论文标题
绩效分析和用户关联对无线网络的优化在多个智能反射表面的帮助下
Performance Analysis and User Association Optimization for Wireless Network Aided by Multiple Intelligent Reflecting Surfaces
论文作者
论文摘要
智能反射表面（IRS）被视为一种有前途的解决方案，可以成本效率地提高无线通信的光谱和能源效率。在本文中，我们考虑了一个无线网络，其中多个基站（BSS）在下行链路通信中的分布式IRS的帮助下为各自的用户提供服务。具体而言，每个IRS通过被动式面对面有助于从其关联的BS到用户的传输，而与此同时，它还随机分散了来自其他共渠道BSS的信号，从而导致网络中的其他信号以及干扰路径。因此，出现了新的IRS使用者/BS关联问题，这是为了在不同的BS-用户通信链接中所有IRS中的被动式上的增益达到最佳平衡。 To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power控制分别。我们还表征了用户asainrs的缩放行为，其IR数量的增加反映了元素，以研究IRS反射信号与干扰功率的不同影响。此外，要解决两个非凸优化问题的Asainr平衡问题，我们在没有BS功率控制和低复杂性次优的解决方案的情况下提出了一个最佳解决方案，分别应用了分支机构方法，并分别利用IRS- user联合的新特性。数值结果验证了我们的性能分析，并且还证明了基准方案上提出的解决方案的显着性能提高。
Intelligent reflecting surface (IRS) is deemed as a promising solution to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we consider a wireless network where multiple base stations (BSs) serve their respective users with the aid of distributed IRSs in the downlink communication. Specifically, each IRS assists in the transmission from its associated BS to user via passive beamforming, while in the meantime, it also randomly scatters the signals from other co-channel BSs, thus resulting in additional signal as well as interference paths in the network. As such, a new IRS-user/BS association problem arises pertaining to optimally balance the passive beamforming gains from all IRSs among different BS-user communication links. To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power control, respectively. We also characterize the scaling behavior of user ASAINRs with the increasing number of IRS reflecting elements to investigate the different effects of IRS-reflected signal versus interference power. Moreover, to solve the two ASAINR balancing problems that are both non-convex optimization problems, we propose an optimal solution to the problem without BS power control and low-complexity suboptimal solutions to both problems by applying the branch-and-bound method and exploiting new properties of the IRS-user associations, respectively. Numerical results verify our performance analysis and also demonstrate significant performance gains of the proposed solutions over benchmark schemes.
