2 research outputs found
Analysis of D2D Communication with RF Energy Harvesting and Interference Management
Device-to-device (D2D) underlaid cellular network, enabled with radio
frequency energy harvesting (RFEH), and enhanced interference management
schemes is a promising candidate to improve spectral and energy efficiency of
next generation wireless networks. In this paper, we propose a time division
duplexing (TDD)-based protocol, in which allows the devices to harvest energy
from the downlink transmissions of the base station, while controlling the
interference among D2D and cellular communication in the uplink. We propose two
schemes for transmission coordination, based on fixed transmission probability
(FTP) and adaptive transmission probability (ATP), respectively. In FTP, the
D2D transmitters that have harvested enough energy can initiate data
transmission with a fixed probability. Differently from this, in ATP a device
utilizes its sensing capability to get improved coordination and interference
control among the transmitting devices. We evaluate the network performance by
presenting an accurate energy model and leveraging tools from stochastic
geometry. The results on outage probability and D2D sum-rate reveal the
importance of transmission coordination on network performance. These
observations led to a solution for choosing the parameters of the ATP scheme
that achieves an optimal tradeoff between the D2D outage probability and number
of transmitting users
Multicell Power Control under Rate Constraints with Deep Learning
In the paper we study a deep learning based method to solve the multicell
power control problem for sum rate maximization subject to per-user rate
constraints and per-base station (BS) power constraints. The core difficulty of
this problem is how to ensure that the learned power control results by the
deep neural network (DNN) satisfy the per-user rate constraints. To tackle the
difficulty, we propose to cascade a projection block after a traditional DNN,
which projects the infeasible power control results onto the constraint set.
The projection block is designed based on a geometrical interpretation of the
constraints, which is of low complexity, meeting the real-time requirement of
online applications. Explicit-form expression of the backpropagated gradient is
derived for the proposed projection block, with which the DNN can be trained to
directly maximize the sum rate via unsupervised learning. We also develop a
heuristic implementation of the projection block to reduce the size of DNN.
Simulation results demonstrate the advantages of the proposed method over
existing deep learning and numerical optimization~methods, and show the
robustness of the proposed method with the model mismatch between training and
testing~datasets