5,712 research outputs found
On the MIMO Capacity with Multiple Power Constraints
Themultiple-inputmultiple-output(MIMO)technology has become an essential element of modern communication systems e.g.,3G,4 Gand massive MIMOtechnology has been recently standardized i n3GPPRel-15i.e. ,New Radio(NR)to enhance the spectral efficiency or the capacity of 5G networks. Given a digital communication system, a receiver will suffer from decoding errors if the transmission rate exceeds the capacity. Therefore, the capacity of a MIMO system is an important metric to characterize the system performance. More importantly,an efficient precoder design to achieve that capacity is of great interest. This thesis is dedicated to this fundamental problem under multiple power constraints. From the theoretical perspective, capacity maximization is a classical problem. However efficient algorithms considering realistic scenarios or multiple power constraints, especially for massive MIMO application, are still sparse. In the thesis, the author has sought new methods of determining the capacity under two practical power constraints: 1) per-antenna power constraint (PAPC) 2) linear transmit covariance constraint (LTCC). In particular, the PAPC imposes an individual power limit one ach power amplifier associated with atransmit antenna, thus is much more realistic than the traditional sum power constraint (SPC) in which all transmit antennas collaborate to satisfy a predefined total power budget. In many other practical scenarios, other power constraints can be imposed on a system, not necessarily to either SPC or PAPC. To this end, LTCCs are general enough to include those constraints. In both cases, we have proposed low-complexity approaches to the considered problems and the description of them is in the following. For the problem of capacity maximization under PAPC,two closed-formlow-complexity approaches have been developed for single-user MIMO and multi-user MIMO under different MIMO channels and precoding techniques. More specifically, the first approach is based on fixed-point-iterationtosolvetheproblemdirectlyinthebroadcast channel (BC), whereas the other relies on alternating optimization (AO) together with successive convex optimization (SCA) to solve the equivalent problem in dual multiple access channel (MAC) domain. Interestingly, the latter approach is also applicable to the problem of computing capacity with LTCCs. For the special case of joint SPC and PAPC, we have also derived analytical solutions to this important problem. Last but not least, we have investigated the applications of machine learning to our capacity problems and presented some preliminary results
On the Secrecy Capacity of MIMO Wiretap Channels: Convex Reformulation and Efficient Numerical Methods
This paper presents novel numerical approaches to finding the secrecy
capacity of the multiple-input multiple-output (MIMO) wiretap channel subject
to multiple linear transmit covariance constraints, including sum power
constraint, per antenna power constraints and interference power constraint. An
analytical solution to this problem is not known and existing numerical
solutions suffer from slow convergence rate and/or high per-iteration
complexity. Deriving computationally efficient solutions to the secrecy
capacity problem is challenging since the secrecy rate is expressed as a
difference of convex functions (DC) of the transmit covariance matrix, for
which its convexity is only known for some special cases. In this paper we
propose two low-complexity methods to compute the secrecy capacity along with a
convex reformulation for degraded channels. In the first method we capitalize
on the accelerated DC algorithm which requires solving a sequence of convex
subproblems, for which we propose an efficient iterative algorithm where each
iteration admits a closed-form solution. In the second method, we rely on the
concave-convex equivalent reformulation of the secrecy capacity problem which
allows us to derive the so-called partial best response algorithm to obtain an
optimal solution. Notably, each iteration of the second method can also be done
in closed form. The simulation results demonstrate a faster convergence rate of
our methods compared to other known solutions. We carry out extensive numerical
experiments to evaluate the impact of various parameters on the achieved
secrecy capacity
Iterative Mode-Dropping for the Sum Capacity of MIMO-MAC with Per-Antenna Power Constraint
We propose an iterative mode-dropping algorithm that optimizes input signals
to achieve the sum capacity of the MIMO-MAC with per-antenna power constraint.
The algorithm successively optimizes each user's input covariance matrix by
applying mode-dropping to the equivalent single-user MIMO rate maximization
problem. Both analysis and simulation show fast convergence. We then use the
algorithm to briefly highlight the difference in MIMO-MAC capacities under sum
and per-antenna power constraints.Comment: 6 pages double-column, 5 figure
Physical Layer Service Integration in 5G: Potentials and Challenges
High transmission rate and secure communication have been identified as the
key targets that need to be effectively addressed by fifth generation (5G)
wireless systems. In this context, the concept of physical-layer security
becomes attractive, as it can establish perfect security using only the
characteristics of wireless medium. Nonetheless, to further increase the
spectral efficiency, an emerging concept, termed physical-layer service
integration (PHY-SI), has been recognized as an effective means. Its basic idea
is to combine multiple coexisting services, i.e., multicast/broadcast service
and confidential service, into one integral service for one-time transmission
at the transmitter side. This article first provides a tutorial on typical
PHY-SI models. Furthermore, we propose some state-of-the-art solutions to
improve the overall performance of PHY-SI in certain important communication
scenarios. In particular, we highlight the extension of several concepts
borrowed from conventional single-service communications, such as artificial
noise (AN), eigenmode transmission etc., to the scenario of PHY-SI. These
techniques are shown to be effective in the design of reliable and robust
PHY-SI schemes. Finally, several potential research directions are identified
for future work.Comment: 12 pages, 7 figure
Beyond Massive-MIMO: The Potential of Data-Transmission with Large Intelligent Surfaces
In this paper, we consider the potential of data-transmission in a system
with a massive number of radiating and sensing elements, thought of as a
contiguous surface of electromagnetically active material. We refer to this as
a large intelligent surface (LIS). The "LIS" is a newly proposed concept, which
conceptually goes beyond contemporary massive MIMO technology, that arises from
our vision of a future where man-made structures are electronically active with
integrated electronics and wireless communication making the entire environment
"intelligent".
We consider capacities of single-antenna autonomous terminals communicating
to the LIS where the entire surface is used as a receiving antenna array. Under
the condition that the surface-area is sufficiently large, the received signal
after a matched-filtering (MF) operation can be closely approximated by a
sinc-function-like intersymbol interference (ISI) channel. We analyze the
capacity per square meter (m^2) deployed surface, \hat{C}, that is achievable
for a fixed transmit power per volume-unit, \hat{P}. Moreover, we also show
that the number of independent signal dimensions per m deployed surface is
2/\lambda for one-dimensional terminal-deployment, and \pi/\lambda^2 per m^2
for two and three dimensional terminal-deployments. Lastly, we consider
implementations of the LIS in the form of a grid of conventional antenna
elements and show that, the sampling lattice that minimizes the surface-area of
the LIS and simultaneously obtains one signal space dimension for every spent
antenna is the hexagonal lattice. We extensively discuss the design of the
state-of-the-art low-complexity channel shortening (CS) demodulator for
data-transmission with the LIS.Comment: Submitted to IEEE Trans. on Signal Process., 30 pages, 12 figure
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