Partially-Connected Hybrid Beamforming Design for Integrated Sensing and Communication Systems

Abstract

Integrated sensing and communications (ISAC) has been considered one of the new paradigms for sixth-generation (6G) wireless networks. In the millimeter-wave (mmWave) ISAC system, hybrid beamforming (HBF) is considered an emerging technology to exploit the limited number of radio frequency (RF) chains in order to reduce the system hardware cost and power consumption. However, the HBF structure reduces the spatial degrees of freedom for the ISAC system, which further leads to increased interference between multiple users and between users and radar sensing. To solve the above problem, rate split multiple access (RSMA), which is a flexible and robust interference management strategy, is considered. We investigate the joint common rate allocation and HBF design problem for the HBF-based RSMA-assisted mmWave ISAC scheme. We propose the penalty dual decomposition (PDD) method coupled with the weighted mean squared error (WMMSE) minimization method to solve this high-dimensional non-convex problem, which converges to the Karush-Kuhn-Tucker (KKT) point of the original problem. Then, we extend the proposed algorithm to the HBF design based on finite-resolution phase shifters (PSs) to further improve the energy efficiency of the system. Simulation results demonstrate the effectiveness of the proposed algorithm and show that the RSMA-ISAC scheme outperforms other benchmark schemes.

Joint Transmit and Receive Beamforming Design for DPC-Based MIMO DFRC Systems

“…Obviously, ( 16) is a non-convex problem stemming from the quadratic equality constraint in (16b). Unlike ( 13) and ( 15), a closed-form optimal solution cannot be derived for (16). In what follows, we put forward an iterative algorithm to resolve this problem efficiently.…”

“…To solve (16), we first transform it into a quadratic problem defined on a complex Stiefel manifold. There is indeed inherent orthogonality among the transmit beamforming vectors due to the transmit covariance constraint in (16b).…”

“…which, in turn, leads to F * = L F * . As the F * c obtained by the MM method is a stationary point solution for problem (21) [35], F * is also a stationary point for the original subproblem (16) by the equivalence between the problems in ( 16) and (21).…”

“…The majority of prior studies [12][13][14][15][16][17][18] have focused on enhancing radar functionality while accommodating communication constraints through beamforming methodologies, thereby shaping a communication-centric DFRC system. For instance, efforts have been made to minimize errors in beampattern matching while guaranteeing a pre-defined level of SINR for each communication user, as demonstrated in [12][13][14].…”

“…For instance, efforts have been made to minimize errors in beampattern matching while guaranteeing a pre-defined level of SINR for each communication user, as demonstrated in [12][13][14]. Under a similar communication performance constraint, the optimization of the Cramér-Rao bound (CRB) for target estimation was pursued to directly assess sensing capabilities in [15][16][17]. Additionally, joint designs for transmit and receive beamforming were developed in [18] to maximize the SINR at the radar receiver while meeting spectral efficiency requirements.…”