451 research outputs found
Spatial Channel Degrees of Freedom for Optimum Antenna Arrays
One of the ultimate goals of future wireless networks is to maximize data rates to accommodate bandwidth-hungry services and applications. Thus, extracting the maximum amount of information bits for given spatial constraints when designing wireless systems will be of great importance. In this paper, we present antenna array topologies that maximize the communication channel capacity for given number of array elements while occupying minimum space. Capacity is maximized via the development of an advanced particle swarm optimization (PSO) algorithm devising optimum standardized and arbitrarily-shaped antenna array topologies. Number of array elements and occupied space are informed by novel heuristic spatial degrees of freedom (SDoF) formulations which rigorously generalize existing SDoF formulas. Our generalized SDoF formulations rely on the differential entropy of three-dimensional (3D) angle of arrival (AOA) distributions and can associate the number of array elements and occupied space for any AOA distribution. The proposed analysis departs from novel closed-form spatial correlation functions (SCFs) of arbitrarily-positioned array elements for all classes of 3D multipath propagation channels, namely, isotropic, omnidirectional, and directional. Extensive simulation runs and comparisons with existing trivial solutions verify correctness of our SDoF formulations resulting in optimum antenna array topologies with maximum capacity performance and minimum space occupancy
Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions
Future wireless systems are envisioned to create an endogenously
holography-capable, intelligent, and programmable radio propagation
environment, that will offer unprecedented capabilities for high spectral and
energy efficiency, low latency, and massive connectivity. A potential and
promising technology for supporting the expected extreme requirements of the
sixth-generation (6G) communication systems is the concept of the holographic
multiple-input multiple-output (HMIMO), which will actualize holographic radios
with reasonable power consumption and fabrication cost. The HMIMO is
facilitated by ultra-thin, extremely large, and nearly continuous surfaces that
incorporate reconfigurable and sub-wavelength-spaced antennas and/or
metamaterials. Such surfaces comprising dense electromagnetic (EM) excited
elements are capable of recording and manipulating impinging fields with utmost
flexibility and precision, as well as with reduced cost and power consumption,
thereby shaping arbitrary-intended EM waves with high energy efficiency. The
powerful EM processing capability of HMIMO opens up the possibility of wireless
communications of holographic imaging level, paving the way for signal
processing techniques realized in the EM-domain, possibly in conjunction with
their digital-domain counterparts. However, in spite of the significant
potential, the studies on HMIMO communications are still at an initial stage,
its fundamental limits remain to be unveiled, and a certain number of critical
technical challenges need to be addressed. In this survey, we present a
comprehensive overview of the latest advances in the HMIMO communications
paradigm, with a special focus on their physical aspects, their theoretical
foundations, as well as the enabling technologies for HMIMO systems. We also
compare the HMIMO with existing multi-antenna technologies, especially the
massive MIMO, present various...Comment: double column, 58 page
Near-Field Communications: A Comprehensive Survey
Multiple-antenna technologies are evolving towards large-scale aperture
sizes, extremely high frequencies, and innovative antenna types. This evolution
is giving rise to the emergence of near-field communications (NFC) in future
wireless systems. Considerable attention has been directed towards this
cutting-edge technology due to its potential to enhance the capacity of
wireless networks by introducing increased spatial degrees of freedom (DoFs) in
the range domain. Within this context, a comprehensive review of the state of
the art on NFC is presented, with a specific focus on its 1) fundamental
operating principles, 2) channel modeling, 3) performance analysis, 4) signal
processing, and 5) integration with other emerging technologies. Specifically,
1) the basic principles of NFC are characterized from both physics and
communications perspectives, unveiling its unique properties in contrast to
far-field communications. 2) Based on these principles, deterministic and
stochastic near-field channel models are investigated for spatially-discrete
(SPD) and continuous-aperture (CAP) antenna arrays. 3) Rooted in these models,
existing contributions on near-field performance analysis are reviewed in terms
of DoFs/effective DoFs (EDoFs), power scaling law, and transmission rate. 4)
Existing signal processing techniques for NFC are systematically surveyed,
encompassing channel estimation, beamforming design, and low-complexity beam
training. 5) Major issues and research opportunities associated with the
integration of NFC and other emerging technologies are identified to facilitate
NFC applications in next-generation networks. Promising directions are
highlighted throughout the paper to inspire future research endeavors in the
realm of NFC.Comment: 56 pages, 23figures; submit for possible journa
Waveform Diversity and Range-Coupled Adaptive Radar Signal Processing
Waveform diversity may offer several benefits to radar systems though often at the cost of reduced sensitivity. Multi-dimensional processing schemes are known to offer many degrees of freedom, which can be exploited to suppress the ambiguity inherent to pulse compression, array processing, and Doppler frequency estimation. Spatial waveform diversity can be achieved by transmitting different but correlated waveforms from each element of an antenna array. A simple yet effective scheme is employed to transmit different waveforms in different spatial directions. A new reiterative minimum mean squared error approach entitled Space-Range Adaptive Processing, which adapts simultaneously in range and angle, is derived and shown in simulation to offer enhanced performance when spatial waveform diversity is employed relative to both conventional matched filtering and sequentially adapting in angle and then range. The same mathematical framework is utilized to develop Time-Range Adaptive Processing (TRAP) algorithm which is capable of simultaneously adapting in Doppler frequency and range. TRAP is useful when pulse-to-pulse changing of the center frequency or waveform coding is used to achieve enhanced range resolution or unambiguous ranging, respectively. The inherent computational complexity of the new multi-dimensional algorithms is addressed by segmenting the full-dimension cost functions, yielding a reduced-dimensional variants of each. Finally, a non-adaptive approach based on the multi-dimensional TRAP signal model is utilized to develop an efficient clutter cancellation technique capable of suppressing multiple range intervals of clutter when waveform diversity is applied to pulse-Doppler radar
The electronically steerable parasitic array radiator antenna for wireless communications : signal processing and emerging techniques
Smart antenna technology is expected to play an important role in future wireless
communication networks in order to use the spectrum efficiently, improve the
quality of service, reduce the costs of establishing new wireless paradigms and
reduce the energy consumption in wireless networks. Generally, smart antennas
exploit multiple widely spaced active elements, which are connected to separate
radio frequency (RF) chains. Therefore, they are only applicable to base stations
(BSs) and access points, by contrast with modern compact wireless terminals with
constraints on size, power and complexity. This dissertation considers an alternative
smart antenna system the electronically steerable parasitic array radiator
(ESPAR) which uses only a single RF chain, coupled with multiple parasitic elements.
