The opening of the millimeter wave (mmWave) spectrum bands for 5G communications has motivated the need for novel spectrum sharing solutions at these high frequencies. In fact, reconfigurable intelligent surfaces (RISs) have recently emerged to enable spectrum sharing while enhancing the incumbents’ quality-of-service (QoS). Nonetheless, co-existence over mm Wave bands remains persistently challenging due to their unfavorable propagation characteristics. Hence, initiating mmWave spectrum sharing requires the RIS to further assist in improving the QoS over mmWave bands without jeopardizing spectrum sharing demands. In this paper, a novel simultaneous transmitting and reflecting RIS (STAR-RIS)-aided solution to enable mmWave spectrum sharing is proposed. In particular, the transmitting and reflecting abilities of the STAR-MS are leveraged to tackle the mmWave spectrum sharing and QoS requirements separately. The STAR-RIS-enabled spectrum sharing problem between a primary network (e.g. a radar transmit-receive pair) and a secondary network is formulated as an optimization problem whose goal is to maximize the downlink sum-rate over a secondary multiple-input-single-output (MISO) network, while limiting interference over a primary network. Moreover, the STAR-MS response coefficients and beamforming matrix in the secondary network are jointly optimized. To solve this non-convex problem, an alternating iterative algorithm is employed, where the STAR-RIS response coefficients and beamforming matrix are obtained using the successive convex approximation method. Simulation results show that the proposed solution outperforms conventional RIS schemes for mmWave spectrum sharing by achieving a 14.57% spectral efficiency gain.

Conventional radio-frequency (RF) sensing systems rely on either frequency diversity or spatial diversity to ensure high sensing accuracy. Such reliance introduces several practical limitations that hinder the pervasive deployment of existing solutions. To circumvent this prevalent reliance, we present MetaSense, a system that leverages antenna pattern diversity for fine-grained RF sensing. MetaSense incorporates the dynamic metasurface antenna (DMA) and the auxiliary-assisted ensemble multimask learning (AEMML) framework in its design. The DMA is a novel type of antenna that can provide a diverse set of uncorrelated radiation patterns in a low-cost and low-complexity manner. The AEMML is a quality-aware learning framework that can dynamically assess and aggregate the heterogeneous channel measurements from different antenna patterns to ensure high sensing accuracy. It also incorporates a transfer learning model that allows it to generalize to new sensing conditions with few training instances required. We prototype MetaSense and demonstrate its effectiveness on a writing motion recognition task using a custom-designed 2-D DMA. The results show that MetaSense achieves 92% to 98% accuracy in classifying ten miniature writing motions, outperforming a nontunable antenna by 20% in all scenarios. Moreover, when deployed in new sensing positions where limited training instances are available, MetaSense requires as few as five training instances per class to achieve over 90% accuracy.

The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) techniques plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We present a model for DMAs which accounts for the configurable frequency selective profile of its metamaterial elements, resulting in a spectrally flexible hybrid structure. We then exploit previous results in task-based quantization to show characterized the achievable OFDM recovery accuracy for a given DMA configuration in the presence of bit-constrained ADCs, and propose methods for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that by properly exploiting the spectral diversity of DMAs, notable performance gains are obtained over existing designs of conventional hybrid architectures, demonstrating the potential of DMAs for realizing high performance massive antenna arrays of reduced cost and power consumption.

The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) systems plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We exploit previous results in task-based quantization to show how DMAs can be configured to improve recovery in the presence of constrained ADCs, and propose an algorithm for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that the DMA-based receiver is capable of accurately recovering OFDM signals, and that its performance is comparable to receivers operating without bit limitations, while being significantly less costly and more power efficient.

Massive multiple-input multiple-output (MIMO) communications are the focus of considerable interest in recent years. While theoretical gains of such massive MIMO have been established, implementing MIMO systems with large-scale antenna arrays in practice is challenging. Among the practical difficulties associated with massive MIMO implementations are increased cost, power consumption, and physical size. In this work we study the implementation of massive MIMO antenna arrays using dynamic metasurface antennas (DMAs), an emerging technology which inherently handles the aforementioned challenges. DMAs realize planar large-scale arrays of tunable antenna elements, and can adaptively incorporate compression and analog combining in the physical antenna structure, thus reducing cost and power consumption. We first propose a mathematical model for massive MIMO systems with DMAs and discuss their constraints compared to ideal antenna arrays. Then, we characterize the fundamental limits of the resulting systems, and propose an algorithm for designing practical DMAs to approach these limits. Our numerical results indicate that the performance of practical DMA-based massive MIMO systems is comparable with ideal antenna arrays.

Massive multiple-input-multiple-output (MIMO) communications are the focus of considerable interest in recent years. While the theoretical gains of massive MIMO have been established, implementing MIMO systems with large-scale antenna arrays in practice is challenging. Among the practical challenges associated with massive MIMO systems are increased cost, power consumption, and physical size. In this paper, we study the implementation of massive MIMO antenna arrays using dynamic metasurface antennas (DMAs), an emerging technology which inherently handles the aforementioned challenges. Specifically, DMAs realize large-scale planar antenna arrays and can adaptively incorporate signal processing methods such as compression and analog combining in the physical antenna structure, thus reducing the cost and power consumption. First, we propose a mathematical model for massive MIMO systems with DMAs and discuss their constraints compared to ideal antenna arrays. Then, we characterize the fundamental limits of uplink communications with the resulting systems and propose two algorithms for designing practical DMAs for approaching these limits. Our numerical results indicate that the proposed approaches result in practical massive MIMO systems whose performance is comparable to that achievable with ideal antenna arrays.

