89 research outputs found
Compressive Random Access Using A Common Overloaded Control Channel
We introduce a "one shot" random access procedure where users can send a
message without a priori synchronizing with the network. In this procedure a
common overloaded control channel is used to jointly detect sparse user
activity and sparse channel profiles. The detected information is subsequently
used to demodulate the data in dedicated frequency slots. We analyze the system
theoretically and provide a link between achievable rates and standard
compressing sensing estimates in terms of explicit expressions and scaling
laws. Finally, we support our findings with simulations in an LTE-A-like
setting allowing "one shot" sparse random access of 100 users in 1ms.Comment: 6 pages, 3 figures, published at Globecom 201
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
Reliable recovery of hierarchically sparse signals for Gaussian and Kronecker product measurements
We propose and analyze a solution to the problem of recovering a block sparse
signal with sparse blocks from linear measurements. Such problems naturally
emerge inter alia in the context of mobile communication, in order to meet the
scalability and low complexity requirements of massive antenna systems and
massive machine-type communication. We introduce a new variant of the Hard
Thresholding Pursuit (HTP) algorithm referred to as HiHTP. We provide both a
proof of convergence and a recovery guarantee for noisy Gaussian measurements
that exhibit an improved asymptotic scaling in terms of the sampling complexity
in comparison with the usual HTP algorithm. Furthermore, hierarchically sparse
signals and Kronecker product structured measurements naturally arise together
in a variety of applications. We establish the efficient reconstruction of
hierarchically sparse signals from Kronecker product measurements using the
HiHTP algorithm. Additionally, we provide analytical results that connect our
recovery conditions to generalized coherence measures. Again, our recovery
results exhibit substantial improvement in the asymptotic sampling complexity
scaling over the standard setting. Finally, we validate in numerical
experiments that for hierarchically sparse signals, HiHTP performs
significantly better compared to HTP.Comment: 11+4 pages, 5 figures. V3: Incomplete funding information corrected
and minor typos corrected. V4: Change of title and additional author Axel
Flinth. Included new results on Kronecker product measurements and relations
of HiRIP to hierarchical coherence measures. Improved presentation of general
hierarchically sparse signals and correction of minor typo
On Improving Throughput of Multichannel ALOHA using Preamble-based Exploration
Machine-type communication (MTC) has been extensively studied to provide
connectivity for devices and sensors in the Internet-of-Thing (IoT). Thanks to
the sparse activity, random access, e.g., ALOHA, is employed for MTC to lower
signaling overhead. In this paper, we propose to adopt exploration for
multichannel ALOHA by transmitting preambles before transmitting data packets
in MTC, and show that the maximum throughput can be improved by a factor of 2 -
exp(-1) = 1.632, In the proposed approach, a base station (BS) needs to send
the feedback information to active users to inform the numbers of transmitted
preambles in multiple channels, which can be reliably estimated as in
compressive random access. A steady-state analysis is also performed with fast
retrial, which shows that the probability of packet collision becomes lower
and, as a result, the delay outage probability is greatly reduced for a lightly
loaded system. Simulation results also confirm the results from analysis.Comment: 10 pages, 7 figures, to appear in the Journal of Communications and
Networks. arXiv admin note: substantial text overlap with arXiv:2001.1111
One-Shot Messaging at Any Load Through Random Sub-Channeling in OFDM
Compressive Sensing has well boosted massive random access protocols over the
last decade. In this paper we apply an orthogonal FFT basis as it is used in
OFDM, but subdivide its image into so-called sub-channels and let each
sub-channel take only a fraction of the load. In a random fashion the
subdivision is consecutively applied over a suitable number of time-slots.
Within the time-slots the users will not change their sub-channel assignment
and send in parallel the data. Activity detection is carried out jointly across
time-slots in each of the sub-channels. For such system design we derive three
rather fundamental results: i) First, we prove that the subdivision can be
driven to the extent that the activity in each sub-channel is sparse by design.
An effect that we call sparsity capture effect. ii) Second, we prove that
effectively the system can sustain any overload situation relative to the FFT
dimension, i.e. detection failure of active and non-active users can be kept
below any desired threshold regardless of the number of users. The only price
to pay is delay, i.e. the number of time-slots over which cross-detection is
performed. We achieve this by jointly exploring the effect of measure
concentration in time and frequency and careful system parameter scaling. iii)
Third, we prove that parallel to activity detection active users can carry one
symbol per pilot resource and time-slot so it supports so-called one-shot
messaging.
The key to proving these results are new concentration results for sequences
of randomly sub-sampled FFTs detecting the sparse vectors "en bloc".
Eventually, we show by simulations that the system is scalable resulting in a
coarsely 30-fold capacity increase compared to standard OFDM
Towards Massive Connectivity Support for Scalable mMTC Communications in 5G networks
The fifth generation of cellular communication systems is foreseen to enable
a multitude of new applications and use cases with very different requirements.
A new 5G multiservice air interface needs to enhance broadband performance as
well as provide new levels of reliability, latency and supported number of
users. In this paper we focus on the massive Machine Type Communications (mMTC)
service within a multi-service air interface. Specifically, we present an
overview of different physical and medium access techniques to address the
problem of a massive number of access attempts in mMTC and discuss the protocol
performance of these solutions in a common evaluation framework
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