The ESPAR antenna is of significant interest because of its
flexibility in beamforming by tuning a number of easy-to-implement reactance loads connected
to parasitic elements; however, parasitic elements require no expensive RF circuits.
This work concentrates on the study of the ESPAR antenna for compact
transceivers in order to achieve some emerging techniques in wireless communications.
The work begins by presenting the work principle and modeling of the ESPAR
antenna and describes the reactance-domain signal processing that is suited to the
single active antenna array, which are fundamental factors throughout this thesis.
The major contribution in this chapter is the adaptive beamforming method
based on the ESPAR antenna. In order to achieve fast convergent beamforming
for the ESPAR antenna, a modified minimum variance distortionless response
(MVDR) beamfomer is proposed. With reactance-domain signal processing, the
ESPAR array obtains a correlation matrix of receive signals as the input to the
MVDR optimization problem. To design a set of feasible reactance loads for a desired
beampattern, the MVDR optimization problem is reformulated as a convex
optimization problem constraining an optimized weight vector close to a feasible
solution. Finally, the necessary reactance loads are optimized by iterating the convex problem and a simple projector. In addition, the generic algorithm-based
beamforming method has also studied for the ESPAR antenna.
Blind interference alignment (BIA) is a promising technique for providing an optimal
degree of freedom in a multi-user, multiple-inputsingle-output broadcast
channel, without the requirements of channel state information at the transmitters.
Its key is antenna mode switching at the receive antenna. The ESPAR
antenna is able to provide a practical solution to beampattern switching (one
kind of antenna mode switching) for the implementation of BIA. In this chapter,
three beamforming methods are proposed for providing the required number of
beampatterns that are exploited across one super symbol for creating the channel
fluctuation patterns seen by receivers. These manually created channel
fluctuation
patterns are jointly combined with the designed spacetime precoding in order to
align the inter-user interference. Furthermore, the directional beampatterns designed
in the ESPAR antenna are demonstrated to improve the performance of
BIA by alleviating the noise amplification.
The ESPAR antenna is studied as the solution to interference mitigation in small
cell networks. Specifically, ESPARs analog beamforming presented in the previous
chapter is exploited to suppress inter-cell interference for the system scenario,
scheduling only one user to be served by each small BS at a single time. In
addition, the ESPAR-based BIA is employed to mitigate both inter-cell and intracell
interference for the system scenario, scheduling a small number of users to be
simultaneously served by each small BS for a single time.
In the cognitive radio (CR) paradigm, the ESPAR antenna is employed for spatial
spectrum sensing in order to utilize the new angle dimension in the spectrum
space besides the conventional frequency, time and space dimensions. The twostage
spatial spectrum sensing method is proposed based on the ESPAR antenna
being targeted at identifying white spectrum space, including the new angle dimension.
