8,728 research outputs found
A robust machine learning method for cell-load approximation in wireless networks
We propose a learning algorithm for cell-load approximation in wireless
networks. The proposed algorithm is robust in the sense that it is designed to
cope with the uncertainty arising from a small number of training samples. This
scenario is highly relevant in wireless networks where training has to be
performed on short time scales because of a fast time-varying communication
environment. The first part of this work studies the set of feasible rates and
shows that this set is compact. We then prove that the mapping relating a
feasible rate vector to the unique fixed point of the non-linear cell-load
mapping is monotone and uniformly continuous. Utilizing these properties, we
apply an approximation framework that achieves the best worst-case performance.
Furthermore, the approximation preserves the monotonicity and continuity
properties. Simulations show that the proposed method exhibits better
robustness and accuracy for small training sets in comparison with standard
approximation techniques for multivariate data.Comment: Shorter version accepted at ICASSP 201
Applications of Soft Computing in Mobile and Wireless Communications
Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications
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
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