122 research outputs found
Interference Alignment (IA) and Coordinated Multi-Point (CoMP) with IEEE802.11ac feedback compression: testbed results
We have implemented interference alignment (IA) and joint transmission
coordinated multipoint (CoMP) on a wireless testbed using the feedback
compression scheme of the new 802.11ac standard. The performance as a function
of the frequency domain granularity is assessed. Realistic throughput gains are
obtained by probing each spatial modulation stream with ten different coding
and modulation schemes. The gain of IA and CoMP over TDMA MIMO is found to be
26% and 71%, respectively under stationary conditions. In our dense indoor
office deployment, the frequency domain granularity of the feedback can be
reduced down to every 8th subcarrier (2.5MHz), without sacrificing performance.Comment: To appear in ICASSP 201
DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning
We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP)
which leverages standard-compliant beamforming feedback matrices to
authenticate MU-MIMO Wi-Fi devices on the move. By capturing unique
imperfections in off-the-shelf radio circuitry, RFP techniques can identify
wireless devices directly at the physical layer, allowing low-latency
low-energy cryptography-free authentication. However, existing Wi-Fi RFP
techniques are based on software-defined radio (SDRs), which may ultimately
prevent their widespread adoption. Moreover, it is unclear whether existing
strategies can work in the presence of MU-MIMO transmitters - a key technology
in modern Wi-Fi standards. Conversely from prior work, DeepCSI does not require
SDR technologies and can be run on any low-cost Wi-Fi device to authenticate
MU-MIMO transmitters. Our key intuition is that imperfections in the
transmitter's radio circuitry percolate onto the beamforming feedback matrix,
and thus RFP can be performed without explicit channel state information (CSI)
computation. DeepCSI is robust to inter-stream and inter-user interference
being the beamforming feedback not affected by those phenomena. We extensively
evaluate the performance of DeepCSI through a massive data collection campaign
performed in the wild with off-the-shelf equipment, where 10 MU-MIMO Wi-Fi
radios emit signals in different positions. Experimental results indicate that
DeepCSI correctly identifies the transmitter with an accuracy of up to 98%. The
identification accuracy remains above 82% when the device moves within the
environment. To allow replicability and provide a performance benchmark, we
pledge to share the 800 GB datasets - collected in static and, for the first
time, dynamic conditions - and the code database with the community.Comment: To be presented at the 42nd IEEE International Conference on
Distributed Computing Systems (ICDCS), Bologna, Italy, July 10-13, 202
An Overview on IEEE 802.11bf: WLAN Sensing
With recent advancements, the wireless local area network (WLAN) or wireless
fidelity (Wi-Fi) technology has been successfully utilized to realize sensing
functionalities such as detection, localization, and recognition. However, the
WLANs standards are developed mainly for the purpose of communication, and thus
may not be able to meet the stringent requirements for emerging sensing
applications. To resolve this issue, a new Task Group (TG), namely IEEE
802.11bf, has been established by the IEEE 802.11 working group, with the
objective of creating a new amendment to the WLAN standard to meet advanced
sensing requirements while minimizing the effect on communications. This paper
provides a comprehensive overview on the up-to-date efforts in the IEEE
802.11bf TG. First, we introduce the definition of the 802.11bf amendment and
its formation and standardization timeline. Next, we discuss the WLAN sensing
use cases with the corresponding key performance indicator (KPI) requirements.
After reviewing previous WLAN sensing research based on communication-oriented
WLAN standards, we identify their limitations and underscore the practical need
for the new sensing-oriented amendment in 802.11bf. Furthermore, we discuss the
WLAN sensing framework and procedure used for measurement acquisition, by
considering both sensing at sub-7GHz and directional multi-gigabit (DMG)
sensing at 60 GHz, respectively, and address their shared features,
similarities, and differences. In addition, we present various candidate
technical features for IEEE 802.11bf, including waveform/sequence design,
feedback types, as well as quantization and compression techniques. We also
describe the methodologies and the channel modeling used by the IEEE 802.11bf
TG for evaluation. Finally, we discuss the challenges and future research
directions to motivate more research endeavors towards this field in details.Comment: 31 pages, 25 figures, this is a significant updated version of
arXiv:2207.0485
On an HARQ-based Coordinated Multi-point Network using Dynamic Point Selection
This paper investigates the performance of coordinated multi-point (CoMP) networks in the presence of hybrid automatic repeat request (HARQ) feedback. With an information theoretic point of view, the throughput and the outage probability of different HARQ protocols are studied for slow-fading channels. The results are compared with the ones obtained in the presence of repetition codes and basic HARQ, or when there is no channel state information available at the base stations. The analytical and numerical results demonstrate the efficiency of the CoMP-HARQ techniques in different conditions
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