2 research outputs found
Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks
With years of tremendous traffic and energy consumption growth, green radio
has been valued not only for theoretical research interests but also for the
operational expenditure reduction and the sustainable development of wireless
communications. Fundamental green tradeoffs, served as an important framework
for analysis, include four basic relationships: spectrum efficiency (SE) versus
energy efficiency (EE), deployment efficiency (DE) versus energy efficiency
(EE), delay (DL) versus power (PW), and bandwidth (BW) versus power (PW). In
this paper, we first provide a comprehensive overview on the extensive on-going
research efforts and categorize them based on the fundamental green tradeoffs.
We will then focus on research progresses of 4G and 5G communications, such as
orthogonal frequency division multiplexing (OFDM) and non-orthogonal
aggregation (NOA), multiple input multiple output (MIMO), and heterogeneous
networks (HetNets). We will also discuss potential challenges and impacts of
fundamental green tradeoffs, to shed some light on the energy efficient
research and design for future wireless networks.Comment: revised from IEEE Communications Surveys & Tutorial
A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
As one of the key communication scenarios in the 5th and also the 6th
generation (6G) of mobile communication networks, ultra-reliable and
low-latency communications (URLLC) will be central for the development of
various emerging mission-critical applications. State-of-the-art mobile
communication systems do not fulfill the end-to-end delay and overall
reliability requirements of URLLC. In particular, a holistic framework that
takes into account latency, reliability, availability, scalability, and
decision making under uncertainty is lacking. Driven by recent breakthroughs in
deep neural networks, deep learning algorithms have been considered as
promising ways of developing enabling technologies for URLLC in future 6G
networks. This tutorial illustrates how domain knowledge (models, analytical
tools, and optimization frameworks) of communications and networking can be
integrated into different kinds of deep learning algorithms for URLLC. We first
provide some background of URLLC and review promising network architectures and
deep learning frameworks for 6G. To better illustrate how to improve learning
algorithms with domain knowledge, we revisit model-based analytical tools and
cross-layer optimization frameworks for URLLC. Following that, we examine the
potential of applying supervised/unsupervised deep learning and deep
reinforcement learning in URLLC and summarize related open problems. Finally,
we provide simulation and experimental results to validate the effectiveness of
different learning algorithms and discuss future directions.Comment: This work has been accepted by Proceedings of the IEE