1,413 research outputs found
Millimeter-wave Wireless LAN and its Extension toward 5G Heterogeneous Networks
Millimeter-wave (mmw) frequency bands, especially 60 GHz unlicensed band, are
considered as a promising solution for gigabit short range wireless
communication systems. IEEE standard 802.11ad, also known as WiGig, is
standardized for the usage of the 60 GHz unlicensed band for wireless local
area networks (WLANs). By using this mmw WLAN, multi-Gbps rate can be achieved
to support bandwidth-intensive multimedia applications. Exhaustive search along
with beamforming (BF) is usually used to overcome 60 GHz channel propagation
loss and accomplish data transmissions in such mmw WLANs. Because of its short
range transmission with a high susceptibility to path blocking, multiple number
of mmw access points (APs) should be used to fully cover a typical target
environment for future high capacity multi-Gbps WLANs. Therefore, coordination
among mmw APs is highly needed to overcome packet collisions resulting from
un-coordinated exhaustive search BF and to increase the total capacity of mmw
WLANs. In this paper, we firstly give the current status of mmw WLANs with our
developed WiGig AP prototype. Then, we highlight the great need for coordinated
transmissions among mmw APs as a key enabler for future high capacity mmw
WLANs. Two different types of coordinated mmw WLAN architecture are introduced.
One is the distributed antenna type architecture to realize centralized
coordination, while the other is an autonomous coordination with the assistance
of legacy Wi-Fi signaling. Moreover, two heterogeneous network (HetNet)
architectures are also introduced to efficiently extend the coordinated mmw
WLANs to be used for future 5th Generation (5G) cellular networks.Comment: 18 pages, 24 figures, accepted, invited paper
AirSync: Enabling Distributed Multiuser MIMO with Full Spatial Multiplexing
The enormous success of advanced wireless devices is pushing the demand for
higher wireless data rates. Denser spectrum reuse through the deployment of
more access points per square mile has the potential to successfully meet the
increasing demand for more bandwidth. In theory, the best approach to density
increase is via distributed multiuser MIMO, where several access points are
connected to a central server and operate as a large distributed multi-antenna
access point, ensuring that all transmitted signal power serves the purpose of
data transmission, rather than creating "interference." In practice, while
enterprise networks offer a natural setup in which distributed MIMO might be
possible, there are serious implementation difficulties, the primary one being
the need to eliminate phase and timing offsets between the jointly coordinated
access points.
In this paper we propose AirSync, a novel scheme which provides not only time
but also phase synchronization, thus enabling distributed MIMO with full
spatial multiplexing gains. AirSync locks the phase of all access points using
a common reference broadcasted over the air in conjunction with a Kalman filter
which closely tracks the phase drift. We have implemented AirSync as a digital
circuit in the FPGA of the WARP radio platform. Our experimental testbed,
comprised of two access points and two clients, shows that AirSync is able to
achieve phase synchronization within a few degrees, and allows the system to
nearly achieve the theoretical optimal multiplexing gain. We also discuss MAC
and higher layer aspects of a practical deployment. To the best of our
knowledge, AirSync offers the first ever realization of the full multiuser MIMO
gain, namely the ability to increase the number of wireless clients linearly
with the number of jointly coordinated access points, without reducing the per
client rate.Comment: Submitted to Transactions on Networkin
Cross-layer wireless bit rate adaptation
This paper presents SoftRate, a wireless bit rate adaptation protocol that is responsive to rapidly varying channel conditions. Unlike previous work that uses either frame receptions or signal-to-noise ratio (SNR) estimates to select bit rates, SoftRate uses confidence information calculated by the physical layer and exported to higher layers via the SoftPHY interface to estimate the prevailing channel bit error rate (BER). Senders use this BER estimate, calculated over each received packet (even when the packet has no bit errors), to pick good bit rates. SoftRate's novel BER computation works across different wireless environments and hardware without requiring any retraining. SoftRate also uses abrupt changes in the BER estimate to identify interference, enabling it to reduce the bit rate only in response to channel errors caused by attenuation or fading. Our experiments conducted using a software radio prototype show that SoftRate achieves 2X higher throughput than popular frame-level protocols such as SampleRate and RRAA. It also achieves 20% more throughput than an SNR-based protocol trained on the operating environment, and up to 4X higher throughput than an untrained SNR-based protocol. The throughput gains using SoftRate stem from its ability to react to channel variations within a single packet-time and its robustness to collision losses.National Science Foundation (U.S.) (Grant CNS-0721702)National Science Foundation (U.S.) (Grant CNS-0520032)Foxconn International Holdings Ltd
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Situation-Aware Rate and Power Adaptation Techniques for IEEE 802.11
The current generation of IEEE 802.11 Wireless Local Area Networks (WLANs) provide multiple data rates from which the different physical (PHY) layers may choose. The rate adaptation algorithm (RAA) is an essential component of 802.11 WLANs which completely determines the data rate a device may use. Some of the key challenges facing data rate selection are the constantly varying wireless channel, selecting the data rate that will result in the maximum throughput, assessing the conditions based on limited feedback and estimating the link conditions at the receiver.
Current RAAs lack the ability to sense their environment and adapt accordingly. 802.11 WLANs are deployed in many locations and use the same technique to choose the data rate in all locations and situations. Therefore, these RAAs suffer from the inability to adapt the method they use to choose the data transmission rate. In this thesis, a new RAA for 802.11 WLANs is proposed which provides an answer to the many challenges faced by RAAs. The proposed RAA is termed SARA which stands for Situation-Aware Rate Adaptation, and combines the use of the received signal strength and packet error rate to enable situational awareness. SARA adapts to the current environmental situation experienced at the moment to rapidly take advantage of changing channel conditions.
In addition to SARA, a method to optimize the transmission power for, but not limited to, IEEE 802.11 WLANs is proposed which can determine the minimum transmission power required by a station (STA) or base station (BS) for successful transmission of a data packet. The technique reduces the transmission power to the minimum level based on the current situation while maintaining QoS constraints. The method employs a Binary Search to quickly determine the minimum transmission power with low complexity and delay. Such a technique is useful to conserve battery life in mobile devices for 802.11 WLANs.
Both algorithms are implemented on an Atheros device driver for the FreeBSD operating system. SARA is compared to the benchmark algorithm SampleRate while an estimate of the energy consumed as well as the energy saved is provided for the minimum transmission power determination
A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends
This paper examines the security vulnerabilities and threats imposed by the
inherent open nature of wireless communications and to devise efficient defense
mechanisms for improving the wireless network security. We first summarize the
security requirements of wireless networks, including their authenticity,
confidentiality, integrity and availability issues. Next, a comprehensive
overview of security attacks encountered in wireless networks is presented in
view of the network protocol architecture, where the potential security threats
are discussed at each protocol layer. We also provide a survey of the existing
security protocols and algorithms that are adopted in the existing wireless
network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term
evolution (LTE) systems. Then, we discuss the state-of-the-art in
physical-layer security, which is an emerging technique of securing the open
communications environment against eavesdropping attacks at the physical layer.
We also introduce the family of various jamming attacks and their
counter-measures, including the constant jammer, intermittent jammer, reactive
jammer, adaptive jammer and intelligent jammer. Additionally, we discuss the
integration of physical-layer security into existing authentication and
cryptography mechanisms for further securing wireless networks. Finally, some
technical challenges which remain unresolved at the time of writing are
summarized and the future trends in wireless security are discussed.Comment: 36 pages. Accepted to Appear in Proceedings of the IEEE, 201
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