109 research outputs found
Low-Rank Channel Estimation for Millimeter Wave and Terahertz Hybrid MIMO Systems
Massive multiple-input multiple-output (MIMO) is one of the fundamental technologies for 5G and beyond. The increased number of antenna elements at both the transmitter and the receiver translates into a large-dimension channel matrix. In addition, the power requirements for the massive MIMO systems are high, especially when fully digital transceivers are deployed. To address this challenge, hybrid analog-digital transceivers are considered a viable alternative. However, for hybrid systems, the number of observations during each channel use is reduced. The high dimensions of the channel matrix and the reduced number of observations make the channel estimation task challenging. Thus, channel estimation may require increased training overhead and higher computational complexity.
The need for high data rates is increasing rapidly, forcing a shift of wireless communication towards higher frequency bands such as millimeter Wave (mmWave) and terahertz (THz). The wireless channel at these bands is comprised of only a few dominant paths. This makes the channel sparse in the angular domain and the resulting channel matrix has a low rank. This thesis aims to provide channel estimation solutions benefiting from the low rankness and sparse nature of the channel. The motivation behind this thesis is to offer a desirable trade-off between training overhead and computational complexity while providing a desirable estimate of the channel
Matrix Factorization Based Blind Bayesian Receiver for Grant-Free Random Access in mmWave MIMO mMTC
Grant-free random access is promising for massive connectivity with sporadic
transmissions in massive machine type communications (mMTC), where the
hand-shaking between the access point (AP) and users is skipped, leading to
high access efficiency. In grant-free random access, the AP needs to identify
the active users and perform channel estimation and signal detection.
Conventionally, pilot signals are required for the AP to achieve user activity
detection and channel estimation before active user signal detection, which may
still result in substantial overhead and latency. In this paper, to further
reduce the overhead and latency, we explore the problem of grant-free random
access without the use of pilot signals in a millimeter wave (mmWave) multiple
input and multiple output (MIMO) system, where the AP performs blind joint user
activity detection, channel estimation and signal detection (UACESD). We show
that the blind joint UACESD can be formulated as a constrained composite matrix
factorization problem, which can be solved by exploiting the structures of the
channel matrix and signal matrix. Leveraging our recently developed unitary
approximate message passing based matrix factorization (UAMP-MF) algorithm, we
design a message passing based Bayesian algorithm to solve the blind joint
UACESD problem. Extensive simulation results demonstrate the effectiveness of
the blind grant-free random access scheme
Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of
resources to facilitate the design and implementation, from user access to data
transmission. Emerging applications and scenarios in the sixth generation (6G)
wireless systems will require massive connectivity and transmission of a deluge
of data, which calls for more flexibility in the design concept that goes
beyond orthogonality. Furthermore, recent advances in signal processing and
learning have attracted considerable attention, as they provide promising
approaches to various complex and previously intractable problems of signal
processing in many fields. This article provides an overview of research
efforts to date in the field of signal processing and learning for
next-generation multiple access, with an emphasis on massive random access and
non-orthogonal multiple access. The promising interplay with new technologies
and the challenges in learning-based NGMA are discussed
MMV-Based Sequential AoA and AoD Estimation for Millimeter Wave MIMO Channels
The fact that the millimeter-wave (mmWave) multiple-input multiple-output
(MIMO) channel has sparse support in the spatial domain has motivated recent
compressed sensing (CS)-based mmWave channel estimation methods, where the
angles of arrivals (AoAs) and angles of departures (AoDs) are quantized using
angle dictionary matrices. However, the existing CS-based methods usually
obtain the estimation result through one-stage channel sounding that have two
limitations: (i) the requirement of large-dimensional dictionary and (ii)
unresolvable quantization error. These two drawbacks are irreconcilable;
improvement of the one implies deterioration of the other. To address these
challenges, we propose, in this paper, a two-stage method to estimate the AoAs
and AoDs of mmWave channels. In the proposed method, the channel estimation
task is divided into two stages, Stage I and Stage II. Specifically, in Stage
I, the AoAs are estimated by solving a multiple measurement vectors (MMV)
problem. In Stage II, based on the estimated AoAs, the receive sounders are
designed to estimate AoDs. The dimension of the angle dictionary in each stage
can be reduced, which in turn reduces the computational complexity
substantially. We then analyze the successful recovery probability (SRP) of the
proposed method, revealing the superiority of the proposed framework over the
existing one-stage CS-based methods. We further enhance the reconstruction
performance by performing resource allocation between the two stages. We also
overcome the unresolvable quantization error issue present in the prior
techniques by applying the atomic norm minimization method to each stage of the
proposed two-stage approach. The simulation results illustrate the
substantially improved performance with low complexity of the proposed
two-stage method.Comment: Accepted by IEEE Transactions on Communication
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