低軌衛(wèi)星通信系統(tǒng)跳波束圖案設(shè)計(jì)算法
doi: 10.11999/JEIT240596 cstr: 32379.14.JEIT240596
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1.
北京科技大學(xué) 北京 100083
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2.
國(guó)家無(wú)線(xiàn)電監(jiān)測(cè)中心檢測(cè)中心 北京 100041
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3.
錢(qián)學(xué)森空間技術(shù)實(shí)驗(yàn)室 北京 100029
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4.
北京郵電大學(xué) 北京 100876
基金項(xiàng)目: 國(guó)家重點(diǎn)研發(fā)計(jì)劃(2020YFB1807900)
The Beam Hopping Pattern Design Algorithm of Low Earth Orbit Satellite Communication System
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1.
University of Science and Technology Beijing, Beijing 100083, China
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2.
The State Radio_monitoring_center Testing Center, Beijing 100041, China
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3.
Qian Xuesen Space Technology Laboratory, Beijing 100029, China
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4.
Beijing University of Posts and Telecommunications, Beijing 100876, China
Funds: The National Key Research and Development Program of China (2020YFB1807900)
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摘要: 低軌衛(wèi)星資源調(diào)度是長(zhǎng)時(shí)間的連續(xù)資源分配過(guò)程，這一過(guò)程中低軌衛(wèi)星保持高速移動(dòng)，跳波束圖案的設(shè)計(jì)需要考慮星地鏈路的切換。針對(duì)這種切換，即衛(wèi)星覆蓋區(qū)域間的服務(wù)目標(biāo)遷移，所導(dǎo)致的多星資源聯(lián)合調(diào)度需求，該文提出一種資源自適應(yīng)權(quán)衡分配的多星聯(lián)合跳波束圖案設(shè)計(jì)算法。該算法通過(guò)設(shè)計(jì)星間聯(lián)合調(diào)度框架和多星聯(lián)合調(diào)度權(quán)重，將多星資源聯(lián)合分配問(wèn)題轉(zhuǎn)化為星座內(nèi)單星資源調(diào)度問(wèn)題，輕量化設(shè)計(jì)跳波束圖案。經(jīng)過(guò)與多種權(quán)重設(shè)計(jì)方法的對(duì)比驗(yàn)證，仿真結(jié)果表明，所提算法的輕量化設(shè)計(jì)思路合理，并且可以有效地保障受遷移影響區(qū)域內(nèi)小區(qū)的服務(wù)質(zhì)量，可為低軌衛(wèi)星系統(tǒng)長(zhǎng)時(shí)資源調(diào)度設(shè)計(jì)提供參考。
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關(guān)鍵詞:
- 低軌衛(wèi)星通信系統(tǒng) /
- 跳波束 /
- 資源分配策略
Abstract:Objective The resource scheduling in Low Earth Orbit (LEO) satellite communication systems using Beam Hopping (BH) technology is a continuous, long-term allocation process. Unlike geostationary earth orbit (GEO) satellites, LEO satellites exhibit high-speed mobility relative to the ground during communication. The design of BH patterns typically occurs within regular time windows, ranging from tens to hundreds of milliseconds, leading to the switching of satellite-to-cell interaction links during certain BH periods. This switching implies that cells migrate between satellite coverage areas, each with varying capacity and delay requirements, which inevitably affects the performance of the receiving satellite. Additionally, the requirements of migrating cells during the switching time slot are directly related to the resource tilt provided by the source satellite before the switch. Therefore, there is a strong correlation between the BH pattern design strategies for different satellites, requiring multi-satellite joint resource scheduling to maintain service quality of cells in regions affected by migration. Methods In order to characterize the demands of joint scheduling for multiple satellites and maximize the minimum traffic satisfaction rate, an optimization problem is proposed for dynamic scenarios involving satellite-to-cell interaction link switching. This optimization problem simultaneously considers co-channel interference, traffic demands with differentiated temporal and spatial distributions, and traffic delay—all factors that affect the service quality of BH systems. To solve this NP-hard problem, a design algorithm of Multi-Satellite Joint BH Pattern based on Resource Adaptive Tradeoff Allocation (RATMJ-BHP) is proposed. First, an inter-satellite joint scheduling framework is proposed to model the complex impact of cell migration on satellite resource scheduling, transforming the multi-satellite scheduling problem into a single-satellite BH pattern design problem. Then, within this framework, a weight design method for multi-satellite joint scheduling is proposed, which quantifies the intensity of service urgency based on the capacity and delay requirements of cells. Finally, this joint scheduling weight is used to design the BH pattern. Results and Discussions Based on the optimization problem modeled in this paper, the satellite optimization region is divided into two areas: the stable region and the immigration region. A comprehensive evaluation, considering both regions within individual satellites and across adjacent satellites, is essential for analyzing the performance of the proposed algorithm. Thus, this paper examines the simulation results from two perspectives: the minimum traffic satisfaction rate and the variation in the minimum traffic satisfaction rate across different regions. Additionally, convergence speed is a key indicator of the algorithm’s performance; therefore, the number of iterations required to produce results for each time slot is counted. The key contributions of this research are as follows: Firstly, the average and maximum convergence times of the proposed algorithm are significantly lower than those of the enumeration method, demonstrating its efficiency in terms of time complexity ( Table 3 ). Specifically, with three satellites, the maximum complexity value of the proposed algorithm is 39.05, compared to that for the enumeration method. Secondly, the proposed algorithm outperforms the comparison algorithms in terms of minimum traffic satisfaction rates under different load rates, with a minimum value above 69.34% across various satellites (Fig. 3a ) (Fig. 4a ) (Fig. 5a ). These results show that the RATMJ-BHP algorithm effectively ensures high traffic satisfaction rates for cells in affected regions, demonstrating robustness across different traffic demand rates. Thirdly, the proposed algorithm exhibits a smaller disparity in minimum traffic satisfaction rates across regions, with values remaining close to zero, unlike other algorithms. This indicates its ability to maintain high traffic satisfaction rates for most cells in service areas (Fig. 3b ) (Fig. 4b ) (Fig. 5b ). Finally, simulation results from both perspectives demonstrate consistent performance across different satellites and varying traffic demand rates, highlighting the general applicability of the proposed algorithm in LEO satellite BH systems.Conclusions This paper addresses the design of BH patterns for dynamic scenarios involving satellite-to-cell interaction link switching. To meet the demands of multi-satellite joint resource scheduling in such scenarios, while considering performance factors such as co-channel interference, traffic demands, and traffic delay, the RATMJ-BHP algorithm is proposed. Simulation results show that the proposed algorithm effectively ensures the service quality of cells in migration-affected areas, and its lightweight design demonstrates broad applicability within LEO constellations. This paper contributes to the design strategy of BH patterns in dynamic scenarios during long-term resource scheduling processes, offering a solution to maintain continuous high-quality service to cells throughout prolonged satellite motion. It provides a reference for the design of long-term beam scheduling strategies in LEO satellite BH systems. However, several challenges remain in resource scheduling strategies for LEO satellite BH systems. For instance, the relationship between resource scheduling across BH periods and its impact on long-term system performance has yet to be fully explored. Additionally, while the proposed algorithm focuses on resource scheduling for the forward link of LEO satellite systems, further research is needed for uplink scenarios. -
