基于傳輸公平性的多無人機(jī)通感一體化空間部署與波束成形設(shè)計
doi: 10.11999/JEIT240590
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哈爾濱工業(yè)大學(xué)電子與信息工程學(xué)院 哈爾濱 150001
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哈爾濱工業(yè)大學(xué)(威海)信息科學(xué)與工程學(xué)院 威海 264209
基金項目: 國家自然科學(xué)基金(62171154, 62171163)，中央高校基本科研業(yè)務(wù)費專項資金(HIT.OCEF.2023030)
Spatial Deployment and Beamforming for Design Multi-Unmanned Aerial Vehicle-integrated Sensing and Communication Based on Transmission Fairness
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School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
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School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China
Funds: The National Natural Science Foundation of China (62171154, 62171163), The Fundamental Research Funds for the Central Universities (HIT.OCEF.2023030)
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摘要: 針對農(nóng)村偏遠(yuǎn)地區(qū)通信不暢的臨時突發(fā)性問題，該文提出一種自適應(yīng)的多無人機(jī)(UAV)輔助通感一體化(ISAC)機(jī)制，在地面用戶和感測目標(biāo)呈簇狀隨機(jī)分布的情況下，通過合理調(diào)度多無人機(jī)實現(xiàn)覆蓋式通信保障，為無人機(jī)使能的通感一體系統(tǒng)提供了一種新的解決思路和方案。該文主要研究了無人機(jī)空間部署及其對地面設(shè)備的波束成形等問題，在空地關(guān)聯(lián)約束條件下，系統(tǒng)可通過優(yōu)化無人機(jī)的通信和感知波束成形變量組，最大限度地提高用戶傳輸可達(dá)速率的下限，同時保證基本的通感需求。為了有效解決所考慮的非凸優(yōu)化問題，該文借助基于高斯核的均值漂移算法(MS)，用以處理關(guān)聯(lián)策略中的混合整型線性問題，此外，結(jié)合2次變換與連續(xù)凸逼近(SCA)的相關(guān)技巧，采用塊坐標(biāo)下降(BCD)的方式優(yōu)化波束成形，以獲取次優(yōu)解。數(shù)值結(jié)果驗證了自適應(yīng)機(jī)制的有效性。Abstract:
Objective: As economic and social development rapidly progresses, the demand for applications across various sectors is increasing. The use of higher frequency bands for future 6G communication is advancing to facilitate enhanced perception. Additionally, due to the inherent similarities in signal processing and hardware configurations between sensing and communication, Integrated Sensing And Communication (ISAC) is becoming a vital area of research for future technological advancements. However, during sudden emergencies, communication coverage and target detection in rural and remote areas with limited infrastructure face considerable challenges. The integration of communication and sensing in Unmanned Aerial Vehicles (UAVs) presents a unique opportunity for equipment flexibility and substantial research potential. Despite this, current academic research primarily focuses on single UAV systems, often prioritizing communication rates while neglecting fairness in multi-user environments. Furthermore, existing literature on multiple UAV systems has not sufficiently addressed the variations in user or target numbers and their random distributions, which impedes the system’s capability to adaptively allocate resources based on actual demands and improve overall efficiency. Therefore, exploring the application of integrated communication and sensing technologies within multi-UAV systems to provide essential services to ground-based random terminals holds significant practical importance. Methods: This paper addresses the scenario in which ground users and sensing targets are randomly distributed within clusters. The primary focus is on the spatial deployment of UAVs and their beamforming techniques tailored for ground-based equipment. The system seeks to enhance the lower bound of user transmission achievable rates by optimizing the communication and sensing beamforming variables of the UAVs, while also adhering to essential communication and sensing requirements. Key constraints considered include the aerial-ground coverage correlation