Both dual-functional radar-communication (DFRC) and massive multiple-input multiple-output (MIMO) have been recognized as enabling technologies for 6G wireless networks. This paper considers the advanced waveform design for hardware-efficient massive MIMO DFRC systems. Specifically, the transmit waveform is imposed with the quantized constant-envelope (QCE) constraint, which facilitates the employment of low-resolution digital-to-analog converters (DACs) and power-efficient amplifiers. The waveform design problem is formulated as the minimization of the mean square error (MSE) between the designed and desired beampatterns subject to the constructive interference (CI)-based communication quality of service (QoS) constraints and the QCE constraint. To solve the formulated problem, we first utilize the penalty technique to transform the discrete problem into an equivalent continuous penalty model. Then, we propose an inexact augmented Lagrangian method (ALM) algorithm for solving the penalty model. In particular, the ALM subproblem at each iteration is solved by a custom-built block successive upper-bound minimization (BSUM) algorithm, which admits closed-form updates, making the proposed inexact ALM algorithm computationally efficient. Simulation results demonstrate the superiority of the proposed approach over existing state-of-the-art ones. In addition, extensive simulations are conducted to examine the impact of various system parameters on the trade-off between communication and radar performances.

翻译：双功能雷达通信（DFRC）与大规模多输入多输出（MIMO）均被视为6G无线网络的关键使能技术。本文针对硬件高效的大规模MIMO DFRC系统，提出先进波形设计方案。具体而言，对发射波形施加量化恒包络（QCE）约束，以促进低分辨率数模转换器（DAC）与高能效放大器的应用。波形设计问题被建模为：在基于建设性干扰（CI）的通信服务质量（QoS）约束与QCE约束下，最小化设计波束方向图与期望波束方向图之间的均方误差（MSE）。为求解该问题，首先采用惩罚技术将离散问题转化为等效的连续惩罚模型；随后提出一种不精确增广拉格朗日乘子法（ALM）算法以求解该惩罚模型。特别地，每次迭代中的ALM子问题由定制化的块连续上界最小化（BSUM）算法求解，该算法具有闭式更新形式，从而显著提升所提不精确ALM算法的计算效率。仿真结果表明，所提方法优于现有最优方案。此外，通过大量仿真实验，系统分析了各类系统参数对雷达与通信性能权衡的影响机制。