At the first stage, the occupancy of a specific frequency band is detected
by conventional spectrum-sensing methods, including energy detector and
eigenvalue-based methods implemented with the switched-beam ESPAR antenna. With the presence of primary users, their directions are estimated at the second
stage, by high-resolution angle-of-arrival (AoA) estimation algorithms. Specifically, the compressive sensing technology has been studied for AoA detection with
the ESPAR antenna, which is demonstrated to provide high-resolution estimation
results and even to outperform the reactance-domain multiple signal classification
Mitigating Interference with Knowledge-Aided Subarray Pattern Synthesis and Space Time Adaptive Processing
Phased arrays are essential to airborne ground moving target indication (GMTI), as they measure the spatial angle-of-arrival of the target, clutter, and interference signals. The spatial and Doppler (temporal) frequency is utilized by space-time adaptive processing (STAP) to separate and filter out the interference from the moving target returns. Achieving acceptable airborne GMTI performance often requires fairly large arrays, but the size, weight and power (SWAP) requirements, cost and complexity considerations often result in the use of subarrays. This yields an acceptable balance between cost and performance while lowering the system’s robustness to interference. This thesis proposes the use of knowledge aided adaptive radar to institute adaptive subarray nulling in concert with digital space-time adaptive processing to improve performance in the presence of substantial interference. This research expands previous work which analyzed a clutter-free airborne moving-target indication (AMTI) application of knowledge-aided subarray pattern synthesis (KASPS) [1] and updates this previous research by applying the same concept to the GMTI application with clutter and STAP
Adaptive Illumination Patterns for Radar Applications
The fundamental goal of Fully Adaptive Radar (FAR) involves full exploitation of the joint, synergistic adaptivity of the radar\u27s transmitter and receiver. Little work has been done to exploit the joint space time Degrees-of-Freedom (DOF) available via an Active Electronically Steered Array (AESA) during the radar\u27s transmit illumination cycle. This research introduces Adaptive Illumination Patterns (AIP) as a means for exploiting this previously untapped transmit DOF. This research investigates ways to mitigate clutter interference effects by adapting the illumination pattern on transmit. Two types of illumination pattern adaptivity were explored, termed Space Time Illumination Patterns (STIP) and Scene Adaptive Illumination Patterns (SAIP). Using clairvoyant knowledge, STIP demonstrates the ability to remove sidelobe clutter at user specified Doppler frequencies, resulting in optimum receiver performance using a non-adaptive receive processor. Using available database knowledge, SAIP demonstrated the ability to reduce training data heterogeneity in dense target environments, thereby greatly improving the minimum discernable velocity achieved through STAP processing
High capacity multiuser multiantenna communication techniques
One of the main issues involved in the development of future wireless communication systems is the multiple access technique used to efficiently share the available spectrum among users. In rich multipath environment, spatial dimension can be exploited to meet the increasing number of users and their demands without consuming extra bandwidth and power. Therefore, it is utilized in the multiple-input multiple-output (MIMO) technology to increase the spectral efficiency significantly. However, multiuser MIMO (MU-MIMO) systems are still challenging to be widely adopted in next generation standards. In this thesis, new techniques are proposed to increase the channel and user capacity and improve the error performance of MU-MIMO over Rayleigh fading channel environment.
For realistic system design and performance evaluation, channel correlation is considered as one of the main channel impurities due its severe influence on capacity and reliability. Two simple methods called generalized successive coloring technique (GSCT) and generalized iterative coloring technique (GICT) are proposed for accurate generation of correlated Rayleigh fading channels (CRFC). They are designed to overcome the shortcomings of existing methods by avoiding factorization of desired covariance matrix of the Gaussian samples. The superiority of these techniques is demonstrated by extensive simulations of different practical system scenarios.
To mitigate the effects of channel correlations, a novel constellation constrained MU-MIMO (CC-MU-MIMO) scheme is proposed using transmit signal design and maximum likelihood joint detection (MLJD) at the receiver. It is designed to maximize the channel capacity and error performance based on principles of maximizing the minimum Euclidean distance (dmin) of composite received signals. Two signal design methods named as unequal power allocation (UPA) and rotation constellation (RC) are utilized to resolve the detection ambiguity caused by correlation. Extensive analysis and simulations demonstrate the effectiveness of considered scheme compared with conventional MU-MIMO. Furthermore, significant gain in SNR is achieved particularly in moderate to high correlations which have direct impact to maintain high user capacity.
A new efficient receive antenna selection (RAS) technique referred to as phase difference based selection (PDBS) is proposed for single and multiuser MIMO systems to maximize the capacity over CRFC. It utilizes the received signal constellation to select the subset of antennas with highest (dmin) constellations due to its direct impact on the capacity and BER performance. A low complexity algorithm is designed by employing the Euclidean norm of channel matrix rows with their corresponding phase differences. Capacity analysis and simulation results show that PDBS outperforms norm based selection (NBS) and near to optimal selection (OS) for all correlation and SNR values. This technique provides fast RAS to capture most of the gains promised by multiantenna systems over different channel conditions.
Finally, novel group layered MU-MIMO (GL-MU-MIMO) scheme is introduced to exploit the available spectrum for higher user capacity with affordable complexity. It takes the advantages of spatial difference among users and power control at base station to increase the number of users beyond the available number of RF chains. It is achieved by dividing the users into two groups according to their received power, high power group (HPG) and low power group (LPG). Different configurations of low complexity group layered multiuser detection (GL-MUD) and group power allocation ratio (η) are utilized to provide a valuable tradeoff between complexity and overall system performance. Furthermore, RAS diversity is incorporated by using NBS and a new selection algorithm called HPG-PDBS to increase the channel capacity and enhance the error performance. Extensive analysis and simulations demonstrate the superiority of proposed scheme compared with conventional MU-MIMO. By using appropriate value of (η), it shows higher sum rate capacity and substantial increase in the user capacity up to two-fold at target BER and SNR values
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