6 research outputs found
On-the-fly Large-scale Channel-Gain Estimation for Massive Antenna-Array Base Stations
We propose a novel scheme for estimating the large-scale gains of the
channels between user terminals (UTs) and base stations (BSs) in a cellular
system. The scheme leverages TDD operation, uplink (UL) training by means of
properly designed non-orthogonal pilot codes, and massive antenna arrays at the
BSs. Subject to Q resource elements allocated for UL training and using the new
scheme, a BS is able to estimate the large-scale channel gains of K users
transmitting UL pilots in its cell and in nearby cells, provided K<=Q^2. Such
knowledge of the large-scale channel gains of nearby out-of-cells users can be
exploited at the BS to mitigate interference to the out-of-cell users that
experience the highest levels of interference from the BS. We investigate the
large-scale gain estimation performance provided by a variety of non-orthogonal
pilot codebook designs. Our simulations suggest that among all the code designs
considered, Grassmannian line-packing type codes yield the best large-scale
channel gain estimation performance.Comment: 6 pages, 3 figures, and published in IEEE ICC 201
Sparse Non-Negative Recovery from Biased Subgaussian Measurements using NNLS
We investigate non-negative least squares (NNLS) for the recovery of sparse
non-negative vectors from noisy linear and biased measurements. We build upon
recent results from [1] showing that for matrices whose row-span intersects the
positive orthant, the nullspace property (NSP) implies compressed sensing
recovery guarantees for NNLS. Such results are as good as for
-regularized estimators but do not require tuning parameters that
depend on the noise level. A bias in the sensing matrix improves this
auto-regularization feature of NNLS and the NSP then determines the sparse
recovery performance only. We show that NSP holds with high probability for
biased subgaussian matrices and its quality is independent of the bias.Comment: 8 pages, 3 figures (proofs simplified
Massive MIMO Unsourced Random Access
We consider an extension of the massive unsourced random access originally
proposed by Polyanskiy to the case where the receiver has a very large number
of antennas (a massive MIMO base station) and no channel state information is
given to the receiver (fully non-coherent detection). Our coding approach
borrows the concatenated coding idea from Amalladinne et. al., combined with a
novel non-Bayesian `activity detection' algorithm for massive MIMO random
access channels, that outperforms currently proposed Bayesian vector AMP (VAMP)
schemes currently proposed for activity detection, and does not suffer from the
numerical instabilities and requirement for accurate a priori statistics as
VAMP. We show that the required transmit for reliable communication
can be made arbitrarily small as the number of receiver antennas M grows
sufficiently large
On-the-fly Uplink Training and Pilot Code Sequence Design for Cellular Networks
Cellular networks of massive MIMO base-stations employing TDD/OFDM and
relying on uplink training for both downlink and uplink transmission are viewed
as an attractive candidate for 5G deployments, as they promise high area
spectral and energy efficiencies with relatively simple low-latency operation.
We investigate the use of non-orthogonal uplink pilot designs as a means for
improving the area spectral efficiency in the downlink of such massive MIMO
cellular networks. We develop a class of pilot designs that are locally
orthogonal within each cell, while maintaining low inner-product properties
between codes in different cells. Using channel estimates provided by
observations on these codes, each cell independently serves its locally active
users with MU-MIMO transmission that is also designed to mitigate interference
to a subset of `strongly interfered' out-of-cell users. As our simulation-based
analysis shows, such cellular operation based on the proposed codes yields
user-rate CDF improvement with respect to conventional operation, which can be
exploited to improve cell and/or cell-throughput performance.Comment: 9 pages, 4 figure
A New Scaling Law for Activity Detection in Massive MIMO Systems
In this paper, we study the problem of \textit{activity detection} (AD) in a
massive MIMO setup, where the Base Station (BS) has antennas. We
consider a block fading channel model where the -dim channel vector of each
user remains almost constant over a \textit{coherence block} (CB) containing
signal dimensions. We study a setting in which the number of potential
users assigned to a specific CB is much larger than the dimension of the
CB () but at each time slot only of them are
active. Most of the previous results, based on compressed sensing, require that
, which is a bottleneck in massive deployment scenarios such as
Internet-of-Things (IoT) and Device-to-Device (D2D) communication. In this
paper, we show that one can overcome this fundamental limitation when the
number of BS antennas is sufficiently large. More specifically, we derive a
\textit{scaling law} on the parameters and also
\textit{Signal-to-Noise Ratio} (SNR) under which our proposed AD scheme
succeeds. Our analysis indicates that with a CB of dimension , and a
sufficient number of BS antennas with , one can identify the
activity of active users, which is much
larger than the previous bound obtained via traditional compressed
sensing techniques. In particular, in our proposed scheme one needs to pay only
a poly-logarithmic penalty for increasing the
number of potential users , which makes it ideally suited for AD in IoT
setups. We propose low-complexity algorithms for AD and provide numerical
simulations to illustrate our results. We also compare the performance of our
proposed AD algorithms with that of other competitive algorithms in the
literature.Comment: 11 pages, 3 Figure
Non-Bayesian Activity Detection, Large-Scale Fading Coefficient Estimation, and Unsourced Random Access with a Massive MIMO Receiver
In this paper, we study the problem of user activity detection and
large-scale fading coefficient estimation in a random access wireless uplink
with a massive MIMO base station with a large number of antennas and a
large number of wireless single-antenna devices (users). We consider a block
fading channel model where the -dimensional channel vector of each user
remains constant over a coherence block containing signal dimensions in
time-frequency. In the considered setting, the number of potential users
is much larger than but at each time slot only
of them are active. Previous results, based on compressed
sensing, require that , which is a bottleneck in massive deployment
scenarios such as Internet-of-Things and unsourced random access. In this work
we show that such limitation can be overcome when the number of base station
antennas is sufficiently large. We also provide two algorithms. One is
based on Non-Negative Least-Squares, for which the above scaling result can be
rigorously proved. The other consists of a low-complexity iterative
componentwise minimization of the likelihood function of the underlying
problem. Finally, we use the discussed approximated ML algorithm as the decoder
for the inner code in a concatenated coding scheme for unsourced random access,
a grant-free uncoordinated multiple access scheme where all users make use of
the same codebook, and the massive MIMO base station must come up with the list
of transmitted messages irrespectively of the identity of the transmitters. We
show that reliable communication is possible at any provided that a
sufficiently large number of base station antennas is used, and that a sum
spectral efficiency in the order of is achievable.Comment: 58 pages, 9 figures, added references, minor corrections and edits,
extended section