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The rapid advancement of communication technologies has made acoustic echo cancellation (AEC) and noise suppression (NS) increasingly essential. Most existing research tackles these tasks separately, often cascading models in practical systems, which is not suitable for resource-constrained environments. In contrast, a model that simultaneously addresses both tasks with minimal computational resources can significantly reduce system complexity. Furthermore, traditional AEC systems often encounter challenges with audio signal delays, compromising their effectiveness. This paper proposes the cross-attention gated convolutional recurrent network (CAGCRN), which utilizes a gating mechanism to efficiently collaborate AEC and NS tasks, and a cross-attention mechanism to align delays. Experimental results show that CAGCRN excels in both AEC and NS tasks while requiring minimal computational resources, with only 0.07M parameters, making it ideal for devices with limited capabilities.