4,142 research outputs found
Blind Estimation of Effective Downlink Channel Gains in Massive MIMO
We consider the massive MIMO downlink with time-division duplex (TDD)
operation and conjugate beamforming transmission. To reliably decode the
desired signals, the users need to know the effective channel gain. In this
paper, we propose a blind channel estimation method which can be applied at the
users and which does not require any downlink pilots. We show that our proposed
scheme can substantially outperform the case where each user has only
statistical channel knowledge, and that the difference in performance is
particularly large in certain types of channel, most notably keyhole channels.
Compared to schemes that rely on downlink pilots, our proposed scheme yields
more accurate channel estimates for a wide range of signal-to-noise ratios and
avoid spending time-frequency resources on pilots.Comment: IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP) 201
Multi-Cell Massive MIMO in LoS
We consider a multi-cell Massive MIMO system in a line-of-sight (LoS)
propagation environment, for which each user is served by one base station,
with no cooperation among the base stations. Each base station knows the
channel between its service antennas and its users, and uses these channels for
precoding and decoding. Under these assumptions we derive explicit downlink and
uplink effective SINR formulas for maximum-ratio (MR) processing and
zero-forcing (ZF) processing. We also derive formulas for power control to meet
pre-determined SINR targets. A numerical example demonstrating the usage of the
derived formulas is provided.Comment: IEEE Global Communications Conference (GLOBECOM) 201
Massive MU-MIMO Downlink TDD Systems with Linear Precoding and Downlink Pilots
We consider a massive MU-MIMO downlink time-division duplex system where a
base station (BS) equipped with many antennas serves several single-antenna
users in the same time-frequency resource. We assume that the BS uses linear
precoding for the transmission. To reliably decode the signals transmitted from
the BS, each user should have an estimate of its channel. In this work, we
consider an efficient channel estimation scheme to acquire CSI at each user,
called beamforming training scheme. With the beamforming training scheme, the
BS precodes the pilot sequences and forwards to all users. Then, based on the
received pilots, each user uses minimum mean-square error channel estimation to
estimate the effective channel gains. The channel estimation overhead of this
scheme does not depend on the number of BS antennas, and is only proportional
to the number of users. We then derive a lower bound on the capacity for
maximum-ratio transmission and zero-forcing precoding techniques which enables
us to evaluate the spectral efficiency taking into account the spectral
efficiency loss associated with the transmission of the downlink pilots.
Comparing with previous work where each user uses only the statistical channel
properties to decode the transmitted signals, we see that the proposed
beamforming training scheme is preferable for moderate and low-mobility
environments.Comment: Allerton Conference on Communication, Control, and Computing,
Urbana-Champaign, Illinois, Oct. 201
Aspects of Favorable Propagation in Massive MIMO
Favorable propagation, defined as mutual orthogonality among the
vector-valued channels to the terminals, is one of the key properties of the
radio channel that is exploited in Massive MIMO. However, there has been little
work that studies this topic in detail. In this paper, we first show that
favorable propagation offers the most desirable scenario in terms of maximizing
the sum-capacity. One useful proxy for whether propagation is favorable or not
is the channel condition number. However, this proxy is not good for the case
where the norms of the channel vectors may not be equal. For this case, to
evaluate how favorable the propagation offered by the channel is, we propose a
``distance from favorable propagation'' measure, which is the gap between the
sum-capacity and the maximum capacity obtained under favorable propagation.
Secondly, we examine how favorable the channels can be for two extreme
scenarios: i.i.d. Rayleigh fading and uniform random line-of-sight (UR-LoS).
Both environments offer (nearly) favorable propagation. Furthermore, to analyze
the UR-LoS model, we propose an urns-and-balls model. This model is simple and
explains the singular value spread characteristic of the UR-LoS model well
Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise
Cell-free Massive MIMO (multiple-input multiple-output) refers to a
distributed Massive MIMO system where all the access points (APs) cooperate to
coherently serve all the user equipments (UEs), suppress inter-cell
interference and mitigate the multiuser interference. Recent works demonstrated
that, unlike co-located Massive MIMO, the \textit{channel hardening} is, in
general, less pronounced in cell-free Massive MIMO, thus there is much to
benefit from estimating the downlink channel. In this study, we investigate the
gain introduced by the downlink beamforming training, extending the previously
proposed analysis to non-orthogonal uplink and downlink pilots. Assuming
single-antenna APs, conjugate beamforming and independent Rayleigh fading
channel, we derive a closed-form expression for the per-user achievable
downlink rate that addresses channel estimation errors and pilot contamination
both at the AP and UE side. The performance evaluation includes max-min
fairness power control, greedy pilot assignment methods, and a comparison
between achievable rates obtained from different capacity-bounding techniques.
