9 research outputs found
Broadband Channel Estimation for Intelligent Reflecting Surface Aided mmWave Massive MIMO Systems
This paper investigates the broadband channel estimation (CE) for intelligent
reflecting surface (IRS)-aided millimeter-wave (mmWave) massive MIMO systems.
The CE for such systems is a challenging task due to the large dimension of
both the active massive MIMO at the base station (BS) and passive IRS. To
address this problem, this paper proposes a compressive sensing (CS)-based CE
solution for IRS-aided mmWave massive MIMO systems, whereby the angular channel
sparsity of large-scale array at mmWave is exploited for improved CE with
reduced pilot overhead. Specifically, we first propose a downlink pilot
transmission framework. By designing the pilot signals based on the prior
knowledge that the line-of-sight dominated BS-to-IRS channel is known, the
high-dimensional channels for BS-to-user and IRS-to-user can be jointly
estimated based on CS theory. Moreover, to efficiently estimate broadband
channels, a distributed orthogonal matching pursuit algorithm is exploited,
where the common sparsity shared by the channels at different subcarriers is
utilized. Additionally, the redundant dictionary to combat the power leakage is
also designed for the enhanced CE performance. Simulation results demonstrate
the effectiveness of the proposed scheme.Comment: 6 pages, 4 figures. Accepted by IEEE International Conference on
Communications (ICC) 2020, Dublin, Irelan
Quasi-Synchronous Random Access for Massive MIMO-Based LEO Satellite Constellations
Low earth orbit (LEO) satellite constellation-enabled communication networks
are expected to be an important part of many Internet of Things (IoT)
deployments due to their unique advantage of providing seamless global
coverage. In this paper, we investigate the random access problem in massive
multiple-input multiple-output-based LEO satellite systems, where the
multi-satellite cooperative processing mechanism is considered. Specifically,
at edge satellite nodes, we conceive a training sequence padded multi-carrier
system to overcome the issue of imperfect synchronization, where the training
sequence is utilized to detect the devices' activity and estimate their
channels. Considering the inherent sparsity of terrestrial-satellite links and
the sporadic traffic feature of IoT terminals, we utilize the orthogonal
approximate message passing-multiple measurement vector algorithm to estimate
the delay coefficients and user terminal activity. To further utilize the
structure of the receive array, a two-dimensional estimation of signal
parameters via rotational invariance technique is performed for enhancing
channel estimation. Finally, at the central server node, we propose a majority
voting scheme to enhance activity detection by aggregating backhaul information
from multiple satellites. Moreover, multi-satellite cooperative linear data
detection and multi-satellite cooperative Bayesian dequantization data
detection are proposed to cope with perfect and quantized backhaul,
respectively. Simulation results verify the effectiveness of our proposed
schemes in terms of channel estimation, activity detection, and data detection
for quasi-synchronous random access in satellite systems.Comment: 38 pages, 16 figures. This paper has been accepted by IEEE JSAC SI on
3GPP Technologies: 5G-Advanced and Beyond. Copyright may be transferred
without notice, after which this version may no longer be accessibl
Quasi-synchronous Random Access for Massive MIMO Based LEO Satellite Constellation
peer reviewedLow earth orbit (LEO) satellite constellationenabled
communication network is considered to be an indispensable
part to realize the Internet of Things (IoT) due to its unique
advantages in providing seamless global coverage. In this paper,
we investigate the random access problem in massive multipleinput
multiple-output based LEO satellite communication systems.
To deal with grant-free random access in IoT, a training
sequence padded multi-carrier system is designed with tolerance
to imperfect synchronization. Specifically, we construct a multisatellite
system where a training sequence is utilized to perform
joint activity detection and channel estimation (JADCE) at the
edge satellite nodes. Considering the sparse feature of terrestrialsatellite
link and sporadic transmission of user terminals (UTs),
we propose a compressed sensing-based algorithm to estimate
the delay tap and UT activities. To further utilize the structured
feature of the receive array, a 2-D ESPRIT algorithm is performed
for augmented parameterized channel estimation. Finally,
enhanced activity detection and data detection are performed
at the central node by leveraging the aggregated information
from edge nodes. To achieve reliable information transmission, we
propose a centralized interference cancellation and data detection
method, where both the high spatial correlation among UTs and
quantized backhaul are taken into account. Simulation results
verify the effectiveness of our proposed scheme in terms of
channel estimation, activity detection, and data detection for
quasi-synchronous random access satellite system
Closed-loop sparse channel estimation for wideband millimeter-wave full-dimensional MIMO systems
This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoding matrix is designed at base station (BS) for channel sounding, and receive combining matrices at user devices (UDs) are designed whereby the hybrid array is visualized as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-ofdepartures (AoDs), are exploited to design multi-beam transmit precoding matrices at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs, whereby the MDU-ESPRIT algorithm is used based on the designed receive combining matrix at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over state-of-the-art approaches