296 research outputs found
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
A Multi-Agent Deep Reinforcement Learning based Spectrum Allocation Framework for D2D Communications
Device-to-device (D2D) communication has been recognized as a promising
technique to improve spectrum efficiency. However, D2D transmission as an
underlay causes severe interference, which imposes a technical challenge to
spectrum allocation. Existing centralized schemes require global information,
which can cause serious signaling overhead. While existing distributed solution
requires frequent information exchange between users and cannot achieve global
optimization. In this paper, a distributed spectrum allocation framework based
on multi-agent deep reinforcement learning is proposed, named Neighbor-Agent
Actor Critic (NAAC). NAAC uses neighbor users' historical information for
centralized training but is executed distributedly without that information,
which not only has no signal interaction during execution, but also utilizes
cooperation between users to further optimize system performance. The
simulation results show that the proposed framework can effectively reduce the
outage probability of cellular links, improve the sum rate of D2D links and
have good convergence.Comment: Accepted to Globecom 201
Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communications
Device-to-device (D2D) communication underlay cellular networks is a
promising technique to improve spectrum efficiency. In this situation, D2D
transmission may cause severe interference to both the cellular and other D2D
links, which imposes a great technical challenge to spectrum allocation.
Existing centralized schemes require global information, which causes a large
signaling overhead. While existing distributed schemes requires frequent
information exchange among D2D users and cannot achieve global optimization. In
this paper, a distributed spectrum allocation framework based on multi-agent
deep reinforcement learning is proposed, named multi-agent actor critic (MAAC).
MAAC shares global historical states, actions and policies during centralized
training, requires no signal interaction during execution and utilizes
cooperation among users to further optimize system performance. Moreover, in
order to decrease the computing complexity of the training, we further propose
the neighbor-agent actor critic (NAAC) based on the neighbor users' historical
information for centralized training. The simulation results show that the
proposed MAAC and NAAC can effectively reduce the outage probability of
cellular links, greatly improve the sum rate of D2D links and converge quickly.Comment: Accepted to IEEE Transactions on Vehicular Technology. arXiv admin
note: text overlap with arXiv:1904.0661
Generative Neural Network based Spectrum Sharing using Linear Sum Assignment Problems
Spectrum management and resource allocation (RA) problems are challenging and
critical in a vast number of research areas such as wireless communications and
computer networks. The traditional approaches for solving such problems usually
consume time and memory, especially for large size problems. Recently different
machine learning approaches have been considered as potential promising
techniques for combinatorial optimization problems, especially the generative
model of the deep neural networks. In this work, we propose a resource
allocation deep autoencoder network, as one of the promising generative models,
for enabling spectrum sharing in underlay device-to-device (D2D) communication
by solving linear sum assignment problems (LSAPs). Specifically, we investigate
the performance of three different architectures for the conditional
variational autoencoders (CVAE). The three proposed architecture are the
convolutional neural network (CVAE-CNN) autoencoder, the feed-forward neural
network (CVAE-FNN) autoencoder, and the hybrid (H-CVAE) autoencoder. The
simulation results show that the proposed approach could be used as a
replacement of the conventional RA techniques, such as the Hungarian algorithm,
due to its ability to find solutions of LASPs of different sizes with high
accuracy and very fast execution time. Moreover, the simulation results reveal
that the accuracy of the proposed hybrid autoencoder architecture outperforms
the other proposed architectures and the state-of-the-art DNN techniques
Deep Learning-based Resource Allocation For Device-to-Device Communication
In this paper, a deep learning (DL) framework for the optimization of the
resource allocation in multi-channel cellular systems with device-to-device
(D2D) communication is proposed. Thereby, the channel assignment and discrete
transmit power levels of the D2D users, which are both integer variables, are
optimized to maximize the overall spectral efficiency whilst maintaining the
quality-of-service (QoS) of the cellular users. Depending on the availability
of channel state information (CSI), two different configurations are
considered, namely 1) centralized operation with full CSI and 2) distributed
operation with partial CSI, where in the latter case, the CSI is encoded
according to the capacity of the feedback channel. Instead of solving the
resulting resource allocation problem for each channel realization, a DL
framework is proposed, where the optimal resource allocation strategy for
arbitrary channel conditions is approximated by deep neural network (DNN)
models. Furthermore, we propose a new training strategy that combines
supervised and unsupervised learning methods and a local CSI sharing strategy
to achieve near-optimal performance while enforcing the QoS constraints of the
cellular users and efficiently handling the integer optimization variables
based on a few ground-truth labels. Our simulation results confirm that
near-optimal performance can be attained with low computation time, which
underlines the real-time capability of the proposed scheme. Moreover, our
results show that not only the resource allocation strategy but also the CSI
encoding strategy can be efficiently determined using a DNN. Furthermore, we
show that the proposed DL framework can be easily extended to communications
systems with different design objectives
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Survey on the state-of-the-art in device-to-device communication: A resource allocation perspective
Device to Device (D2D) communication takes advantage of the proximity between the communicating devices in order to achieve efficient resource utilization, improved throughput and energy efficiency, simultaneous serviceability and reduced latency. One of the main characteristics of D2D communication is reuse of the frequency resource in order to improve spectral efficiency of the system. Nevertheless, frequency reuse introduces significantly high interference levels thus necessitating efficient resource allocation algorithms that can enable simultaneous communication sessions through effective channel and/or power allocation. This survey paper presents a comprehensive investigation of the state-of-the-art resource allocation algorithms in D2D communication underlaying cellular networks. The surveyed algorithms are evaluated based on heterogeneous parameters which constitute the elementary features of a resource allocation algorithm in D2D paradigm. Additionally, in order to familiarize the readers with the basic design of the surveyed resource allocation algorithms, brief description of the mode of operation of each algorithm is presented. The surveyed algorithms are divided into four categories based on their technical doctrine i.e., conventional optimization based, Non-Orthogonal-MultipleAccess (NOMA) based, game theory based and machine learning based techniques. Towards the end, several open challenges are remarked as the future research directions in resource allocation for D2D communication
Learning to Branch: Accelerating Resource Allocation in Wireless Networks
Resource allocation in wireless networks, such as device-to-device (D2D)
communications, is usually formulated as mixed integer nonlinear programming
(MINLP) problems, which are generally NP-hard and difficult to get the optimal
solutions. Traditional methods to solve these MINLP problems are all based on
mathematical optimization techniques, such as the branch-and-bound (B&B)
algorithm that converges slowly and has forbidding complexity for real-time
implementation. Therefore, machine leaning (ML) has been used recently to
address the MINLP problems in wireless communications. In this paper, we use
imitation learning method to accelerate the B&B algorithm. With invariant
problem-independent features and appropriate problem-dependent feature
selection for D2D communications, a good auxiliary prune policy can be learned
in a supervised manner to speed up the most time-consuming branch process of
the B&B algorithm. Moreover, we develop a mixed training strategy to further
reinforce the generalization ability and a deep neural network (DNN) with a
novel loss function to achieve better dynamic control over optimality and
computational complexity. Extensive simulation demonstrates that the proposed
method can achieve good optimality and reduce computational complexity
simultaneously.Comment: to appear in IEEE Transactions on Vehicular Technolog
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