strategy, UAV transmission power, collision avoidance distances, and the spatial deployment locations. To effectively address the non-convex optimization problem, the study divides it into two sub-problems: the joint optimization of aerial-ground correlation and planar position deployment, and the joint optimization of communication and sensing beamforming. The first sub-problem is solved using an improved Mean Shift algorithm (MS), which focuses on optimizing aerial-ground correlation variables alongside UAV planar coordinate variables (Algorithm 1). The second sub-problem employs a quadratic transformation technique to optimize communication beamforming variables (Algorithm 2), further utilizing a successive convex approximation strategy to tackle the optimization challenges associated with sensing beamforming (Algorithm 3). Ultimately, a Block Coordinate Descent algorithm is implemented to alternately optimize the two sets of variables (Algorithm 4), leading to a relatively optimal solution for the system. Results and Discussions: Algorithm 1 focuses on establishing aerial-ground correlations and determining the planar deployment of UAVs. During the clustering phase, users and targets are treated as equivalent sample entities, with ground sample points generated through a Poisson clustering random process. These points are subsequently categorized into nine optimal clusters using an enhanced mean shift algorithm. Samples within the same Voronoi category are assigned to a single UAV, positioned at the mean shift center for optimal service coverage. Algorithm 4 addresses the beamforming requirements for UAVs servicing ground users or targets. Remarkably, multiple UAVs achieve convergence within seven iterations concerning regional convergence. The dynamic interplay between communication and sensing resources is highlighted by variations in the number of sensing targets and the altitude of UAV deployment. The fairness-first approach proposed in this paper, in contrast to a rate-centric strategy, ensures maximum individual transmission quality while maintaining balanced system performance. Furthermore, the overall scheme, referred to as MS+BCD, is compared with two benchmark algorithms: Block Coordinate Descent beamforming optimization with Central point Sensing Deployment (CSD+BCD) and Random Sensing Beamforming with Mean Shift deployment (MS+RSB). The proposed optimization strategy clearly demonstrates advantages in system effectiveness, irrespective of changes in beam pattern gain or increases in UAV antenna numbers. Conclusions: This paper addresses the multi-UAV coverage challenge within the framework of Integrated Sensing and Communication. With a focus on equitable user transmission rates, this study incorporates constraints related to communication and sensing power, beam pattern gain, and aerial-ground correlation. By employing an enhanced Mean Shift algorithm along with the Block Coordinate Descent method, this research optimizes a variety of parameters, including aerial-ground correlation strategies, UAV planar deployment, and communication-sensing beamforming. The objective is to maximize the system’s transmission rate while ensuring high-quality user transmission and fair resource allocation, thereby providing a novel approach for multi-UAV systems enhanced by integrated communication and sensing. Future research will extend these findings to tackle additional altitude optimization challenges and to ensure equitable resource distribution across different UAV coverage zones. -
