A Model-based DNN for Learning HMIMO Beamforming

Holographic MIMO (HMIMO) is a promising technique for large-scale MIMO systems to enhance spectral efficiency while maintaining low hardware cost and power consumption. Existing alternating optimization algorithms can effectively optimize the hybrid beamforming of HMIMO to improve the system performance, while their high computational complexity hinders real-time application. In this paper, we propose a model-based deep neural network (MB-DNN), which leverages permutation equivalent properties and the optimal beamforming structure to jointly optimize the holographic and digital beamforming. Simulation results demonstrate that the proposed MB-DNN outperforms benchmark schemes and requires much less inference time than existing alternating optimization algorithms.

Learn to Optimize Resource Allocation under QoS Constraint of AR

This paper studies the uplink and downlink power allocation for interactive augmented reality (AR) services, where live video captured by an AR device is uploaded to the network edge and then the augmented video is subsequently downloaded. By modeling the AR transmission process as a tandem queuing system, we derive an upper bound for the probabilistic quality of service (QoS) requirement concerning end-to-end latency and reliability. The resource allocation with the QoS constraints results in a functional optimization problem. To address it, we design a deep neural network to learn the power allocation policy, leveraging the structure of optimal power allocation to enhance learning performance. Simulation results demonstrate that the proposed method effectively reduces transmit powers while meeting the QoS requirement.