1,991 research outputs found
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Optical wireless communication (OWC) is a promising technology for future
wireless communications owing to its potentials for cost-effective network
deployment and high data rate. There are several implementation issues in the
OWC which have not been encountered in radio frequency wireless communications.
First, practical OWC transmitters need an illumination control on color,
intensity, and luminance, etc., which poses complicated modulation design
challenges. Furthermore, signal-dependent properties of optical channels raise
non-trivial challenges both in modulation and demodulation of the optical
signals. To tackle such difficulties, deep learning (DL) technologies can be
applied for optical wireless transceiver design. This article addresses recent
efforts on DL-based OWC system designs. A DL framework for emerging image
sensor communication is proposed and its feasibility is verified by simulation.
Finally, technical challenges and implementation issues for the DL-based
optical wireless technology are discussed.Comment: To appear in IEEE Communications Magazine, Special Issue on
Applications of Artificial Intelligence in Wireless Communication
Resource Allocation Techniques for Wireless Powered Communication Networks with Energy Storage Constraint
This paper studies multi-user wireless powered communication networks, where
energy constrained users charge their energy storages by scavenging energy of
the radio frequency signals radiated from a hybrid access point (H-AP). The
energy is then utilized for the users' uplink information transmission to the
H-AP in time division multiple access mode. In this system, we aim to maximize
the uplink sum rate performance by jointly optimizing energy and time resource
allocation for multiple users in both infinite capacity and finite capacity
energy storage cases. First, when the users are equipped with the infinite
capacity energy storages, we derive the optimal downlink energy transmission
policy at the H-AP. Based on this result, analytical resource allocation
solutions are obtained. Next, we propose the optimal energy and time allocation
algorithm for the case where each user has finite capacity energy storage.
Simulation results confirm that the proposed algorithms offer 30% average sum
rate performance gain over conventional schemes
Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in
the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The
system aims at delivering contents on demand with minimal average latency from
a time-varying library of popular contents. Information about uncached
requested files can be transferred from the cloud to the eRRHs by following
either backhaul or fronthaul modes. The backhaul mode transfers fractions of
the requested files, while the fronthaul mode transmits quantized baseband
samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the
eRRHs to be updated, which may lower future delivery latencies. In contrast,
the fronthaul mode enables cooperative C-RAN transmissions that may reduce the
current delivery latency. Taking into account the trade-off between current and
future delivery performance, this paper proposes an adaptive selection method
between the two delivery modes to minimize the long-term delivery latency.
Assuming an unknown and time-varying popularity model, the method is based on
model-free Reinforcement Learning (RL). Numerical results confirm the
effectiveness of the proposed RL scheme.Comment: 5 pages, 2 figure
Joint Design of Digital and Analog Processing for Downlink C-RAN with Large-Scale Antenna Arrays
In millimeter-wave communication systems with large-scale antenna arrays,
conventional digital beamforming may not be cost-effective. A promising
solution is the implementation of hybrid beamforming techniques, which consist
of low-dimensional digital beamforming followed by analog radio frequency (RF)
beamforming. This work studies the optimization of hybrid beamforming in the
context of a cloud radio access network (C-RAN) architecture. In a C-RAN
system, digital baseband signal processing functionalities are migrated from
remote radio heads (RRHs) to a baseband processing unit (BBU) in the "cloud" by
means of finite-capacity fronthaul links. Specifically, this work tackles the
problem of jointly optimizing digital beamforming and fronthaul quantization
strategies at the BBU, as well as RF beamforming at the RRHs, with the goal of
maximizing the weighted downlink sum-rate. Fronthaul capacity and per-RRH power
constraints are enforced along with constant modulus constraints on the RF
beamforming matrices. An iterative algorithm is proposed that is based on
successive convex approximation and on the relaxation of the constant modulus
constraint. The effectiveness of the proposed scheme is validated by numerical
simulation results
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