1,741 research outputs found
Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks
Fog radio access networks (F-RANs), which consist of a cloud and multiple
edge nodes (ENs) connected via fronthaul links, have been regarded as promising
network architectures. The F-RAN entails a joint optimization of cloud and edge
computing as well as fronthaul interactions, which is challenging for
traditional optimization techniques. This paper proposes a Cloud-Enabled
Cooperation-Inspired Learning (CECIL) framework, a structural deep learning
mechanism for handling a generic F-RAN optimization problem. The proposed
solution mimics cloud-aided cooperative optimization policies by including
centralized computing at the cloud, distributed decision at the ENs, and their
uplink-downlink fronthaul interactions. A group of deep neural networks (DNNs)
are employed for characterizing computations of the cloud and ENs. The
forwardpass of the DNNs is carefully designed such that the impacts of the
practical fronthaul links, such as channel noise and signling overheads, can be
included in a training step. As a result, operations of the cloud and ENs can
be jointly trained in an end-to-end manner, whereas their real-time inferences
are carried out in a decentralized manner by means of the fronthaul
coordination. To facilitate fronthaul cooperation among multiple ENs, the
optimal fronthaul multiple access schemes are designed. Training algorithms
robust to practical fronthaul impairments are also presented. Numerical results
validate the effectiveness of the proposed approaches.Comment: to appear in IEEE Transactions on Wireless Communication
Learning Robust Beamforming for MISO Downlink Systems
This paper investigates a learning solution for robust beamforming
optimization in downlink multi-user systems. A base station (BS) identifies
efficient multi-antenna transmission strategies only with imperfect channel
state information (CSI) and its stochastic features. To this end, we propose a
robust training algorithm where a deep neural network (DNN), which only accepts
estimates and statistical knowledge of the perfect CSI, is optimized to fit to
real-world propagation environment. Consequently, the trained DNN can provide
efficient robust beamforming solutions based only on imperfect observations of
the actual CSI. Numerical results validate the advantages of the proposed
learning approach compared to conventional schemes.Comment: to appear in IEEE Communications Letters (5 pages, 5 figures, 1
tables
High-Resolution 3D Printing of Freeform, Transparent Displays in Ambient Air
Direct 3D printing technologies to produce 3D optoelectronic architectures have been explored extensively over the last several years. Although commercially available 3D printing techniques are useful for many applications, their limits in printable materials, printing resolutions, or processing temperatures are significant challenges for structural optoelectronics in achieving fully 3D-printed devices on 3D mechanical frames. Herein, the production of active optoelectronic devices with various form factors using a hybrid 3D printing process in ambient air is reported. This hybrid 3D printing system, which combines digital light processing for printing 3D mechanical architectures and a successive electrohydrodynamic jet for directly printing transparent pixels of organic light-emitting diodes at room temperature, can create high-resolution, transparent displays embedded inside arbitrarily shaped, 3D architectures in air. Also, the demonstration of a 3D-printed, eyeglass-type display for a wireless, augmented reality system is an example of another application. These results represent substantial progress in the development of next-generation, freeform optoelectronics
Deep Learning Methods for Universal MISO Beamforming
This letter studies deep learning (DL) approaches to optimize beamforming
vectors in downlink multi-user multi-antenna systems that can be universally
applied to arbitrarily given transmit power limitation at a base station. We
exploit the sum power budget as side information so that deep neural networks
(DNNs) can effectively learn the impact of the power constraint in the
beamforming optimization. Consequently, a single training process is sufficient
for the proposed universal DL approach, whereas conventional methods need to
train multiple DNNs for all possible power budget levels. Numerical results
demonstrate the effectiveness of the proposed DL methods over existing schemes.Comment: to appear in IEEE Wireless Communications Letters (5 pages, 3
figures, 2 tables
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks
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.Comment: accepted for publication on IEEE Wireless Communications Letter
A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization
Deep learning (DL) techniques have been intensively studied for the
optimization of multi-user multiple-input single-output (MU-MISO) downlink
systems owing to the capability of handling nonconvex formulations. However,
the fixed computation structure of existing deep neural networks (DNNs) lacks
flexibility with respect to the system size, i.e., the number of antennas or
users. This paper develops a bipartite graph neural network (BGNN) framework, a
scalable DL solution designed for multi-antenna beamforming optimization. The
MU-MISO system is first characterized by a bipartite graph where two disjoint
vertex sets, each of which consists of transmit antennas and users, are
connected via pairwise edges. These vertex interconnection states are modeled
by channel fading coefficients. Thus, a generic beamforming optimization
process is interpreted as a computation task over a weight bipartite graph.
This approach partitions the beamforming optimization procedure into multiple
suboperations dedicated to individual antenna vertices and user vertices.
Separated vertex operations lead to scalable beamforming calculations that are
invariant to the system size. The vertex operations are realized by a group of
DNN modules that collectively form the BGNN architecture. Identical DNNs are
reused at all antennas and users so that the resultant learning structure
becomes flexible to the network size. Component DNNs of the BGNN are trained
jointly over numerous MU-MISO configurations with randomly varying network
sizes. As a result, the trained BGNN can be universally applied to arbitrary
MU-MISO systems. Numerical results validate the advantages of the BGNN
framework over conventional methods.Comment: accepted for publication on IEEE Transactions on Wireless
Communication
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