IRS輔助的感知與隱蔽通信一體化資源分配算法
doi: 10.11999/JEIT240643
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安徽農(nóng)業(yè)大學(xué)信息與人工智能學(xué)院 合肥 230036
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海南大學(xué)信息與通信工程學(xué)院 ?？? 570228
基金項(xiàng)目: 國家自然科學(xué)基金(62371004, 62301005)，安徽省高校協(xié)同創(chuàng)新項(xiàng)目(GXXT-2022-055)
Resource Allocation Algorithm for Intelligent Reflecting Surface-assisted Integrated Sensing and Covert Communication
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School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
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School of Information and Communication Engineering, Hainan University, Haikou 570228, China
Funds: The National Natural Science Foundation of China (62371004, 62301005), The University Synergy Innovation Program of Anhui Province (GXXT-2022-055)
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摘要: 為了解決感知與通信一體化(ISAC)系統(tǒng)中的信息安全傳輸問題，該文研究智能反射面(IRS)輔助的感知與隱蔽通信一體化(ISACC)系統(tǒng)中的資源分配算法。首先，分析監(jiān)測(cè)者Willie的最優(yōu)檢測(cè)性能，并推導(dǎo)了其最小檢測(cè)錯(cuò)誤概率的下界表達(dá)式。隨后，推導(dǎo)目標(biāo)估計(jì)的平均克拉美羅下界(CRLB)的解析表達(dá)式。在此基礎(chǔ)上，構(gòu)建以最小化平均CRLB為目標(biāo)函數(shù)，以隱蔽需求、通信速率需求、IRS相移等為約束的優(yōu)化問題。提出基于交替優(yōu)化(AO)的懲罰連續(xù)凸近似(PSCA)的算法聯(lián)合設(shè)計(jì)了感知信號(hào)協(xié)方差矩陣、通信信號(hào)波束成形以及IRS相移。仿真結(jié)果表明，所提IRS輔助的ISACC系統(tǒng)方案可以較好地均衡目標(biāo)感知性能和隱蔽無線通信性能。Abstract:
Objective Integrated Sensing and Communication (ISAC) systems are considered key technologies for the upcoming 6G networks, offering a unified platform for wireless communication and environmental sensing. To enhance the security of ISAC systems, an Integrated Sensing and Covert Communication (ISACC) system is proposed. Additionally, an Intelligent Reflecting Surface (IRS)-assisted ISACC scheme is proposed to address the limitation of existing ISACC research, which cannot be applied to scenarios without a Line-of-Sight (LoS) link between the Base Station (BS) and the target. In this context, the average Cramér-Rao Lower Bound (CRLB) is adopted as a metric for sensing performance, aiming to overcome the limitations of traditional beampatterns in quantifying sensing performance directly. Methods The detection performance at warden Willie is first analyzed. An analytical expression for the average CRLB is then derived. Based on this, an optimization problem is formulated to minimize the average CRLB, subject to communication rate, covertness, and IRS phase shift constraints. The optimization problem is challenging to solve directly due to the coupling of the sensing covariance matrix, communication beamforming, and IRS reflective beamforming in the objective function, communication rate constraint, and covertness constraint. To tackle this, the optimization problem is decomposed into two subproblems: one for the sensing covariance matrix and communication beamforming optimization, and another for the IRS reflection beamforming optimization. An Alternating Optimization-based Penalty Successive Convex Approximation (AO-PSCA) algorithm is proposed to solve the two subproblems iteratively. Results and Discussions The relationship between the average CRLB, the number of IRS reflection elements, and the number of BS antennas is presented ( Fig. 2 ). As observed, the average CRLB obtained by the AO-PSCA algorithm and the IRS random phase algorithm decreases as the number of IRS elements increases. This is because a larger number of IRS elements not only enhances covert communication performance but also improves the quality of the virtual link between the BS and the sensing target. Additionally, the proposed AO-PSCA algorithm outperforms the IRS random phase scheme, highlighting the importance of designing IRS reflection coefficients. Furthermore, as the number of BS antennas increases, the average CRLB decreases, since more antennas simultaneously improve both target sensing and covert communication performance. The relationship between the average CRLB, covertness threshold, and communication rate threshold is shown (Fig. 3 ). It can be seen that the average CRLB decreases as the covertness parameter$\varepsilon $increases. This indicates that increasing the covertness parameter improves the sensing performance of the ISACC system improves with$\varepsilon $. The reason is that a larger covertness value of$\varepsilon $makes it easier to satisfy the covertness constraints, thereby allowing more resources for communication and sensing. In contrast, the average CRLB increases with the communication rate requirement, as a larger value of$ \varGamma $requires more system resources, leaving fewer resources for radar sensing. The relationships between the average CRLB, average maximum transmit power, and symbol length, as well as between average maximum transmit power, communication signal power, and sensing signal power, are presented (Fig. 4 ). It can be observed that the average CRLB decreases as the average maximum transmit power increases. This is due to the increase in both sensing and communication signal powers with higher transmit power. The average CRLB also decreases as the symbol length increases, as a larger symbol length improves target sensing performance. The relationship between the beampattern, angle, and average maximum transmit power is illustrated (Fig. 5 ). The beampatterns are focused on their main lobe, with the sensing target located at 0°. Due to communication rate and covertness constraints, random fluctuations appear in the side lobe regions of the beampatterns. Moreover, the beampattern values increase with the average maximum transmit power, indicating that increasing transmit power effectively enhances both target sensing and covert communication performance.Conclusions The IRS-assisted ISACC system is investigated in this work. An optimization problem is formulated to minimize the average CRLB, subject to constraints on covertness, maximum transmit power, communication rate, and IRS phase shifts. The AO-PSCA algorithm is proposed for the joint design of the sensing covariance matrix, communication beamforming, and IRS phase shifts. Simulation results demonstrate that the proposed ISACC scheme, assisted by IRS, can effectively balance target sensing and covert wireless communication performance. -
1 基于AO的PSCA算法
(1) 交替迭代索引${r_0} = 0$，初始化IRS相移向量${{\boldsymbol{v}}^{{r_0}}}$ (2) 重復(fù)執(zhí)行，初始化索引${r_1} = 0$，給定初始可行解${\tilde {\boldsymbol W}_{\rm c}}^{{r_1}}$，懲罰參數(shù)${\tau }_0^{{r_1}}$，給定${c_0} > 1$，${\tau _{0,\max}}$。 (a)在給定的IRS相移向量${{\boldsymbol{v}}^{{r_0}}}$下，求解優(yōu)化問題(34)，得到優(yōu)化問題的解${ {\boldsymbol W}_{\rm c}}^{{r_1} + 1}$，${{\boldsymbol{R}}}_0^{{r_1} + 1}$和${\eta _0}^{{r_1} + 1}$。 (b)更新$ {\tau _0}^{{r_1} + 1} = \min({c_0}{\tau _0}^{{r_1}},{\tau _{0,\max}}) $，${\tilde {\boldsymbol W}_{\rm c}}^{{r_1} + 1} = {{\boldsymbol W}_{\rm c}}^{{r_1} + 1}$，${r_1} = {r_1} + 1$。 (c)直至收斂，得到該優(yōu)化問題解${{\boldsymbol{R}}}_0^{{r_0}}$和秩1解${{\boldsymbol W}_{\rm c}}^{{r_0}}$。 (3) 重復(fù)執(zhí)行，初始化索引${r_2} = 0$，給定初始可行解$ {\tilde {\boldsymbol{V}}^{{r_2}}} $，$ {\tilde u_1}^{{r_2}} $，$ {\tilde u_2}^{{r_2}} $和$ {\tilde u_3}^{{r_2}} $，懲罰參數(shù)${\tau}_1^{{r_2}}$，給定${c_1} > 1$，${\tau _{1,\max}}$。 (a)使用$ {\tilde {\boldsymbol{V}}^{{r_2}}} $, $ {\tilde u_1}^{{r_2}} $, $ {\tilde u_2}^{{r_2}} $和$ {\tilde u_3}^{{r_2}} $構(gòu)造$ {{\mathop f\limits^ \vee} _{1,i}}({\boldsymbol{V}},{u_1},{u_{i + 1}}) $，$ i = 1,2 $。 (b)在步驟(2)得到的${\boldsymbol W}_{\rm c}^{{r_0}}$和${\boldsymbol{R}}_0^{{r_0}}$下，求解優(yōu)化問題(45)，得到優(yōu)化問題的解$ {{\boldsymbol{V}}^{{r_2} + 1}} $, $ u_1^{{r_2} + 1} $, $ u_2^{{r_2} + 1} $, $ u_3^{{r_2} + 1} $和$\eta _1^{{r_2} + 1}$。 (c)更新$ \tau _1^{{r_2} + 1} = \min({c_1}\tau _1^{{r_2}},{\tau _{1,\max}}) $, $ {\tilde {\boldsymbol{V}}^{{r_2} + 1}} = {{\boldsymbol{V}}^{{r_2} + 1}} $，$ {\tilde u_1}^{{r_2} + 1} = u_1^{{r_2} + 1} $, $ {\tilde u_2}^{{r_2} + 1} = u_2^{{r_2} + 1} $, $ {\tilde u_3}^{{r_2} + 1} = u_3^{{r_2} + 1} $, ${r_2} = {r_2} + 1$。 (d)直至收斂，得到該優(yōu)化問題秩1解$ {{\boldsymbol{V}}^{{r_0}}} $，可以直接通過分解$ {{\boldsymbol{V}}^{{r_0}}} $得到${{\boldsymbol{v}}^{{r_0}}}$。 (4) 更新${r_0} = {r_0} + 1$。 (5) 直至收斂。 
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