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Register a new userDeep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming
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Added by
Zhiyan Liu
Publication date
2021
fields
Electronic Engineering
and research's language is
English
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In this paper, we consider a massive multiple-input-multiple-output (MIMO) downlink system that improves the hardware efficiency by dynamically selecting the antenna subarray and utilizing 1-bit phase shifters for hybrid beamforming. To maximize the spectral efficiency, we propose a novel deep unsupervised learning-based approach that avoids the computationally prohibitive process of acquiring training labels. The proposed design has its input as the channel matrix and consists of two convolutional neural networks (CNNs). To enable unsupervised training, the problem constraints are embedded in the neural networks: the first CNN adopts deep probabilistic sampling, while the second CNN features a quantization layer designed for 1-bit phase shifters. The two networks can be trained jointly without labels by sharing an unsupervised loss function. We next propose a phased training approach to promote the convergence of the proposed networks. Simulation results demonstrate the advantage of the proposed approach over conventional optimization-based algorithms in terms of both achieved rate and computational complexity.
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Increasing the number of transmit and receive elements in multiple-input-multiple-output (MIMO) antenna arrays imposes a substantial increase in hardware and computational costs. We mitigate this problem by employing a reconfigurable MIMO array where large transmit and receive arrays are multiplexed in a smaller set of k baseband signals. We consider four stages for the MIMO array configuration and propose four different selection strategies to offer dimensionality reduction in post-processing and achieve hardware cost reduction in digital signal processing (DSP) and radio-frequency (RF) stages. We define the problem as a determinant maximization and develop a unified formulation to decouple the joint problem and select antennas/elements in various stages in one integrated problem. We then analyze the performance of the proposed selection approaches and prove that, in terms of the output SINR, a joint transmit-receive selection method performs best followed by matched-filter, hybrid and factored selection methods. The theoretical results are validated numerically, demonstrating that all methods allow an excellent trade-off between performance and cost.
In this paper, we consider the problem of joint antenna selection and analog beamformer design in downlink single-group multicast networks. Our objective is to reduce the hardware costs by minimizing the number of required phase shifters at the transmitter while fulfilling given distortion limits at the receivers. We formulate the problem as an L0 minimization problem and devise a novel branch-and-cut based algorithm to solve the resulting mixed-integer nonlinear program to optimality. We also propose a suboptimal heuristic algorithm to solve the above problem approximately with a low computational complexity. Computational results illustrate that the solutions produced by the proposed heuristic algorithm are optimal in most cases. The results also indicate that the performance of the optimal methods can be significantly improved by initializing with the result of the suboptimal method.
Cooperative beamforming across access points (APs) and fronthaul quantization strategies are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP power and fronthaul capacity constraints, requires high computational complexity for executing iterative algorithms. To resolve this issue, we investigate a deep learning approach where the optimization module is replaced with a well-trained deep neural network (DNN). An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies. Numerical results validate the advantages of the proposed learning solution.
In this paper, outage probability (OP) of a joint transmit and receive antenna selection (JTRAS) scheme is analyzed in multiple-input multiple-output non orthogonal multiple access based downlink energy harvesting (EH) relaying networks. In this dual-hop and amplify-and-forward relaying based network, since the first and second hops are types of single-user and multi-user systems, respectively, the optimal JTRAS and suboptimal majority-based JTRAS schemes are employed in the first and second hops. The theoretical OP analysis is carried out over Nakagami-m fading channels in the cases of perfect and imperfect successive interference cancellation. Finally, Monte Carlo simulations are performed to substantiate the accuracy of the theoretical analysis. It is shown that the optimal power splitting ratios at the EH relay are different for users and the users with good channel conditions have minimum optimal ratios.
Integrating unmanned aerial vehicle (UAV) into the existing cellular networks that are delicately designed for terrestrial transmissions faces lots of challenges, in which one of the most striking concerns is how to adopt UAV into the cellular networks with less (or even without) adverse effects to ground users. In this paper, a cellular-connected UAV network is considered, in which multiple UAVs receive messages from terrestrial base stations (BSs) in the down-link, while BSs are serving ground users in their cells. Besides, the line-of-sight (LoS) wireless links are more likely to be established in ground-to-air (G2A) transmission scenarios. On one hand, UAVs may potentially get access to more BSs. On the other hand, more co-channel interferences could be involved. To enhance wireless transmission quality between UAVs and BSs while protecting the ground users from being interfered by the G2A communications, a joint time-frequency resource block (RB) and beamforming optimization problem is proposed and investigated in this paper. Specifically, with given flying trajectory, the ergodic outage duration (EOD) of UAV is minimized with the aid of RB resource allocation and beamforming design. Unfortunately, the proposed optimization problem is hard to be solved via standard optimization techniques, if not impossible. To crack this nut, a deep reinforcement learning (DRL) solution is proposed, where deep double duelling Q network (D3QN) and deep deterministic policy gradient (DDPG) are invoked to deal with RB allocation in discrete action domain and beamforming design in continuous action regime, respectively. The hybrid D3QN-DDPG solution is applied to solve the outer Markov decision process (MDP) and the inner MDP interactively so that it can achieve the sub-optimal result for the considered optimization problem.
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