148 research outputs found
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
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