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

Robust Sub-meter Level Indoor Localization - A Logistic Regression Approach

Indoor localization becomes a raising demand in our daily lives. Due to the massive deployment in the indoor environment nowadays, WiFi systems have been applied to high accurate localization recently. Although the traditional model based localization scheme can achieve sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is significant. To address this issue, the model-free localization approach using deep learning framework has been proposed and the classification based technique is applied. In this paper, instead of using classification based mechanism, we propose to use a logistic regression based scheme under the deep learning framework, which is able to achieve sub-meter level accuracy (97.2cm medium distance error) in the standard laboratory environment and maintain reasonable online prediction overhead under the single WiFi AP settings. We hope the proposed logistic regression based scheme can shed some light on the model-free localization technique and pave the way for the practical deployment of deep learning based WiFi localization systems.Comment: 6 pages, 5 figures, conferenc