307 research outputs found
A Survey on Multi-AP Coordination Approaches over Emerging WLANs: Future Directions and Open Challenges
Recent advancements in wireless local area network (WLAN) technology include
IEEE 802.11be and 802.11ay, often known as Wi-Fi 7 and WiGig, respectively. The
goal of these developments is to provide Extremely High Throughput (EHT) and
low latency to meet the demands of future applications like as 8K videos,
augmented and virtual reality, the Internet of Things, telesurgery, and other
developing technologies. IEEE 802.11be includes new features such as 320 MHz
bandwidth, multi-link operation, Multi-user Multi-Input Multi-Output,
orthogonal frequency-division multiple access, and Multiple-Access Point
(multi-AP) coordination (MAP-Co) to achieve EHT. With the increase in the
number of overlapping APs and inter-AP interference, researchers have focused
on studying MAP-Co approaches for coordinated transmission in IEEE 802.11be,
making MAP-Co a key feature of future WLANs. Moreover, similar issues may arise
in EHF bands WLAN, particularly for standards beyond IEEE 802.11ay. This has
prompted researchers to investigate the implementation of MAP-Co over future
802.11ay WLANs. Thus, in this article, we provide a comprehensive review of the
state-of-the-art MAP-Co features and their shortcomings concerning emerging
WLAN. Finally, we discuss several novel future directions and open challenges
for MAP-Co.Comment: The reason for the replacement of the previous version of the paper
is due to a change in the author's list. As a result, a new version has been
created, which serves as the final draft version before acceptance. This
updated version contains all the latest changes and improvements made to the
pape
A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks
Massive MIMO is a promising technology to connect very large numbers of
energy constrained nodes, as it offers both extensive spatial multiplexing and
large array gain. A challenge resides in partitioning the many nodes in groups
that can communicate simultaneously such that the mutual interference is
minimized. We here propose node partitioning strategies that do not require
full channel state information, but rather are based on nodes' respective
directional channel properties. In our considered scenarios, these typically
have a time constant that is far larger than the coherence time of the channel.
We developed both an optimal and an approximation algorithm to partition users
based on directional channel properties, and evaluated them numerically. Our
results show that both algorithms, despite using only these directional channel
properties, achieve similar performance in terms of the minimum
signal-to-interference-plus-noise ratio for any user, compared with a reference
method using full channel knowledge. In particular, we demonstrate that
grouping nodes with related directional properties is to be avoided. We hence
realise a simple partitioning method requiring minimal information to be
collected from the nodes, and where this information typically remains stable
over a long term, thus promoting their autonomy and energy efficiency
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
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