在這研究裡,我們研究多用戶多輸入單輸出(Multi-input single output)干擾通道之合 作式波束成型設計(Cooridnated beamforming design),其中接收端具有分配功率(Power splitting)之獵能(Energy harvesting)功能,並且假設傳送端只知道通道之統計特性(Channel distribution information)。在給定傳送中斷機率(Outage probability)和平均獵能功率下,我 們致力於系統效能最大化,並且共同將波束成型向量與功率分配參數最佳化。此共同優化問 題涉及複雜且非凸特性的限制條件。我們提出一個集中式連續凸化近似(Successive convex approximation)演算法,其主要方法是透過反覆解一連串近似凸化的問題。我們進一步提 出了兩個連續凸化近似演算法的分散式實現:串行式實現(DSCA-Serial)和平行式實現 (DSCA-Parallel)。對典型的獵能波束成型設計,獵能能量與系統效能的去捨通常是可預期 的。非常意外的,我們模擬結果顯示只需犧牲1%的系統效能就可在接收端汲取10%的傳送能 量。與窮舉法(exhaustive search method)相比,我們提出的三個演算法皆能達到接近最佳的 表現。與另外兩個演算法相比,我們也觀察到分散式連續凸化近似演算法之乘子交替方向法 節省了大量的運算時間,也顯現了分散式連續凸化近似演算法的平行式實現的巨大潛力。

In this work, we consider coordinated beamforming design in a multi-user multiple input single output (MISO) interference channel where receivers are capable of energy harvesting (EH) in a power splitting manner, assuming only channel distribution information (CDI) is known to transmitter. Under a given requirement on the rate outage probability and the average EH power for receivers, we aim to maximize the system utility where the beamforming vectors and the power splitting factor are jointly optimized. This joint optimization problem involves complicated and non-convex constraints. We propose a centralized successive convex approximation (SCA) algorithm via solving a series of approximate convex problems iteratively. We further propose two decentralized implementations of the SCA algorithm: one serial implementation (DSCA-Serial) and the other parallel implementation (DSCA-Parallel). For a typical EH beamforming design, a tradeoff between the harvested energy and the system utility is often expected. Surprisingly, our simulations show that only less than 1% of the system utility is sacrificed while 10% of the transmitted power is harvested at receivers. All three proposed algorithms are shown to achieve near-optimal performance when compared with the exhaustive search method. Significant reduction in computational time is also observed for the proposed DSCA-Parallel when compared with DSCA-Serial, indicating a promising potential for a parallel implementation of decentralized SCA algorithm.

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