527 research outputs found
Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments
Hybrid massive MIMO structures with reduced hardware complexity and power
consumption have been widely studied as a potential candidate for millimeter
wave (mmWave) communications. Channel estimators that require knowledge of the
array response, such as those using compressive sensing (CS) methods, may
suffer from performance degradation when array-inherent impairments bring
unknown phase errors and gain errors to the antenna elements. In this paper, we
design matrix completion (MC)-based channel estimation schemes which are robust
against the array-inherent impairments. We first design an open-loop training
scheme that can sample entries from the effective channel matrix randomly and
is compatible with the phase shifter-based hybrid system. Leveraging the
low-rank property of the effective channel matrix, we then design a channel
estimator based on the generalized conditional gradient (GCG) framework and the
alternating minimization (AltMin) approach. The resulting estimator is immune
to array-inherent impairments and can be implemented to systems with any array
shapes for its independence of the array response. In addition, we extend our
design to sample a transformed channel matrix following the concept of
inductive matrix completion (IMC), which can be solved efficiently using our
proposed estimator and achieve similar performance with a lower requirement of
the dynamic range of the transmission power per antenna. Numerical results
demonstrate the advantages of our proposed MC-based channel estimators in terms
of estimation performance, computational complexity and robustness against
array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS
channel estimator.Comment: This work has been submitted to the IEEE for possible publication.
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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
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
Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms
Algorithms for Massive MIMO uplink detection and downlink precoding typically
rely on a centralized approach, by which baseband data from all antenna modules
are routed to a central node in order to be processed. In the case of Massive
MIMO, where hundreds or thousands of antennas are expected in the base-station,
said routing becomes a bottleneck since interconnection throughput is limited.
This paper presents a fully decentralized architecture and an algorithm for
Massive MIMO uplink detection and downlink precoding based on the Stochastic
Gradient Descent (SGD) method, which does not require a central node for these
tasks. Through a recursive approach and very low complexity operations, the
proposed algorithm provides a good trade-off between performance,
interconnection throughput and latency. Further, our proposed solution achieves
significantly lower interconnection data-rate than other architectures,
enabling future scalability.Comment: Manuscript accepted for publication in IEEE Transactions on Signal
Processin
Impact of Relay Cooperation on the Performance of Large-scale Multipair Two-way Relay Networks
We consider a multipair two-way relay communication network, where pairs of
user devices exchange information via a relay system. The communication between
users employs time division duplex, with all users transmitting simultaneously
to relays in one time slot and relays sending the processed information to all
users in the next time slot. The relay system consists of a large number of
single antenna units that can form groups. Within each group, relays exchange
channel state information (CSI), signals received in the uplink and signals
intended for downlink transmission. On the other hand, per-group CSI and
uplink/downlink signals (data) are not exchanged between groups, which perform
the data processing completely independently. Assuming that the groups perform
zero-forcing in both uplink and downlink, we derive a lower bound for the
ergodic sumrate of the described system as a function of the relay group size.
By close observation of this lower bound, it is concluded that the sumrate is
essentially independent of group size when the group size is much larger than
the number of user pairs. This indicates that a very large group of cooperating
relays can be substituted by a number of smaller groups, without incurring any
significant performance reduction. Moreover, this result implies that relay
cooperation is more efficient (in terms of resources spent on cooperation) when
several smaller relay groups are used in contrast to a single, large group.Comment: Accepted to Globecom 2018. Copyright 2018 IEE
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