First 20 Years of Green Radios

Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge

AI heralds a step-change in the performance and capability of wireless networks and other critical infrastructures. However, it may also cause irreversible environmental damage due to their high energy consumption. Here, we address this challenge in the context of 5G and beyond, where there is a complexity explosion in radio resource management (RRM). On the one hand, deep reinforcement learning (DRL) provides a powerful tool for scalable optimization for high dimensional RRM problems in a dynamic environment. On the other hand, DRL algorithms consume a high amount of energy over time and risk…

AI-Based Secure NOMA and Cognitive Radio enabled Green Communications: Channel State Information and Battery Value Uncertainties

In this paper, the security-aware robust resource allocation in energy harvesting cognitive radio networks is considered with cooperation between two transmitters while there are uncertainties in channel gains and battery energy value. To be specific, the primary access point harvests energy from the green resource and uses time switching protocol to send the energy and data towards the secondary access point (SAP). Using power-domain non-orthogonal multiple access technique, the SAP helps the primary network to improve the security of data transmission by using the frequency band of the prima…

具有少量发射射频链的绿色全息MIMO通信

Green Holographic MIMO Communications With A Few Transmit Radio Frequency Chains

本文研究在少量发射射频链条件下实现绿色全息MIMO通信，提出非均匀全息图案调制（NUHPM）方案，在不增加射频链数量的情况下通过增大天线孔径利用额外空间自由度，从而在高信噪比区域达到容量极限并显著提升性能。

RF Wireless Power Transfer: Regreening Future Networks

Green radio communication is an emerging topic since the overall footprint of information and communication technology (ICT) services is predicted to triple between 2007 and 2020. Given this research line, energy harvesting (EH) and wireless power transfer (WPT) networks can be evaluated as promising approaches. In this paper, an overview of recent trends for future green networks on the platforms of EH and WPT is provided. By rethinking the application of radio frequency (RF)-WPT, a new concept, namely green RF-WTP, is introduced. Accordingly, opening challenges and promising combinations amo…

$L_2$-Box Optimization for Green Cloud-RAN via Network Adaptation

In this paper, we propose a reformulation for the Mixed Integer Programming (MIP) problem into an exact and continuous model through using the $\ell_2$-box technique to recast the binary constraints into a box with an $\ell_2$ sphere constraint. The reformulated problem can be tackled by a dual ascent algorithm combined with a Majorization-Minimization (MM) method for the subproblems to solve the network power consumption problem of the Cloud Radio Access Network (Cloud-RAN), and which leads to solving a sequence of Difference of Convex (DC) subproblems handled by an inexact MM algorithm. Afte…

云原生O-RAN赋能的小小区网络中的绿色资源分配

Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks

本研究提出一种基于深度强化学习和迁移学习的分布式框架，在云原生O-RAN小小区网络中实现面向eMBB和URLLC用户的节能资源分配，仿真验证其能快速适应动态网络状态。