AI 中文总结
针对IRS辅助多用户MISO下行链路，提出仅利用BER反馈的黑盒攻击方法PRIS，通过在线学习相位切换并逐步细化搜索，在有限测试预算下最大化最差用户BER，性能优于现有基准。
AI 中文摘要
智能反射面（IRS）已成为增强合法通信的一种有前景的无线技术。本文研究了一个IRS辅助的多用户MISO下行网络，其中攻击者重新配置IRS以降低合法数据传输质量。具体而言，我们考虑一种黑盒攻击场景，攻击者既无法获取信道信息，也无法获取符号级观测，只能观察到每个用户报告的误码率（BER）。攻击者的目标是在有限的非重复测试预算下，确定一种IRS配置，以最大化所有用户中的最小BER。为解决这一仅含BER反馈的黑盒攻击问题，我们提出了PRIS，一种在线搜索方法，该方法从先前的试验中学习有效的相位切换，并逐步将搜索范围从大块修改缩小到单元素细化。具体地，我们引入了退火的用户平衡奖励和优先回放，以利用噪声BER反馈。数值结果表明，PRIS实现的最小BER高于现有基准方法。
英文摘要
Intelligent reflecting surface (IRS) has emerged as a promising wireless technology for enhancing legitimate communications. In this paper, we investigate an IRS-assisted multiuser MISO downlink network in which an attacker reconfigures the IRS to degrade legitimate data transmission. Specifically, we consider a black-box attack setting in which the attacker has access to neither channel information nor symbol-level observations and can observe only the BER reported by each user. The objective of the attacker is to identify an IRS configuration that maximizes the minimum BER among all users under a limited budget of non-repeated tests. To address this BER-only black-box attack problem, we propose PRIS, an online search method that learns effective phase transitions from previous trials and progressively narrows the search from large-block modifications to single-element refinement. Specifically, an annealed user-balancing reward and prioritized replay are incorporated to exploit noisy BER feedback. Numerical results demonstrate that PRIS achieves a higher minimum BER than existing benchmarks.
Comments5 pages, 4 figures
