A Deep Learning Framework for Optimization of MISO Downlink Beamforming

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for realtime implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems

Model-Driven Beamforming Neural Networks

Beamforming is evidently a core technology in recent generations of mobile communication networks. Nevertheless, an iterative process is typically required to optimize the parameters, making it ill-placed for real-time implementation due to high complexity and computational delay. Heuristic solutions such as zero-forcing (ZF) are simpler but at the expense of performance loss. Alternatively, deep learning (DL) is well understood to be a generalizing technique that can deliver promising results for a wide range of applications at much lower complexity if it is sufficiently trained. As a consequence, DL may present itself as an attractive solution to beamforming. To exploit DL, this article introduces general data- and model-driven beamforming neural networks (BNNs), presents various possible learning strategies, and also discusses complexity reduction for the DL-based BNNs. We also offer enhancement methods such as training-set augmentation and transfer learning in order to improve the generality of BNNs, accompanied by computer simulation results and testbed results showing the performance of such BNN solutions

Deep learning SIC approach for uplink MIMO-NOMA system

Abstract. Deep learning-based successive interference cancellation (DL-SIC) for uplink multiple-input multiple-output -non-orthogonal multiple access (MIMO-NOMA) system tries to optimize the users’ bit error rate (BER) and total mean square error (MSE) performance with higher order modulation schemes. The recent work of DL-SIC receiver design for users with a QPSK modulation scheme is investigated in this thesis to validate its performance as a potential alternative approach to traditional SIC receivers for NOMA users. Then, a DL-SIC receiver design for higher order modulation with less dependence on modulation order in the output layer is proposed, which enables us to decode the users with different modulation schemes. In our proposed design, we employ two deep neural networks (DNNs) for each SIC step. The system model is considered an M-antenna base station (BS) that serves two uplink users with a single antenna in the Rayleigh fading channel. The equivalent conventional minimum mean square error-based SIC (MMSE-SIC) and zero-forcing-based SIC (ZF-SIC) receivers are implemented as a baseline comparison. The simulation results showed that the BER performance of the proposed DL-SIC receiver for both users with QPSK modulation results in a 10 dB gain between BER of 10^(-2) and 10^(-3) compared to the ZF-SIC receiver. Furthermore, the performance difference between the proposed scheme and ZF-SIC is significantly high when both users transmit with 16QAM. Overall, the proposed DL-SIC receiver performs better in all signal-to-noise ratio (SNR) regions than the equivalent ZF-SIC receivers and also aids in mitigating the SIC error propagation problem. In addition, it improves the processing latency due to the benefits of the parallelized computing architecture and decreases the complexity of traditional SIC receivers

Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration

This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes