18 research outputs found
Channel Assignment in Uplink Wireless Communication using Machine Learning Approach
This letter investigates a channel assignment problem in uplink wireless
communication systems. Our goal is to maximize the sum rate of all users
subject to integer channel assignment constraints. A convex optimization based
algorithm is provided to obtain the optimal channel assignment, where the
closed-form solution is obtained in each step. Due to high computational
complexity in the convex optimization based algorithm, machine learning
approaches are employed to obtain computational efficient solutions. More
specifically, the data are generated by using convex optimization based
algorithm and the original problem is converted to a regression problem which
is addressed by the integration of convolutional neural networks (CNNs),
feed-forward neural networks (FNNs), random forest and gated recurrent unit
networks (GRUs). The results demonstrate that the machine learning method
largely reduces the computation time with slightly compromising of prediction
accuracy
Joint User Scheduling and Beamforming Design for Multiuser MISO Downlink Systems
In multiuser communication systems, user scheduling and beamforming (US-BF)
design are two fundamental problems that are usually studied separately in the
existing literature. In this work, we focus on the joint US-BF design with the
goal of maximizing the set cardinality of scheduled users, which is
computationally challenging due to the non-convex objective function and the
coupled constraints with discrete-continuous variables. To tackle these
difficulties, a successive convex approximation based US-BF (SCA-USBF)
optimization algorithm is firstly proposed. Then, inspired by wireless
intelligent communication, a graph neural network based joint US-BF (J-USBF)
learning algorithm is developed by combining the joint US and power allocation
network model with the BF analytical solution. The effectiveness of SCA-USBF
and J-USBF is verified by various numerical results, the latter achieves close
performance and higher computational efficiency. Furthermore, the proposed
J-USBF also enjoys the generalizability in dynamic wireless network scenarios.Comment: 31 pages, 9 figures, submit to IEEE Transactions on Wireless
Communication
Learning Decentralized Wireless Resource Allocations with Graph Neural Networks
We consider the broad class of decentralized optimal resource allocation
problems in wireless networks, which can be formulated as a constrained
statistical learning problems with a localized information structure. We
develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process
a sequence of delayed and potentially asynchronous graph aggregated state
information obtained locally at each transmitter from multi-hop neighbors. We
further utilize model-free primal-dual learning methods to optimize performance
subject to constraints in the presence of delay and asynchrony inherent to
decentralized networks. We demonstrate a permutation equivariance property of
the resulting resource allocation policy that can be shown to facilitate
transference to dynamic network configurations. The proposed framework is
validated with numerical simulations that exhibit superior performance to
baseline strategies.Comment: 13 pages, 13 figure