Abstract
We study hybrid beamforming design for millimeter wave dual-function radar-communication system, which simultaneously performs downlink communication and target detection. With two typical analog beamformer structures, we consider the hybrid beamforming design to minimize a weighted summation of radar and communication performance. Leveraging Riemannian optimization theory, a manifold alternating direction method of multipliers is developed for the fully-connected structure. While for the partially-connected structure, a low-complexity Riemannian product manifold trust region algorithm is proposed to approach a near-optimal solution. Numerical simulations are provided to demonstrate the effectiveness of the proposed methods.
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Appendices
Appendix A: Derivation of Riemannian gradient
Recalling the Riemannian gradient is calculated as
where the Riemannian gradients \({\text {gra}}{{\text {d}}_{{{{\mathbf {F}}}_{PS}}}}J\left( {{{{\mathbf {F}}}_{PS}},{{{\mathbf {F}}}_{BB}}} \right) \) and \({\text {gra}}{{\text {d}}_{{{{\mathbf {F}}}_{BB}}}}J\left( {{{{\mathbf {F}}}_{PS}},{{{\mathbf {F}}}_{BB}}} \right) \) are computed by orthogonal projection from Euclidean space to the tangent space (Absil et al., 2009) as
where \({{\text {Gra}}{{\text {d}}_{{{{\mathbf {F}}}_{PS}}}}J\left( {{{{\mathbf {F}}}_{PS}}{\mathbf {,}}{{{\mathbf {F}}}_{BB}}} \right) }\) and \({{\text {Gra}}{{\text {d}}_{{{{\mathbf {F}}}_{BB}}}}J\left( {{{{\mathbf {F}}}_{PS}}{\mathbf {,}}{{{\mathbf {F}}}_{BB}}} \right) }\) denote, respectively, the Euclidean gradients with respect to \({{{{\mathbf {F}}}_{PS}}}\) and \({{{{\mathbf {F}}}_{BB}}}\), and they are calculated as
Appendix B: Derivation of Riemannian hessian
The Riemannian Hessian of \(J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) \) at \(\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) \in {\mathcal {M}}\) is a linear operator \({\text {Hes}}{{\text {s}}_{\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{BB}}} \right) }}J:{T_{\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{BB}}} \right) }}{{\mathcal {M}}}{\text { }} \mapsto {T_{\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{BB}}} \right) }}{{\mathcal {M}}}\) (Boumal, 2020; Absil et al., 2009; Udriste, 2013) defined by
for the point \({{{\varvec{\zeta }}}_{\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{BB}}} \right) }} \in {T_{\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{BB}}} \right) }}{{\mathcal {M}}}\). \(\nabla \) denotes the Levi-Civita connection of \({\mathcal {M}}\). Specifically, for the RPM, the Riemannian Hessian of \(J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) \) holds that (Absil et al., 2009)
where \({{{\varvec{\zeta }}}_{\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{BB}}} \right) }}\) is tangent vector on the tangent space, i.e. \({{{\varvec{\zeta }}}_{\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{BB}}} \right) }} \in {T_{\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{BB}}} \right) }}{{\mathcal {M}}}\), \({{\text {Hes}}{{\text {s}}_{{\mathbf{{F}}_{PS}}}}J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) }\) and \({{\text {Hes}}{{\text {s}}_{{\mathbf{{F}}_{BB}}}}J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) }\) denote, respectively, the Riemannian Hessian of \(J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) \) with respect to \({{\mathbf{{F}}_{PS}}}\) and \({{\mathbf{{F}}_{PS}}}\). Based on the classical expression of the Levi-Civita connection on a Riemannian submanifold of a Euclidean space (Boumal, 2020; Absil et al., 2009; Udriste, 2013), the Riemannian Hessian can be calculated via direction derivative in the embedding space followed by an orthogonal projection to the tangent space. Thus, the Riemannian Hessian \({{\text {Hes}}{{\text {s}}_{{\mathbf{{F}}_{PS}}}}J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) }\) and \({{\text {Hes}}{{\text {s}}_{{\mathbf{{F}}_{BB}}}}J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) }\) are computed as Li et al. (2020); Absil et al. (2013)
where \({{\text {Dgra}}{{\text {d}}_{{\mathbf{{F}}_{PS}}}}J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) \left[ {{\mathbf{{\zeta }}_{\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{BB}}} \right) }}} \right] }\) and \({{\text {Dgra}}{{\text {d}}_{{\mathbf{{F}}_{BB}}}}J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) \left[ {{\mathbf{{\zeta }}_{\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{BB}}} \right) }}} \right] }\) are the directional derivative of the Riemannian gradient \({{\text {gra}}{{\text {d}}_{{\mathbf{{F}}_{PS}}}}J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) }\) and \({\text {gra}}{{\text {d}}_{{\mathbf{{F}}_{BB}}}}J\left( {{\mathbf{{F}}_{PS}},{\mathbf{{F}}_{PS}}} \right) \), the Euclidean Hessian \({\text {eHes}}{{\text {s}}_{{\mathbf{{F}}_{PS}}}}J\) and \({\text {eHes}}{{\text {s}}_{{\mathbf{{F}}_{BB}}}}J\) denote, respectively, the directional derivative of the Euclidean gradient (54a) and (54b), which are computed as
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Wang, B., Cheng, Z. & He, Z. Manifold optimization for hybrid beamforming in dual-function radar-communication system. Multidim Syst Sign Process 34, 1–24 (2023). https://doi.org/10.1007/s11045-022-00851-x
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DOI: https://doi.org/10.1007/s11045-022-00851-x