论文标题
用于集成多播多播毫米波传播的能节能混合编码设计设计
Energy-Efficient Hybrid Precoding Design for Integrated Multicast-Unicast Millimeter Wave Communications with SWIPT
论文作者
论文摘要
在本文中,我们研究了集成的多播毫米波(MMWave)系统的节能混合编码设计,其中在接收器中考虑了同时的无线信息和功率变换。我们在基站(BS)(即完全连接和子阵列结构)中采用两个稀疏的射频链天线结构,并根据不同的结构设计基于代码的模拟编码。然后,我们制定了联合数字多播,单播预编码和功率分裂比优化问题,以最大化系统的能效,而BS处的最大发射功率和接收器处的最小收获能量则被考虑。由于它难以直接解决该法式问题,因此我们等效地将分数目标函数转换为减去形式的形式,并提出了一种两循环迭代算法来求解它。对于外循环,应用了经典的双段迭代算法。对于内部循环,我们通过连续的凸近似技术将配方的问题转换为凸一个问题,并提出了一种迭代算法来求解它。同时,为了降低内部循环的复杂性,我们开发了零强迫(ZF)基于技术的低复杂性迭代算法。具体而言,应用ZF技术来取消互化干扰,而一阶taylor近似用于原始问题中的非convex约束。最后,提供了模拟结果,以比较不同方案下提出的算法的性能。
In this paper, we investigate the energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. We adopt two sparse radio frequency chain antenna structures at the base station (BS), i.e., fully-connected and subarray structures, and design the codebook-based analog precoding according to the different structures. Then, we formulate a joint digital multicast, unicast precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at the BS and minimum harvested energy at receivers are considered. Due to its difficulty to directly solve the formulated problem, we equivalently transform the fractional objective function into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques and propose an iterative algorithm to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop a zero forcing (ZF) technique-based low complexity iterative algorithm. Specifically, the ZF technique is applied to cancel the inter-unicast interference and the first order Taylor approximation is used for the convexification of the non-convex constraints in the original problem. Finally, simulation results are provided to compare the performance of the proposed algorithms under different schemes.