183 research outputs found
Millimeter Wave Communications with Reconfigurable Antennas
The highly sparse nature of propagation channels and the restricted use of
radio frequency (RF) chains at transceivers limit the performance of millimeter
wave (mmWave) multiple-input multiple-output (MIMO) systems. Introducing
reconfigurable antennas to mmWave can offer an additional degree of freedom on
designing mmWave MIMO systems. This paper provides a theoretical framework for
studying the mmWave MIMO with reconfigurable antennas. We present an
architecture of reconfigurable mmWave MIMO with beamspace hybrid analog-digital
beamformers and reconfigurable antennas at both the transmitter and the
receiver. We show that employing reconfigurable antennas can provide throughput
gain for the mmWave MIMO. We derive the expression for the average throughput
gain of using reconfigurable antennas, and further simplify the expression by
considering the case of large number of reconfiguration states. In addition, we
propose a low-complexity algorithm for the reconfiguration state and beam
selection, which achieves nearly the same throughput performance as the optimal
selection of reconfiguration state and beams by exhaustive search.Comment: presented at IEEE ICC 201
Reconfigurable Antennas in mmWave MIMO Systems
The key obstacle to achieving the full potential of the millimeter wave
(mmWave) band has been the poor propagation characteristics of wireless signals
in this band. One approach to overcome this issue is to use antennas that can
support higher gains while providing beam adaptability and diversity, i.e.,
reconfigurable antennas. In this article, we present a new architecture for
mmWave multiple-input multiple-output (MIMO) communications that uses a new
class of reconfigurable antennas. More specifically, the proposed lens-based
antennas can support multiple radiation patterns while using a single radio
frequency chain. Moreover, by using a beam selection network, each antenna beam
can be steered in the desired direction. Further, using the proposed
reconfigurable antenna in a MIMO architecture, we propose a new signal
processing algorithm that uses the additional degrees of freedom provided by
the antennas to overcome propagation issues at mmWave frequencies. Our
simulation results show that the proposed reconfigurable antenna MIMO
architecture significantly enhances the performance of mmWave communication
systems
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities
Recently there has been a flurry of research on the use of reconfigurable
intelligent surfaces (RIS) in wireless networks to create smart radio
environments. In a smart radio environment, surfaces are capable of
manipulating the propagation of incident electromagnetic waves in a
programmable manner to actively alter the channel realization, which turns the
wireless channel into a controllable system block that can be optimized to
improve overall system performance. In this article, we provide a tutorial
overview of reconfigurable intelligent surfaces (RIS) for wireless
communications. We describe the working principles of reconfigurable
intelligent surfaces (RIS) and elaborate on different candidate implementations
using metasurfaces and reflectarrays. We discuss the channel models suitable
for both implementations and examine the feasibility of obtaining accurate
channel estimates. Furthermore, we discuss the aspects that differentiate RIS
optimization from precoding for traditional MIMO arrays highlighting both the
arising challenges and the potential opportunities associated with this
emerging technology. Finally, we present numerical results to illustrate the
power of an RIS in shaping the key properties of a MIMO channel.Comment: to appear in the IEEE Transactions on Cognitive Communications and
Networking (TCCN
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Secure Communication for Spatially Sparse Millimeter-Wave Massive MIMO Channels via Hybrid Precoding
In this paper, we investigate secure communication over sparse millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) channels by exploiting the spatial sparsity of legitimate user's channel. We propose a secure communication scheme in which information data is precoded onto dominant angle components of the sparse channel through a limited number of radio-frequency (RF) chains, while artificial noise (AN) is broadcast over the remaining nondominant angles interfering only with the eavesdropper with a high probability. It is shown that the channel sparsity plays a fundamental role analogous to secret keys in achieving secure communication. Hence, by defining two statistical measures of the channel sparsity, we analytically characterize its impact on secrecy rate. In particular, a substantial improvement on secrecy rate can be obtained by the proposed scheme due to the uncertainty, i.e., 'entropy', introduced by the channel sparsity which is unknown to the eavesdropper. It is revealed that sparsity in the power domain can always contribute to the secrecy rate. In contrast, in the angle domain, there exists an optimal level of sparsity that maximizes the secrecy rate. The effectiveness of the proposed scheme and derived results are verified by numerical simulations
Construction and Capacity Analysis of High-Rank LoS MIMO Channels in High Speed Railway Scenarios
The validity of the maximum capacity criterion applied to realize high-rank line-of-sight (LoS) multiple-input multiple-output (MIMO) channels is investigated for high speed railway scenarios. Performance is evaluated by ergodic capacity. Numerical results demonstrate that by simply adjusting antenna spacing according to the maximum capacity criterion, significant capacity gains are achievable. We find relatively low sensitivity of the system to displacements from the optimal point and angle in relatively short range. Thus, we present two proposals to reconfigure antenna arrays so as to maximize LoS MIMO capacity in the high speed railway scenario
Sparse Array Architectures for Wireless Communication and Radar Applications
This thesis focuses on sparse array architectures for the next generation of wireless communication, known as fifth-generation (5G), and automotive radar direction-of-arrival (DOA) estimation. For both applications, array spatial resolution plays a critical role to better distinguish multiple users/sources. Two novel base station antenna (BSA) configurations and a new sparse MIMO radar, which both outperform their conventional counterparts, are proposed.\ua0We first develop a multi-user (MU) multiple-input multiple-output (MIMO) simulation platform which incorporates both antenna and channel effects based on standard network theory. The combined transmitter-channel-receiver is modeled by cascading Z-matrices to interrelate the port voltages/currents to one another in the linear network model. The herein formulated channel matrix includes physical antenna and channel effects and thus enables us to compute the actual port powers. This is in contrast with the assumptions of isotropic radiators without mutual coupling effects which are commonly being used in the Wireless Community.\ua0Since it is observed in our model that the sum-rate of a MU-MIMO system can be adversely affected by antenna gain pattern variations, a novel BSA configuration is proposed by combining field-of-view (FOV) sectorization, array panelization and array sparsification. A multi-panel BSA, equipped with sparse arrays in each panel, is presented with the aim of reducing the implementation complexities and maintaining or even improving the sum-rate.\ua0We also propose a capacity-driven array synthesis in the presence of mutual coupling for a MU-MIMO system. We show that the appearance of\ua0grating lobes is degrading the system capacity and cannot be disregarded in a MU communication, where space division\ua0multiple access (SDMA) is applied. With the aid of sparsity and aperiodicity, the adverse effects of grating lobes and mutual coupling\ua0are suppressed and capacity is enhanced. This is performed by proposing a two-phase optimization. In Phase I, the problem\ua0is relaxed to a convex optimization by ignoring the mutual coupling and weakening the constraints. The solution of Phase I\ua0is used as the initial guess for the genetic algorithm (GA) in phase II, where the mutual coupling is taken into account. The\ua0proposed hybrid algorithm outperforms the conventional GA with random initialization.\ua0A novel sparse MIMO radar is presented for high-resolution single snapshot DOA estimation. Both transmit and receive arrays are divided into two uniform arrays with increased inter-element spacings to generate two uniform sparse virtual arrays. Since virtual arrays are uniform, conventional spatial smoothing can be applied for temporal correlation suppression among sources. Afterwards, the spatially smoothed virtual arrays satisfy the co-primality concept to avoid DOA ambiguities. Physical antenna effects are incorporated in the received signal model and their effects on the DOA estimation performance are investigated
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