1 RATMJ-BHP算法
1 輸入：$ {G_{\rm{th}}} $ 2 初始化：$ \forall s \in \mathcal{S},{{\boldsymbol{X}}_s} = \varnothing $,$ \forall k \in (1,K),{{\mathcal{N}}}_{{\mathrm{sort}}}^{(k)} = \varnothing $ 3 For $ t = 1,2, \cdots ,T $ 4 If $ \forall s \in \mathcal{S},{\text{ }}\exists {t^{'}} \in \iota _s^{({\mathrm{in}})},{\text{ }}{\mathrm{s.t}}.{\text{ }}{t^{'}} = = t{\text{ or }}\exists {t^{''}} \in \iota _s^{({\mathrm{out}})} $,
${\mathrm{s.t.}}{\text{ }}{t^{''}} = = t{\text{ }} $5 $ {{\mathcal{N}}}_s^{(t)} = {{\mathcal{N}}}_s^{(t)} + {{\mathcal{N}}}_s^{({t^{'}})} - {{\mathcal{N}}}_s^{({t^{''}})} $, $ {{\mathcal{N}}}_{{\mathrm{set}}}^{(s)} = {{\mathcal{N}}}_s^{(t)} $ 6 根據(jù)式(11)和式(12)更新$ \sigma _{s,t}^{({\mathrm{out}})} $, $ \sigma _{s,t}^{({\mathrm{in}})} $ 7 End If 8 $ \forall {n_s} \in {{\mathcal{N}}}_s^{(t)} $，根據(jù)式(20)和式(21)計(jì)算$ \overline \beta _{{n_s}}^{(t)} $，式(23)和
式(24)計(jì)算$ \overline D _{{n_s}}^{(t)} $9 For $ k = 1,2, \cdots ,K $ 10 For $ s = 1,2, \cdots ,S $ 11 If $ D_{{\mathrm{now}},{n_s}}^{(t)} > R_{{\mathrm{unit}}}^{({n_s})} $ 12 依據(jù)策略(3)和(4)計(jì)算$ \varpi _{{n_s}}^{(t)} $，選擇候選服務(wù)小區(qū)$ n_s^{'} $ 13 $ n_s^{'} \leftarrow {{\mathcal{N}}}_{{\mathrm{set}}}^{(s)} $；$ {{\mathcal{N}}}_{{\mathrm{sort}}}^{(k)} \leftarrow n_s^{'} $ 14 Else 15 依據(jù)策略(1)和(2)計(jì)算$ \varpi _{{n_s}}^{(t)} $，并選擇候選服務(wù)小區(qū)$ n_s^{'} $ 16 $ n_s^{'} \leftarrow {{\mathcal{N}}}_{{\mathrm{set}}}^{(s)} $；$ {{\mathcal{N}}}_{{\mathrm{sort}}}^{(k)} \leftarrow n_s^{'} $ 17 End If 18 End For 19 依據(jù)4種策略對(duì)$ {{\mathcal{N}}}_{{\mathrm{sort}}}^{(k)} $中的小區(qū)排序 20 While $ {{\mathcal{N}}}_{{\mathrm{sort}}}^{(k)} \ne \varnothing $ 21 選擇$ {{\mathcal{N}}}_{{\mathrm{sort}}}^{(k)} $中的第一個(gè)小區(qū)$ n_s^{'} $。 22 If $ \exists {n_{{s_1}}},\max \left\{ {G_{k_s^{'},{k_{{s_1}}}}^{({\rm{tx}})},G_{{k_{{s_1}}},k_s^{'}}^{({\rm{tx}})}} \right\} \ge {G_{\rm{th}}} $,
$ {s_1} \in \mathcal{S},{n_{{s_1}}} \in {{\boldsymbol{X}}_{{s_1}}},{k_s} \ne {k_{{s_1}}} $23 $ n_s^{'} \leftarrow {{\mathcal{N}}}_{{\mathrm{sort}}}^{(k)} $ 24 對(duì)于衛(wèi)星$ s $，轉(zhuǎn)至步驟11， 25 Else 26 $ x_{k_s^{'}}^{(t)} = n_s^{'} $ 27 $ n_s^{'} \leftarrow {{\mathcal{N}}}_{{\mathrm{sort}}}^{(k)} $ 28 End If 29 End While 30 End For 31 End For 32 輸出跳波束圖案$ {{\boldsymbol{X}}_1},{{\boldsymbol{X}}_2}, \cdots ,{{\boldsymbol{X}}_s},s \in \mathcal{S} $ 
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表 1 仿真參數(shù)
參數(shù) 值 衛(wèi)星數(shù)目 3 高度(km) 508 衛(wèi)星初始經(jīng)度(°) [–3.81, 0.65, 5.55] 衛(wèi)星初始緯度(°) [26.45, 31.01, 35.39] 初始小區(qū)數(shù)目 [38, 37, 40] 衛(wèi)星遷出、遷入小區(qū)數(shù)目 [3, 4, 4, 4, 4, 3] 載波頻率(MHz) 1 990 帶寬(MHz) 40 星上總功率(dBW) 14 接收機(jī)天線(xiàn)模型 全向天線(xiàn) 極化方式 圓極化 業(yè)務(wù)包大小(MHz) 2 波束數(shù)目 8 跳波束時(shí)隙長(zhǎng)度(ms) 30 跳波束周期長(zhǎng)度(時(shí)隙) 35 時(shí)延門(mén)限(時(shí)隙) 5 干擾增益門(mén)限(dBi) 10
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表 2 相控陣天線(xiàn)參數(shù)
參數(shù) 參數(shù)值 最大陣元增益(dBi) 5 陣元水平方向3 dB波束寬度(°) 65 陣元垂直方向3 dB波束寬度(°) 65 前后比(dB) 30 陣元水平方向間隔 0.5 陣元垂直方向間隔 0.5 水平方向陣元數(shù)目 32 垂直方向陣元數(shù)目 32
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表 3 迭代次數(shù)
衛(wèi)星數(shù)目 1 2 3 平均迭代次數(shù) 28.1 32.29 36.95 最大迭代次數(shù) 31.58 35.71 39.05 枚舉法 $ {\mathrm{C}}_{{\text{38}}}^{\text{8}} $ $ {\mathrm{C}}_{{\text{38}}}^{\text{8}} \cdot {\mathrm{C}}_{{\text{37}}}^{\text{8}} $ $ {\mathrm{C}}_{{\text{38}}}^{\text{8}} \cdot {\mathrm{C}}_{{\text{37}}}^{\text{8}} \cdot {\mathrm{C}}_{{\text{40}}}^{\text{8}} $
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