This paper proposes an adaptive bit allocation scheme by using a fully connected (FC) deep neural network (DNN) considering imperfect channel state information (CSI) for heterogeneous networks. Achieving an accurate CSI has a crucial role on the system performance of the heterogeneous networks. Different quantization techniques have been employed to reduce the feedback overhead. However, the system performance cannot increase linearly with the number of bits increasing exponentially. Since optimizing the total number of bits is too complex for the entire network, an initial step is performed to distribute the bits to each cell in the conventional method. Then, the distributed bits are further allocated to each channel optimally. In order to enable direct allocation for the entire network, a FC-DNN based method is presented in this study. The optimized number of bits can be directly obtained for a different number of bits and scenarios by the proposed approach. The simulations are performed by using various scenarios with different allocation schemes. The performance results show that the DNN based method achieves a closer performance to the conventional approach.

本文提出了一种自适应比特分配方案，该方案使用全连接（FC）深度神经网络（DNN）考虑异构网络的不完善信道状态信息（CSI）。实现准确的 CSI 对异构网络的系统性能具有至关重要的作用。已采用不同的量化技术来减少反馈开销。然而，系统性能不能随着比特数呈指数增长而线性增长。由于优化总比特数对于整个网络来说过于复杂，因此在传统方法中执行初始步骤以将比特分配到每个单元。然后，将分配的比特进一步优化分配给每个信道。为了能够对整个网络进行直接分配，本研究提出了一种基于 FC-DNN 的方法。通过所提出的方法，可以针对不同的比特数和场景直接获得优化的比特数。通过使用具有不同分配方案的各种场景来执行模拟。性能结果表明，基于 DNN 的方法实现了与传统方法更接近的性能。