Secrecy Wireless Information and Power Transfer in Ultra-Dense Cloud-RAN with Wireless Fronthaul

J. Wang, Xinxin Ma, Le Zheng, Kai Yang, Zhao Chen, Qiaoqiao Xia
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Abstract

This paper studies the secrecy wireless information and power transfer problem in ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul, which is a promising framework for future Internet of Things (IoT). The transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense network such as base station diversity and high probability of line-of-sight transmission. Specifically, we employ the idea of block diagonalization to deal with the fronthaul interference, which support multi-stream fronthaul transmission for each remote radio head (RRH). We then jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of RRHs, and user-RRH association. In order to solve the formulated mixed integer non-convex optimization problem, we leverage the sparsity of beamforming vectors brought by the ultra-dense RRHs. We then solve the reformulated problem by employing the successive convex approximation approach. Finally, numerical results are presented to demonstrate the effectiveness of the proposed scheme.
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基于无线前传的超密集云- ran保密无线信息与电力传输
研究了具有无线前传的超密集云无线接入网(ld - cran)中的保密无线信息和功率传输问题,该网络是未来物联网(IoT)的一个有前途的框架。针对超密集网络中基站分集和视距传输概率大的特点,联合设计了无线前传和接入链路的传输方案。具体来说,我们采用块对角化的思想来处理前传干扰,从而支持每个远程无线电头(RRH)的多流前传传输。然后,我们共同优化了前传的功率分配和接入链路的资源分配,包括信息和能量传输的波束形成、rrh的开/关和用户rrh关联。为了解决公式化的混合整数非凸优化问题,我们利用了超密集RRHs带来的波束形成向量的稀疏性。然后,我们采用逐次凸逼近方法解决了重新表述的问题。最后给出了数值结果,验证了该方法的有效性。
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