARDNER S. Optimal LAP altitude for maximum coverage[J]. IEEE Wireless Communications Letters, 2014, 3(6): 569–572. doi: 10.1109/lwc.2014.2342736 MOZAFFARI M, SAAD W, BENNIS M, et al. Drone small cells in the clouds: Design, deployment and performance analysis[C]. 2015 IEEE Global Communications Conference (GLOBECOM). San Diego, USA, 2015: 1–6. doi: 10.1109/GLOCOM.2015.7417609. GHAZZAI H, GHORBEL M B, KADRI A, et al. Energy–efficient management of unmanned aerial vehicles for underlay cognitive radio systems[J]. IEEE Transactions on Green Communications and Networking, 2017, 1(4): 434–443. doi: 10.1109/TGCN.2017.2750721 LIANG Yingchang, ZENG Yonghong, PEH E C Y, et al. Sensing–throughput tradeoff for cognitive radio networks[J]. IEEE Transactions on Wireless Communications, 2008, 7(4): 1326–1337. doi: 10.1109/twc.2008.060869 LIU Liang, ZHANG Shuowen, and ZHANG Rui. CoMP in the Sky: UAV placement and movement optimization for multi–user communications[J]. IEEE Transactions on Communications, 2019, 67(8): 5645–5658. doi: 10.1109/TCOMM.2019.2907944 -
图(8) / 表(2)
计量
- 文章访问数: 2157
- HTML全文浏览量: 1059
- PDF下载量: 87
- 被引次数: 0
下载:
