Rotatable-Antenna-Aided Near-Field Wideband Integrated Sensing and Communication Systems: Hybrid Beamforming Design
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摘要: 针对通信感知一体化(ISAC)系统在近场宽带场景下存在的近场效应与波束分裂挑战，该文提出一种可旋转天线(RA)辅助的系统架构。通过引入天线视轴可调的旋转自由度并结合真时延器(TTD)全连接混合波束赋形结构，构建了以最大化系统和速率为目标的联合优化模型。针对模型的高度非凸与强耦合特性，提出一种基于罚函数的全数字逼近(PBFDA)优化算法，通过将原问题分解为三个子问题并采用交替迭代方式进行求解：第一个子问题利用粒子群(PSO)方法优化天线指向；第二个子问题结合降维技术与连续凸逼近(SCA)方法求解最优全数字波束赋形器；第三个子问题运用流形方法与块坐标下降(BCD)法协同优化混合波束赋形参数。仿真结果表明，该方案能在保证感知性能的前提下显著提升系统和速率，性能优于传统固定天线架构且接近全数字方案。该研究验证了RA辅助架构在近场宽带ISAC场景中的有效性，为系统能量聚焦与频率鲁棒性提升提供了新思路。
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关键词:
- 通信感知一体化(ISAC) /
- 可旋转天线(RA) /
- 真时延器(TTD) /
- 混合波束赋形 /
- 近场通信
Abstract:Objective With the rapid evolution of sixth-generation (6G) mobile communication systems, integrated sensing and communication (ISAC) has emerged as a key enabling paradigm for simultaneously supporting high-precision sensing and high-rate data transmission under limited spectrum resources. In near-field wideband scenarios, however, ISAC systems suffer from several fundamental challenges, including pronounced near-field effects, and wideband beam splitting. These impairments significantly degrade both communication throughput and sensing reliability, especially when conventional fixed-orientation antenna arrays and phase-shifter-based beamforming architectures are employed. Due to their limited spatial adaptability and inherent frequency-independent characteristics, traditional architectures are unable to fully exploit the spatial–frequency degrees of freedom available in near-field wideband channels. Therefore, it is of great importance to develop a new antenna architecture and beamforming framework that can effectively mitigate beam splitting, enhance energy focusing capability, and maintain robustness across wide bandwidths. To address these challenges, a rotatable-antenna-assisted near-field wideband ISAC architecture is investigated, aiming to improve system sum-rate performance while satisfying sensing-related constraints. Methods A novel near-field wideband ISAC system architecture assisted by rotatable antennas (RAs) is proposed. By introducing mechanically or electronically adjustable antenna boresight directions, additional angular degrees of freedom are provided at the antenna element level, enabling flexible spatial coverage and adaptive energy focusing. Furthermore, a TTD-based hybrid beamforming architecture is adopted, which provides frequency-dependent phase shifts in the frequency domain to compensate for the frequency-independent characteristics of conventional phase shifters, thereby ensuring consistent beam focusing across all subcarriers and effectively suppressing wideband beam splitting. Based on a spherical-wave near-field channel model that explicitly incorporates propagation distance, angular information, and the orientation gain of rotatable antennas—thereby allowing the array response to depend jointly on both angle and distance and overcoming the limitations of the planar-wave assumption—a joint optimization problem is formulated to maximize the system sum rate, while simultaneously considering transmit power constraints, sensing power thresholds, and physical limitations on antenna rotation angles. To address the formulated non-convex optimization problem, a penalty-based fully digital approximation (PBFDA) algorithm is developed. In each iteration, the orientations of the rotatable antennas are first optimized using a particle swarm optimization (PSO) method to enhance the weighted channel gain. Then, with the antenna orientations fixed, a reduced-dimensional formulation combined with successive convex approximation (SCA) is employed to solve the fully digital beamforming problem. Finally, a block coordinate descent (BCD) algorithm based on manifold optimization is adopted to jointly optimize the analog beamformer, digital beamformer, and TTD units, thereby progressively approximating the fully digital solution, with the three components iteratively updated until convergence is achieved (Algorithm 1–Algorithm 4). Results and Discussions Simulation results demonstrate the effectiveness and superiority of the proposed RA-assisted near-field wideband ISAC framework. The convergence behavior of the proposed penalty-based fully digital approximation (PBFDA) optimization algorithm indicates that the objective function monotonically increases and stabilizes within a limited number of iterations, confirming its numerical stability and efficiency ( Fig. 2 ). Compared with conventional fixed-antenna architectures, the proposed RA-based scheme achieves a substantial improvement in system sum rate under the same transmit power constraints (Fig. 3 ). Furthermore, the impact of system bandwidth on spectral efficiency is investigated. As the system bandwidth increases, TTD-based hybrid beamforming schemes experience weakened frequency-dependent compensation capability due to the limited number of TTD units and the constrained maximum delay, which exacerbates wideband beam splitting and leads to a degradation in spectral efficiency. In contrast, the optimal fully digital beamforming approach enables accurate control over each subcarrier, rendering its spectral efficiency basically not varying with bandwidth (Fig. 4 ). The trade-off between communication performance and sensing power is also evaluated. As the sensing power threshold increases, the achievable sum rate decreases for all schemes, while the proposed method consistently outperforms the others (Fig. 5 ). The effects of antenna array size, antenna directivity factor, and maximum rotation angle are further investigated. Increasing the number of antennas improves spectral efficiency due to higher array gain, with the RA-based system consistently outperforming benchmark schemes (Fig. 6 ). As the antenna directivity factor increases, the RA system leverages adaptive orientation to focus energy toward desired users, achieving continuous performance gains, whereas fixed-orientation and isotropic schemes degrade (Fig. 7 ). Moreover, enlarging the allowable rotation range provides greater spatial alignment flexibility and further improves system performance (Fig. 8 ). Overall, the results demonstrate that the proposed architecture enhances near-field energy focusing and achieves performance close to fully digital beamforming with lower hardware complexity.Conclusions A rotatable-antenna-assisted near-field wideband ISAC system with a TTD-based fully connected hybrid beamforming architecture is investigated. By jointly exploiting antenna rotation and true time delay, the proposed framework effectively mitigates near-field effects and wideband beam splitting. A penalty-based fully digital approximation (PBFDA) optimization algorithm is developed to address the resulting highly non-convex problem. Numerical results demonstrate that the proposed scheme significantly improves system sum rate under sensing constraints and approaches the performance of fully digital beamforming, validating its effectiveness for near-field wideband ISAC applications. -
2 求解最优全数字波束的算法
1：初始化$ t \leftarrow 0, \Omega^{(t)} \leftarrow \Omega^{(0)}, \tau, T_{\max} $; 2：重复 3： t$ \leftarrow t + 1 $ 4： 根据(26)、(27)更新$ \mu^{(t)} $, $\lambda^{(t)} $ 5： 求解(25)关于$\Omega^{(t)} $的子问题，更新$\Omega^{(t)} $ 6：直到：问题(25)的目标函数值的相对变化量小于$\tau $或达到最大
迭代次数$ T_{\max} $7：返回$\Omega^* = \Omega^{(t)}, \mu^* = \mu^{(t)}, \lambda^* = \lambda^{(t)} $ 
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4 用于求解问题(13)的罚函数法
1: 初始化矩阵FRF, Tm, Vm, Wm, F，以及参数0 < e < 1, ρ > 0; 2: 重复 3: 重复 4: 利用算法1更新F 5: 利用算法2和公式(28)更新Wm 6: for i = 1:L 7: 利用算法3更新矩阵FRF 8: 利用式(36)更新矩阵Tm 9: 利用式(37)更新FBB,m 10: end for 11: 直到：问题(14)的目标函数值的相对变化量小于τ 或达到最大迭代次数Tmax 12: 将罚因子更新为ρ = eρ 13: 直到：罚函数值小于设定的阈值; 14: 缩放数字波束$ {\bf{F}}_{{\mathrm{BB}},m} = \frac{P_t}{\|{\bf{W}}_m - {\bf{F}}_{{\mathrm{RF}}}{\bf{T}}_m{\bf{F}}_{{\mathrm{BB}},m} \|_F^2} {\bf{F}}_{{\mathrm{BB}},m} $
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1 PSO求解指向矩阵 F
1: 初始化: $\{{\boldsymbol{\theta}}_i^{(0)}, {\boldsymbol{\psi}}_i^{(0)}\}_{i=1}^S, \alpha_max, \alpha_min, \beta_1, \beta_2, \xi_1, \xi_2, T $; 2: ${\boldsymbol{\theta}}_i^* = {\boldsymbol{\theta}}_i^{(0)}, {\boldsymbol{\theta}}^best = {\boldsymbol{\theta}}_{{\mathrm{arg}} \mathop{\mathrm{max}}\limits_{P=1,2,\cdots,S} J({\boldsymbol{\theta}}_i^*)}' $ 3: for t=1:T 4: $\alpha = \alpha_{\max} - (\alpha_{\max} - \alpha_{\min}) t/T $; 5: for i=1:S 6: ${\boldsymbol{\psi}}_i^{(t)} = \alpha{\boldsymbol{\psi}}_i^{(t-1)} + \beta_1\xi_1({\boldsymbol{\theta}}_i^* - {\boldsymbol{\theta}}_i^{(t-1)}) +$
$ \beta_2\xi_2({\boldsymbol{\theta}}^{{\mathrm{best}}} - {\boldsymbol{\theta}}_i^{(t-1)}); $7: ${\boldsymbol{\theta}}_i^{(t)} = {\boldsymbol{\theta}}_i^{(t-1) }+ {\boldsymbol{\psi}}_i^{(t)}; $ 8: 通过式(23)对${\boldsymbol{\theta}}_i^{(t)} $投影修正以满足约束(13f); 9: if $ J({\boldsymbol{\theta}}_i^(t)) \gt J({\boldsymbol{\theta}}_i^*) $, ${\boldsymbol{\theta}}_i^* = {\boldsymbol{\theta}}_i^{(t)} $; 10: end for 11: ${\boldsymbol{\theta}}^best = {\boldsymbol{\theta}}_{arg \mathop{\mathrm{max}}\limits_{i=1,2,\cdots,S} J({\boldsymbol{\theta}}_i^*)}' $ 12: end for 13: 输出: 通过${\boldsymbol{\theta}}^{\mathrm{best}} $构造指向矩阵F。
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3 黎曼流形求解模拟波束赋形器$\mathbf{F}_{RF} $
1：初始化：$\{ \mathbf{W}_m \}_{m=1}^M, \{ \mathbf{F}_{BB,m} \}_{m=1}^M, \eta, \xi, i = 0, I $ 2：$ \Phi_0 = \mathbf{F}_{RF}, \Delta_0 = -\text{ grad } F(\Phi_0) $ 3：for $i = 1 : I $ 4： $\Phi_i = (\Phi_{i-1} + \eta \Delta_{i-1}) \oslash |\Phi_{i-1} + \eta \Delta_{i-1}| $ 5： $\Phi_i = \Phi_i \circ \Phi_{\text{ref}} $ 6： $\mathbf{C}_{i-1} = \Delta_{i-1} - \Re(\Delta_{i-1} \circ \Phi_i^*) \circ \Phi_i $ 7： $\Delta_i = \xi \mathbf{C}_{i-1} - \text{grad } F(\Phi_i) $ 8：end for 9：输出：$\mathbf{F}_{RF} = \Phi_I $
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表 1 仿真参数
参数名称 符号 数值 参数名称 符号 数值 基站天线数 $ N $ 32 天线间距 $ d $ $ {\lambda }_{\text{c}}/2 $ 射频链路数 $ {N}_{\text{RF}} $ 4 TTD数量 $ {N}_{\text{T}} $ 8 载波中心频率 $ {f}_{\text{c}} $ 2.4 GHz TTD最大时延 $ {t}_{\max } $ $ N/(2{f}_{\text{c}})\approx 6.67 $ ns 系统带宽 $ B $ 80 MHz 最大旋转角度 $ {\phi }_{\max } $ $ \text{π} /6 $ 子载波数 $ M $ 6 用户/目标数 $ K $/ - 4 / 1 循环前缀长度 $ {L}_{\text{CP}} $ 4 用户距离范围 - 5 $ \sim $ 10 m 非视距路径数 $ {L}_{k} $ 2 噪声功率 $ \sigma _{m,k}^{2} $ -80 dBm 方向性因子 $ p $ 4 单天线尺寸 $ A $ $ \lambda _{\text{c}}^{2}/4\text{π} $ 单载波最大发射功率 $ {P}_{\text{t}} $ 20 dBm 感知功率阈值 $ {P}_{0} $ 3 W 收敛阈值 $ \epsilon $ 10–6 惩罚因子 $ \rho $ 103 缩减系数 $ e $ 0.5 一维搜索精度 $ Q $ 103 最大/最小惯性系数 $ {\alpha }_{\max } $/$ {\alpha }_{\min } $ 0.9/0.4 学习因子 $ {\beta }_{1} $/$ {\beta }_{2} $ 2 更新次数/粒子数量 $ T $/$ S $ 500/50 PSO惩罚因子 $ \lambda $ 500
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