1 空地關(guān)聯(lián)與空中基站二維笛卡爾坐標(biāo)部署的解決策略
步驟1 初始化地面用戶(目標(biāo))集合，設(shè)定初始迭代次數(shù)為${l_1} = 1$，任選一用戶或目標(biāo)點作為中心點，位置為$ {\boldsymbol{q}}_i^1,i \in \{ 1,2,\cdots,K + J\} $； 步驟2 計算均值漂移向量${\boldsymbol{M}}\left( {{\boldsymbol{q}}_i^{{l_1}}} \right)$，其中半徑$r$設(shè)為${d_{{\text{min}}}}$，注意：同一水平面無人機(jī)滿足防撞約束，則3維環(huán)境下同樣適用； 步驟3 移動密度估計窗口${\boldsymbol{q}}_i^{{l_1} + 1} = {\boldsymbol{q}}_i^{{l_1}} + {\boldsymbol{M}}\left( {{\boldsymbol{q}}_i^{{l_1}}} \right)$； 步驟4 迭代更新${l_1} = {l_1} + 1$； 步驟5 當(dāng)均值漂移向量的模值小于閾值$ \varepsilon _{{\mathrm{th}}}^{\boldsymbol{q}} $或移動窗口內(nèi)用戶(目標(biāo))數(shù)不再增加時，迭代停止，否則重復(fù)步驟2～4； 步驟6 重新選定未被覆蓋的隨機(jī)點作為中心點，繼續(xù)執(zhí)行步驟2～5，直至所有用戶(目標(biāo))點都被分配到對應(yīng)聚類中心； 步驟7 構(gòu)建Voronoi圖和相應(yīng)的凸包區(qū)域； 步驟8 計算凸包區(qū)域范圍內(nèi)的所有用戶(目標(biāo))的位置均值； 步驟9 輸出所有聚類中心坐標(biāo)和對應(yīng)坐標(biāo)下的聚類用戶(目標(biāo))。 
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2 無人機(jī)通信波束成形子算法
步驟1 初始化通信波束成形${\boldsymbol{W}}_{b,k}^{\left( 0 \right)}$，設(shè)定初始迭代次數(shù)為
${l_2} = 1$, $b = 1$；步驟2 給定${\boldsymbol{W}}_{b,k}^{\left( {{l_2} - 1} \right)}$，利用式 (18) 更新輔助變量$\delta _{b,k}^{\left( {{l_2}} \right)}$； 步驟3 給定${\boldsymbol{W}}_{b,k}^{\left( {{l_2} - 1} \right)}$和$\delta _{b,k}^{\left( {{l_2}} \right)}$，求解問題P3.1.1，獲得${\boldsymbol{W}}_{b,k}^{\left( {{l_2}} \right)}$，
迭代更新${l_2} = {l_2} + 1$；步驟4 當(dāng)信息傳輸速率增量小于閾值$\varepsilon _{{\text{th}}}^{\boldsymbol{V}}$時，迭代停止，否則重
復(fù)步驟2～3；步驟5 無人機(jī)選擇切換$b = b + 1$，繼續(xù)執(zhí)行步驟2～4，直至所
有無人機(jī)的通信波束成形均已優(yōu)化；步驟6 輸出所有無人機(jī)通信波束成形矩陣${{\boldsymbol{W}}_{b,k}}$。
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3 無人機(jī)感知波束成形子算法
步驟1 初始化感知波束成形${\boldsymbol{V}}_{b,j}^{\left( 0 \right)}$，設(shè)定初始迭代次數(shù)為
${l_3} = 1$, $b = 1$；步驟2 給定${\boldsymbol{V}}_{b,j}^{\left( {{l_3} - 1} \right)}$，求解凸優(yōu)化問題P3.2.1，獲取${\boldsymbol{V}}_{b,j}^*$，令 ${\boldsymbol{V}}_{b,j}^{\left( {{l_3}} \right)} = {\boldsymbol{V}}_{b,j}^*$，迭代更新${l_3} = {l_3} + 1$； 步驟3 當(dāng)信息傳輸速率增量小于閾值$\varepsilon _{{\text{th}}}^{\boldsymbol{V}}$時，迭代停止，否則重 復(fù)步驟2； 步驟4 無人機(jī)選擇切換$b = b + 1$，繼續(xù)執(zhí)行步驟2～3，直至所 有無人機(jī)的感知波束成形均已優(yōu)化； 步驟5 輸出所有無人機(jī)感知波束成形矩陣$ {{\boldsymbol{V}}_{b,j}} $。
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4 無人機(jī)波束成形總體算法
步驟1 初始化通信和感知波束成形${\boldsymbol{W}}_{b,k}^{\left( 0 \right)}$, ${\boldsymbol{V}}_{b,j}^{\left( 0 \right)}$，設(shè)定初始迭
代次數(shù)為${l_4} = 1$, $b = 1$；步驟2 給定${\boldsymbol{W}}_{b,k}^{\left( {{l_4} - 1} \right)}$,${\boldsymbol{V}}_{b,j}^{\left( {{l_4} - 1} \right)}$，通過算法2求解通信波束成形子
問題，獲得${\boldsymbol{W}}_{b,k}^{\left( {{l_4}} \right)}$；步驟3 給定${\boldsymbol{W}}_{b,k}^{\left( {{l_4}} \right)}$,${\boldsymbol{V}}_{b,j}^{\left( {{l_4} - 1} \right)}$，通過算法3求解感知波束成形子
問題，獲得${\boldsymbol{V}}_{b,j}^{\left( {{l_4}} \right)}$；步驟4 迭代更新${l_4} = {l_4} + 1$，當(dāng)信息傳輸速率增量小于閾值
$\varepsilon _{{\mathrm{th}}}^{{\mathrm{all}}}$時，迭代停止，否則重復(fù)步驟2～3；步驟5 無人機(jī)選擇切換$b = b + 1$，繼續(xù)執(zhí)行步驟2～4，直至所
有無人機(jī)的波束成形均已優(yōu)化；步驟6 輸出所有無人機(jī)波束成形矩陣${{\boldsymbol{W}}_{b,k}}$, $ {{\boldsymbol{V}}_{b,j}} $。
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表 1 主要仿真參數(shù)
參數(shù)名稱 數(shù)值 單位距離的信道功率增益${\beta _0}$ –60 dB 噪聲功率${\sigma ^2}$ –110 dBm 無人機(jī)防撞距離${d_{{\text{min}}}}$ 0.3 km 單無人機(jī)發(fā)射功率上限${P_{{\text{max}}}}$ 0.5 W 波束圖增益下限${\varGamma _{{\text{th}}}}$ –17 dBm 迭代收斂閾值$ \varepsilon _{{\text{th}}}^{\boldsymbol{q}},\varepsilon _{{\text{th}}}^{\boldsymbol{W}},\varepsilon _{{\text{th}}}^{\boldsymbol{V}},\varepsilon _{{\text{th}}}^{{\text{all}}} $ 1 × 10–4
