179 research outputs found
Gaussian Message Passing for Overloaded Massive MIMO-NOMA
This paper considers a low-complexity Gaussian Message Passing (GMP) scheme
for a coded massive Multiple-Input Multiple-Output (MIMO) systems with
Non-Orthogonal Multiple Access (massive MIMO-NOMA), in which a base station
with antennas serves sources simultaneously in the same frequency.
Both and are large numbers, and we consider the overloaded cases
with . The GMP for MIMO-NOMA is a message passing algorithm operating
on a fully-connected loopy factor graph, which is well understood to fail to
converge due to the correlation problem. In this paper, we utilize the
large-scale property of the system to simplify the convergence analysis of the
GMP under the overloaded condition. First, we prove that the \emph{variances}
of the GMP definitely converge to the mean square error (MSE) of Linear Minimum
Mean Square Error (LMMSE) multi-user detection. Secondly, the \emph{means} of
the traditional GMP will fail to converge when . Therefore, we propose and derive a new
convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the
LMMSE multi-user detection performance for any , and show that it
has a faster convergence speed than the traditional GMP with the same
complexity. Finally, numerical results are provided to verify the validity and
accuracy of the theoretical results presented.Comment: Accepted by IEEE TWC, 16 pages, 11 figure
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
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
Message Passing-Based Joint User Activity Detection and Channel Estimation for Temporally-Correlated Massive Access
This paper studies the user activity detection and channel estimation problem
in a temporally-correlated massive access system where a very large number of
users communicate with a base station sporadically and each user once activated
can transmit with a large probability over multiple consecutive frames. We
formulate the problem as a dynamic compressed sensing (DCS) problem to exploit
both the sparsity and the temporal correlation of user activity. By leveraging
the hybrid generalized approximate message passing (HyGAMP) framework, we
design a computationally efficient algorithm, HyGAMP-DCS, to solve this
problem. In contrast to only exploit the historical estimations, the proposed
algorithm performs bidirectional message passing between the neighboring frames
for activity likelihood update to fully exploit the temporally-correlated user
activities. Furthermore, we develop an expectation maximization HyGAMP-DCS
(EM-HyGAMP-DCS) algorithm to adaptively learn the hyperparameters during the
estimation procedure when the system statistics are unknown. In particular, we
propose to utilize the analysis tool of state evolution to find the appropriate
hyperparameter initialization of EM-HyGAMP-DCS. Simulation results demonstrate
that our proposed algorithms can significantly improve the user activity
detection accuracy and reduce the channel estimation error.Comment: 31 pages, 14 figures, minor revisio
- …