标题： 鲁棒且高效的平均一致性与非相干过空气聚合 显示英文标题

标题： Robust and Efficient Average Consensus with Non-Coherent Over-the-Air Aggregation

摘要： 非相干过空气接口（OTA）计算因其在资源受限的网络中促进分布式代理间信息聚合的优势而受到越来越多的关注，且无需精确的信道估计。 该方法的一个有前景的应用场景是无线多智能体系统的分布式平均一致性。 然而，在这种情况下，同时传输的非相干干扰可能会导致一致性值出现偏差。 为了解决这个问题，我们通过将一致性问题建模为一个分布式优化问题，开发了一种鲁棒的分布式平均一致性算法。 利用去中心化投影梯度下降（D-PGD），我们的算法即使在存在非相干干扰和噪声的情况下，也能实现无偏均方平均一致性。 此外，我们还实现了发送功率控制和接收缩放机制以进一步加速收敛。 仿真结果表明，我们的方法可以显著提高D-PGD算法在OTA平均一致性中的收敛速度，而不影响准确性。 显示英文摘要

摘要： Non-coherent over-the-air (OTA) computation has garnered increasing attention for its advantages in facilitating information aggregation among distributed agents in resource-constrained networks without requiring precise channel estimation. A promising application scenario of this method is distributed average consensus in wireless multi-agent systems. However, in such scenario, non-coherent interference from concurrent OTA transmissions can introduce bias in the consensus value. To address this issue, we develop a robust distributed average consensus algorithm by formulating the consensus problem as a distributed optimization problem. Using decentralized projected gradient descent (D-PGD), our proposed algorithm can achieve unbiased mean square average consensus even in the presence of non-coherent interference and noise. Additionally, we implement transmit power control and receive scaling mechanisms to further accelerate convergence. Simulation results demonstrate that our method can significantly enhance the convergence speed of the D-PGD algorithm for OTA average consensus without compromising accuracy.