Machine Learning Assisted Cross-Layer Joint Optimal Subcarrier and Power Allocation for Device-to-Device Video Transmission

Abstract

The previous scheme used imitation learning by classification of branching or pruning, combined with Branch and Bound (B&B) algorithm to solve physical-layer-only joint optimal subcarrier and power allocation problem in the device-to-device communications. In this paper, we propose joint source encoding rate control and machine learning assisted cross-layer joint optimal subcarrier/ power allocation. The proposed scheme has source encoder rate control and can adaptively adjust video rate to increase the video quality, peak signal to noise ratio (PSNR). The previous physical-layer-only scheme did not use the content-based video rate adaption. Furthermore, the proposed scheme uses the objective function of PSNR directly and allocates the subcarrier and power considering the different rate-distortion function of users’ videos. The previous physical-layer-only scheme could only treat the users’ video equally. Under the new minimum PSNR constraint of the cellular user (CU), we derive a new objective function that is independent of the transmission power of the CU to simplify the optimization problem formulation. The previous scheme considered the physical-layer-only objective function and constraints. Finally, in addition to imitation learning, the proposed scheme adopts ensemble learning with downsampling the majority set {prune} to alleviate the class imbalance problem and improves performance. The simulation results show that in the scenario where the number of CUs is 5, the number of subcarriers is equal to the number of CUs, the bandwidth is 15k Hz, and the number of D2D pairs is 2, the PSNR of the previous physical-layer-only scheme is 31.03 dB, while our proposed cross-layer allocation scheme is 35.67 dB, a 4.64 dB gain. The trained model trained at 5 CUs can generalize without re-training to 10 CUs with only 5.91% gap to the optimal PSNR and 20.43 times speed (95% execution time reduction) when compared to the globally joint optimal subcarrier/power allocation B&B algorithm.

Keywords

• Video communications
• machine learning
• resource management
• Internet of Things
• data imbalance
• cross layer design