245 research outputs found
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
ADMM-based Detector for Large-scale MIMO Code-domain NOMA Systems
Large-scale multi-input multi-output (MIMO) code domain non-orthogonal
multiple access (CD-NOMA) techniques are one of the potential candidates to
address the next-generation wireless needs such as massive connectivity, and
high reliability. This work focuses on two primary CD-NOMA techniques:
sparse-code multiple access (SCMA) and dense-code multiple access (DCMA). One
of the primary challenges in implementing MIMO-CD-NOMA systems is designing the
optimal detector with affordable computation cost and complexity. This paper
proposes an iterative linear detector based on the alternating direction method
of multipliers (ADMM). First, the maximum likelihood (ML) detection problem is
converted into a sharing optimization problem. The set constraint in the ML
detection problem is relaxed into the box constraint sharing problem. An
alternative variable is introduced via the penalty term, which compensates for
the loss incurred by the constraint relaxation. The system models, i.e., the
relation between the input signal and the received signal, are reformulated so
that the proposed sharing optimization problem can be readily applied.
The ADMM is a robust algorithm to solve the sharing problem in a distributed
manner. The proposed detector leverages the distributive nature to reduce
per-iteration cost and time. An ADMM-based linear detector is designed for
three MIMO-CD-NOMA systems: single input multi output CD-NOMA (SIMO-CD-NOMA),
spatial multiplexing CD-NOMA (SMX-CD-NOMA), and spatial modulated CD-NOMA
(SM-CD-NOMA). The impact of various system parameters and ADMM parameters on
computational complexity and symbol error rate (SER) has been thoroughly
examined through extensive Monte Carlo simulations
Unfolding for Joint Channel Estimation and Symbol Detection in MIMO Communication Systems
This paper proposes a Joint Channel Estimation and Symbol Detection (JED)
scheme for Multiple-Input Multiple-Output (MIMO) wireless communication
systems. Our proposed method for JED using Alternating Direction Method of
Multipliers (JED-ADMM) and its model-based neural network version JED using
Unfolded ADMM (JED-U-ADMM) markedly improve the symbol detection performance
over JED using Alternating Minimization (JED-AM) for a range of MIMO antenna
configurations. Both proposed algorithms exploit the non-smooth constraint,
that occurs as a result of the Quadrature Amplitude Modulation (QAM) data
symbols, to effectively improve the performance using the ADMM iterations. The
proposed unfolded network JED-U-ADMM consists of a few trainable parameters and
requires a small training set. We show the efficacy of the proposed methods for
both uncorrelated and correlated MIMO channels. For certain configurations, the
gain in SNR for a desired BER of for the proposed JED-ADMM and
JED-U-ADMM is upto dB and is also accompanied by a significant reduction in
computational complexity of upto , depending on the MIMO configuration,
as compared to the complexity of JED-AM.Comment: 14 pages, 19 figures, submitted to IEEE Transactions on Signal
Processin
A Simple ADMM Solution To Sparse-Modeling-Based Detectors For Massive MIMO Systems
International audienceWe give a simple yet efficient Alternating Direction Method of Multipliers algorithm for solving sparse-modeling-based detectors [7, 9] for massive MIMO systems. Our solution relies on a special reformulation of the associated optimization problem by describing the constraints as a Cartesian power of the probability simplex. Simulation results show that the proposed algorithm is as accurate as the best known solvers (interior point methods), while its complexity remains linear with respect to the size of the system
- …