Numerical results show that downlink beamforming training, although increases
pilot overhead and introduces additional pilot contamination, improves
significantly the achievable downlink rate. Even for large number of APs, it is
not fully efficient for the UE relying on the statistical channel state
information for data decoding.Comment: Published in IEEE Transactions on Wireless Communications on August
14, 2019. {\copyright} 2019 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other use
How Much Do Downlink Pilots Improve Cell-Free Massive MIMO?
In this paper, we analyze the benefits of including downlink pilots in a
cell-free massive MIMO system. We derive an approximate per-user achievable
downlink rate for conjugate beamforming processing, which takes into account
both uplink and downlink channel estimation errors, and power control. A
performance comparison is carried out, in terms of per-user net throughput,
considering cell-free massive MIMO operation with and without downlink
training, for different network densities. We take also into account the
performance improvement provided by max-min fairness power control in the
downlink. Numerical results show that, exploiting downlink pilots, the
performance can be considerably improved in low density networks over the
conventional scheme where the users rely on statistical channel knowledge only.
In high density networks, performance improvements are moderate.Comment: 7 pages, 5 figures. IEEE Global Communications Conference 2016
(GLOBECOM). Accepte
Multipair Full-Duplex Relaying with Massive Arrays and Linear Processing
We consider a multipair decode-and-forward relay channel, where multiple
sources transmit simultaneously their signals to multiple destinations with the
help of a full-duplex relay station. We assume that the relay station is
equipped with massive arrays, while all sources and destinations have a single
antenna. The relay station uses channel estimates obtained from received pilots
and zero-forcing (ZF) or maximum-ratio combining/maximum-ratio transmission
(MRC/MRT) to process the signals. To reduce significantly the loop interference
effect, we propose two techniques: i) using a massive receive antenna array; or
ii) using a massive transmit antenna array together with very low transmit
power at the relay station. We derive an exact achievable rate in closed-form
for MRC/MRT processing and an analytical approximation of the achievable rate
for ZF processing. This approximation is very tight, especially for large
number of relay station antennas. These closed-form expressions enable us to
determine the regions where the full-duplex mode outperforms the half-duplex
mode, as well as, to design an optimal power allocation scheme. This optimal
power allocation scheme aims to maximize the energy efficiency for a given sum
spectral efficiency and under peak power constraints at the relay station and
sources. Numerical results verify the effectiveness of the optimal power
allocation scheme. Furthermore, we show that, by doubling the number of
transmit/receive antennas at the relay station, the transmit power of each
source and of the relay station can be reduced by 1.5dB if the pilot power is
equal to the signal power, and by 3dB if the pilot power is kept fixed, while
maintaining a given quality-of-service
Ubiquitous Cell-Free Massive MIMO Communications
Since the first cellular networks were trialled in the 1970s, we have
witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic
growth has been managed by a combination of wider bandwidths, refined radio
interfaces, and network densification, namely increasing the number of antennas
per site. Due its cost-efficiency, the latter has contributed the most. Massive
MIMO (multiple-input multiple-output) is a key 5G technology that uses massive
antenna arrays to provide a very high beamforming gain and spatially
multiplexing of users, and hence, increases the spectral and energy efficiency.
It constitutes a centralized solution to densify a network, and its performance
is limited by the inter-cell interference inherent in its cell-centric design.
Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive
MIMO system implementing coherent user-centric transmission to overcome the
inter-cell interference limitation in cellular networks and provide additional
macro-diversity. These features, combined with the system scalability inherent
in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO
from prior coordinated distributed wireless systems. In this article, we
investigate the enormous potential of this promising technology while
addressing practical deployment issues to deal with the increased
back/front-hauling overhead deriving from the signal co-processing.Comment: Published in EURASIP Journal on Wireless Communications and
Networking on August 5, 201
Cell-Free Massive MIMO versus Small Cells
A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a
very large number of distributed access points (APs)which simultaneously serve
a much smaller number of users over the same time/frequency resources based on
directly measured channel characteristics. The APs and users have only one
antenna each. The APs acquire channel state information through time-division
duplex operation and the reception of uplink pilot signals transmitted by the
users. The APs perform multiplexing/de-multiplexing through conjugate
beamforming on the downlink and matched filtering on the uplink. Closed-form
expressions for individual user uplink and downlink throughputs lead to max-min
power control algorithms. Max-min power control ensures uniformly good service
throughout the area of coverage. A pilot assignment algorithm helps to mitigate
the effects of pilot contamination, but power control is far more important in
that regard.
Cell-Free Massive MIMO has considerably improved performance with respect to
a conventional small-cell scheme, whereby each user is served by a dedicated
AP, in terms of both 95%-likely per-user throughput and immunity to shadow
fading spatial correlation. Under uncorrelated shadow fading conditions, the
cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user
throughput over the small-cell scheme, and 10-fold improvement when shadow
fading is correlated.Comment: EEE Transactions on Wireless Communications, accepted for publicatio
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