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[1] CUI Yanpeng, FENG Zhiyong, ZHANG Qixun, et al. Toward trusted and swift UAV communication: ISAC-enabled dual identity mapping[J]. IEEE Wireless Communications, 2023, 30(1): 58–66. doi: 10.1109/MWC.003.2200207. [2] MU Junsheng, ZHANG Ronghui, CUI Yuanhao, et al. UAV meets integrated sensing and communication: Challenges and future directions[J]. IEEE Communications Magazine, 2023, 61(5): 62–67. doi: 10.1109/MCOM.008.2200510. [3] DENG Cailian, FANG Xuming, and WANG Xianbin. Beamforming design and trajectory optimization for UAV-empowered adaptable integrated sensing and communication[J]. IEEE Transactions on Wireless Communications, 2023, 22(11): 8512–8526. doi: 10.1109/TWC.2023.3264523. [4] MENG Kaitao, WU Qingqing, MA Shaodan, et al. Throughput maximization for UAV-enabled integrated periodic sensing and communication[J]. IEEE Transactions on Wireless Communications, 2023, 22(1): 671–687. doi: 10.1109/TWC.2022.3197623. [5] SAVKIN A V, NI Wei, and ESKANDARI M. Effective UAV navigation for cellular-assisted radio sensing, imaging, and tracking[J]. IEEE Transactions on Vehicular Technology, 2023, 72(10): 13729–13733. doi: 10.1109/TVT.2023.3277426. [6] WU Jun, YUAN Weijie, and HANZO L. When UAVs meet ISAC: Real-time trajectory design for secure communications[J]. IEEE Transactions on Vehicular Technology, 2023, 72(12): 16766–16771. doi: 10.1109/TVT.2023.3290033. [7] WANG Qianli and FAN Pingzhi. A multi-symbol compressive sensing model for OTFS based ISAC system[C]. 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023: 2487–2492. doi: 10.1109/GLOBECOM54140.2023.10437965. [8] LIU Qian, LIANG Hairong, LUO Rui, et al. Energy-efficiency computation offloading strategy in UAV aided V2X network with integrated sensing and communication[J]. IEEE Open Journal of the Communications Society, 2022, 3: 1337–1346. doi: 10.1109/OJCOMS.2022.3195703. [9] WU Jun, YUAN Weijie, and BAI Lin. Multi-UAV enabled sensing: Cramér-Rao bound optimization[C]. 2023 IEEE International Conference on Communications Workshops (ICC Workshops), Rome, Italy, 2023: 925–930. doi: 10.1109/ICCWorkshops57953.2023.10283770. [10] LIU Xin, LIU Yuemin, LIU Zechen, et al. Fair integrated sensing and communication for multi-UAV-enabled internet of things: Joint 3-D trajectory and resource optimization[J]. IEEE Internet of Things Journal, 2024, 11(18): 29546–29556. doi: 10.1109/JIOT.2023.3327445. [11] JIANG Wangjun, WANG Ailing, WEI Zhiqing, et al. Improve sensing and communication performance of UAV via integrated sensing and communication[C]. 2021 IEEE 21st International Conference on Communication Technology (ICCT), Tianjin, China, 2021: 644–648, doi: 10.1109/ICCT52962.2021.9657955. [12] PAN Yu, LI Ruoguang, DA Xinyu, et al. Cooperative trajectory planning and resource allocation for UAV-enabled integrated sensing and communication systems[J]. IEEE Transactions on Vehicular Technology, 2024, 73(5): 6502–6516. doi: 10.1109/TVT.2023.3337106. [13] CHENG Yizong. Mean shift, mode seeking, and clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790–799. doi: 10.1109/34.400568. [14] SHEN Kaiming and YU Wei. Fractional programming for communication systems—Part I: Power control and beamforming[J]. IEEE Transactions on Signal Processing, 2018, 66(10): 2616–2630. doi: 10.1109/TSP.2018.2812733. [15] XU Dongfang, YU Xianghao, SUN Yan, et al. Resource allocation for IRS-assisted full-duplex cognitive radio systems[J]. IEEE Transactions on Communications, 2020, 68(12): 7376–7394. doi: 10.1109/TCOMM.2020.3020838. -
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