A Prototype Performance Analysis for V2V Communications using USRP-based Software Defined Radio Platform

Autonomous driving is usually recognized as a promising technology to replace human drivers in the near future. To guarantee the safety performance in the daily life scenario, multiple-car intelligence with high quality inter-vehicle communication capability is necessary in general. In this paper, to figure out the potential practical issues in the vehicle-to-vehicle transmission, we present a software defined radio platform for V2V communication using universal software radio peripheral (USRP). Based on the LTE framework, we modify the frame structure, the signal processing mechanisms and the resource allocation schemes to emulate the updated LTE-V standard and generate the corresponding numerical results based on the real measured signals. As shown through some empirical studies, one to four dB back-off is in general required to guarantee the reliability performance for V2V communication environments.Comment: 5 pages, 6 figures, conferenc

Energy-Efficient Subchannel and Power Allocation for HetNets Based on Convolutional Neural Network

Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 6.76% of its CPU runtime.Comment: 5 pages, 7 figures, VTC2019-Sprin

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

Distributed Coordinated Multicell Beamforming for Wireless Cellular Networks Powered by Renewables: A Stochastic ADMM Approach

The integration of renewable energy sources (RES) has facilitated efficient and sustainable resource allocation for wireless communication systems. In this paper, a novel framework is introduced to develop coordinated multicell beamforming (CMBF) design for wireless cellular networks powered by a smart microgrid, where the BSs are equipped with RES harvesting devices and can perform two-way (i.e., buying/selling) energy trading with the main grid. To this end, new models are put forth to account for the stochastic RES harvesting, two-way energy trading, and conditional value-at-risk (CVaR) based energy transaction cost. Capitalizing on these models, we propose a distributed CMBF solution to minimize the grid-wide transaction cost subject to user quality-of-service (QoS) constraints. Specifically, relying on state-of-the-art optimization tools, we show that the relevant task can be formulated as a convex problem that is well suited for development of a distributed solver. To cope with stochastic availability of the RES, the stochastic alternating direction method of multipliers (ADMM) is then leveraged to develop a novel distributed CMBF scheme. It is established that the proposed scheme is guaranteed to yield the optimal CMBF solution, with only local channel state information available at each BS and limited information exchange among the BSs. Numerical results are provided to corroborate the merits of the proposed scheme.Comment: 11 pages, 6 figure

6G: Connecting Everything by 1000 Times Price Reduction

The commercial deployment of 5G communication networks makes the industry and academia to seriously consider the possible solutions for the next generation. Although the conventional approach indicates that 6G vision and requirements can be figured out by simply multiplying a certain amount of magnitude on top of the previous generations, we argue in this article that 1000 times price reduction from the customer's view point is the key to success. Guided by this vision, we categorize the current candidate technologies in a well organized manner and select AI-assisted intelligent communications as an example to elaborate the drive-force behind. Although it is impossible to identify every detail of 6G during the current time frame, we believe this article will help to eliminate the technical uncertainties and aggregate the efforts towards key breakthrough innovations for 6G.Comment: Accepted by IEEE Open Journal of Vehicular Technology (OJVT

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

Passive TCP Identification for Wired and WirelessNetworks: A Long-Short Term Memory Approach

Transmission control protocol (TCP) congestion control is one of the key techniques to improve network performance. TCP congestion control algorithm identification (TCP identification) can be used to significantly improve network efficiency. Existing TCP identification methods can only be applied to limited number of TCP congestion control algorithms and focus on wired networks. In this paper, we proposed a machine learning based passive TCP identification method for wired and wireless networks. After comparing among three typical machine learning models, we concluded that the 4-layers Long Short Term Memory (LSTM) model achieves the best identification accuracy. Our approach achieves better than 98% accuracy in wired and wireless networks and works for newly proposed TCP congestion control algorithms

Energy Efficient Iterative Waterfilling for the MIMO Broadcasting Channels

Optimizing energy efficiency (EE) for the MIMO broadcasting channels (BC) is considered in this paper, where a practical power model is taken into account. Although the EE of the MIMO BC is non-concave, we reformulate it as a quasiconcave function based on the uplink-downlink duality. After that, an energy efficient iterative waterfilling scheme is proposed based on the block-coordinate ascent algorithm to obtain the optimal transmission policy efficiently, and the solution is proved to be convergent. Through simulations, we validate the efficiency of the proposed scheme and discuss the system parameters' effect on the EE.Comment: 6 pages, 4 figures, accepted in IEEE proc. of WCNC 201

An Analytical Framework for Delay Optimal Mobile Edge Deployment in Wireless Networks

The emerging edge caching provides an effective way to reduce service delay for mobile users. However, due to high deployment cost of edge hosts, a practical problem is how to achieve minimum delay under a proper edge deployment strategy. In this letter, we provide an analytical framework for delay optimal mobile edge deployment in a partially connected wireless network, where the request files can be cached at the edge hosts and cooperatively transmitted through multiple base stations. In order to deal with the heterogeneous transmission requirements, we separate the entire transmission into backhaul and wireless phases, and propose average user normalized delivery time (AUNDT) as the performance metric. On top of that, we characterize the trade-off relations between the proposed AUNDT and other network deployment parameters. Using the proposed analytical framework, we are able to provide the optimal mobile edge deployment strategy in terms of AUNDT, which provides a useful guideline for future mobile edge deployment

Age of Information Optimized MAC in V2X Sidelink via Piggyback-Based Collaboration

Real-time status update in future vehicular networks is vital to enable control-level cooperative autonomous driving. Cellular Vehicle-to-Everything (C-V2X), as one of the most promising vehicular wireless technologies, adopts a Semi-Persistent Scheduling (SPS) based Medium-Access-Control (MAC) layer protocol for its sidelink communications. Despite the recent and ongoing efforts to optimize SPS, very few work has considered the status update performance of SPS. In this paper, Age of Information (AoI) is first leveraged to evaluate the MAC layer performance of C-V2X sidelink. Critical issues of SPS, i.e., persistent packet collisions and Half-Duplex (HD) effects, are identified to hinder its AoI performance. Therefore, a piggyback-based collaboration method is proposed accordingly, whereby vehicles collaborate to inform each other of potential collisions and collectively afford HD errors, while entailing only a small signaling overhead. Closed-form AoI performance is derived for the proposed scheme, optimal configurations for key parameters are hence calculated, and the convergence property is proved for decentralized implementation. Simulation results show that compared with the standardized SPS and its state-of-the-art enhancement schemes, the proposed scheme shows significantly better performance, not only in terms of AoI, but also of conventional metrics such as transmission reliability.Comment: Submitted to IEEE TWC for possible publicatio