89 research outputs found
An Overview of Physical Layer Security with Finite-Alphabet Signaling
Providing secure communications over the physical layer with the objective of
achieving perfect secrecy without requiring a secret key has been receiving
growing attention within the past decade. The vast majority of the existing
studies in the area of physical layer security focus exclusively on the
scenarios where the channel inputs are Gaussian distributed. However, in
practice, the signals employed for transmission are drawn from discrete signal
constellations such as phase shift keying and quadrature amplitude modulation.
Hence, understanding the impact of the finite-alphabet input constraints and
designing secure transmission schemes under this assumption is a mandatory step
towards a practical implementation of physical layer security. With this
motivation, this article reviews recent developments on physical layer security
with finite-alphabet inputs. We explore transmit signal design algorithms for
single-antenna as well as multi-antenna wiretap channels under different
assumptions on the channel state information at the transmitter. Moreover, we
present a review of the recent results on secure transmission with discrete
signaling for various scenarios including multi-carrier transmission systems,
broadcast channels with confidential messages, cognitive multiple access and
relay networks. Throughout the article, we stress the important behavioral
differences of discrete versus Gaussian inputs in the context of the physical
layer security. We also present an overview of practical code construction over
Gaussian and fading wiretap channels, and we discuss some open problems and
directions for future research.Comment: Submitted to IEEE Communications Surveys & Tutorials (1st Revision
Rate Splitting with Finite Constellations: The Benefits of Interference Exploitation vs Suppression
Rate-Splitting (RS) has been proposed recently to enhance the performance of
multi-user multiple-input multiple-output (MU-MIMO) systems. In RS, a user
message is split into a common and a private part, where the common part is
decoded by all users, while the private part is decoded only by the intended
user. In this paper, we study RS under a phase-shift keying (PSK) input
alphabet for multi-user multi-antenna system and propose a constructive
interference (CI) exploitation approach to further enhance the sum-rate
achieved by RS under PSK signaling. To that end, new analytical expressions for
the ergodic sum-rate are derived for two precoding techniques of the private
messages, namely, 1) a traditional interference suppression zero-forcing (ZF)
precoding approach, 2) a closed-form CI precoding approach. Our analysis is
presented for perfect channel state information at the transmitter (CSIT), and
is extended to imperfect CSIT knowledge. A novel power allocation strategy,
specifically suited for the finite alphabet setup, is derived and shown to lead
to superior performance for RS over conventional linear precoding not relying
on RS (NoRS). The results in this work validate the significant sum-rate gain
of RS with CI over the conventional RS with ZF and NoRS
Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO
Base station (BS) architectures for massive multi-user (MU) multiple-input
multiple-output (MIMO) wireless systems are equipped with hundreds of antennas
to serve tens of users on the same time-frequency channel. The immense number
of BS antennas incurs high system costs, power, and interconnect bandwidth. To
circumvent these obstacles, sophisticated MU precoding algorithms that enable
the use of 1-bit DACs have been proposed. Many of these precoders feature
parameters that are, traditionally, tuned manually to optimize their
performance. We propose to use deep-learning tools to automatically tune such
1-bit precoders. Specifically, we optimize the biConvex 1-bit PrecOding (C2PO)
algorithm using neural networks. Compared to the original C2PO algorithm, our
neural-network optimized (NNO-)C2PO achieves the same error-rate performance at
lower complexity. Moreover, by training NNO-C2PO for
different channel models, we show that 1-bit precoding can be made robust to
vastly changing propagation conditions
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