We propose a spatial multiplexing system using reconfigurable cavity-backed metasurface antennas. The metasurface antennas consist of a printed cavity with dynamically tunable metamaterial radiators patterned on one side and fed by multiple radio frequency ports on the other side (each port representing one communication node), forming a shared aperture. By individual tuning of the radiators, the antennas can generate steerable, concurrent beams that can be adapted to the properties of multiple-input-multiple-output (MIMO) channels. In this paper, we present a 2 × 2 MIMO system with simulated metasurface antennas as transmit and receive antennas operating at 5.9 GHz. We demonstrate that the flexibility in beamforming supported by the metasurface antennas can be used to achieve low spatial correlation and high SNR gain in clustered MIMO channels, leading to a significant improvement of the channel capacity. Numerical studies show 2.36-fold, 2.11-fold enhancements of capacity in MIMO channels with one and two clusters, respectively, compared with an MIMO system consisting of linear dipoles. The MIMO system based on the metasurface antennas can be low cost, low profile, and low power. The metasurface antenna thus has potential applications in small cell networks requiring high data rate under bandwidth, energy, and cost constraints.

The growing need for high-performance imaging tools for terrorist threat detection and medical diagnosis has led to the development of new active architectures in the microwave and millimeter range. Notably, multiple-input multiple-output systems can meet the resolution constraints imposed by these applications by creating large, synthetic radiating apertures with a limited number of antennas used independently in transmitting and receiving signals. However, the implementation of such systems is coupled with strong constraints in the software layer, requiring the development of reconstruction techniques capable of interrogating the observed scene by optimizing both the resolution of images reconstructed in two or three dimensions and the associated computation times. In this paper, we first review the formalisms and constraints associated with each application by taking stock of efficient processing techniques based on spectral decompositions, and then, we present a new technique called the transverse spectrum deconvolution range migration algorithm allowing us to carry out reconstructions that are both faster and more accurate than with conventional Fourier domain processing techniques. This paper is particularly relevant to the development of new computational imaging tools that require, even more pronouncedly than in the case of conventional architectures, fast image computing techniques despite a very large number of radiating elements interrogating the scene to be imaged.

To maintain sufficient signal-to-noise ratio (SNR) for image reconstruction and image interpretation, conventional synthetic aperture radar (SAR) systems must trade off resolution and scene size. This paper proposes a new SAR mode of operation, which improves resolution while maintaining good SNR and a large scene size. It leverages the unique properties of dynamic metasurface antennas (MSAs) to subsample a large virtual beamwidth utilizing multiple small distinct antenna beams. Due to this parallelization in scene sampling, the constraints on the azimuth sampling rate can be relaxed while maintaining an aliasing-free cross range. Due to the versatile properties of MSAs and their cost effective manufacturing process, this paper proposes SAR systems, which can obtain high resolution images over a wide scene size with lower cost and complexity than competing approaches. Point-spread functions and proof-of-concept SAR simulations are shown to verify this approach. In addition, laboratory experiments using a commercial prototype MSA are presented, which show an improvement of 62% in cross-range resolution of the proposed approach, compared with the cross-range resolution of stripmap mode SAR with the same aperture.

Transformation optics is an emerging technique for the design of advanced electromagnetic media. Transformation optical devices exploit the form invariance of Maxwell's equations, allowing geometry to play the dominant role in the design process rather than traditional wave or ray optics. The use of coordinate transformations vastly eases the burden of design for a large class of devices, though at the expense of increasing the complexity of the underlying materials used. Although the required constitutive parameters of a transformation optical structure can be challenging—inherently anisotropic and spatially varying, with both magnetic and electric response—nevertheless the parameter requirements can often be met or approximated through the use of artificially structured metamaterials. Here, we review the basic concepts associated with transformation optics and provide several examples to illustrate its application.

The capture probability for signals over Rician fading channels (with equal mean power) is analyzed. Expressions for probability density function and cumulative distribution function for power of n interferers are derived, validating with a known expression for Rayleigh fading. These equations are applied to slotted ALOHA, showing an increase in throughput.

This paper discusses experience with synthesis from a Verilog writing style using encapsulated modules. The method is shown to be capable of significant advantages in reduction of code complexity, re-use of submodules, and automatic inference of control. In order to pass synthesis and low level simulation, care must be taken in the translation of the encapsulated modules through an intermediate style accessible to industry synthesizers. If the encapsulated modules are edge activated then the control points need to be staggered in time through the clock cycle as control is passed down through the hierarchy. Examples are given of a such an intermediate style which is acceptable to synthesis and low level simulation. A conclusion discusses other implications of adapting the objective style to hardware design.

The paper provides recommendations for the enhancement and protection of the Defense Information System Network (DISN) for disaster recovery and contingencies. The DISN is a worldwide Defense Department network designed to serve consolidated and integrated communication requirements of the defense services and agencies. The ability to recover from disasters and allow for contingencies must be inherent in the design of this network. Basic design aspects of the DISN are described, along with several complementary programs for disaster recovery planning being implemented by the Office of the Manager, National Communications System (OMNCS). Both hostile and nonhostile threats are examined to set the stage for recommendations that follow. Techniques for disaster and contingency preparedness are described that will improve DISN survivability and disaster recovery. Specific technologies are considered for provision of independent orderwires and backup transmission systems. An interface device designed for transmission recovery is recommended for both satellite and terrestrial media. Other specific recommendations are made for network management within the various hierarchical levels of DISN management.>

A chip organization is proposed for the classical tree-pattern-matching problem. It is based on an algorithm which uses a combination of a content-addressed memory, shift registers, and one-bit-wide stacks. All tree pattern matches are found simultaneously in one scan of the subject stream. The chip could be operated as a coprocessor to speed up functional language processing implementations. Multiple chips can be cascaded to increase capacity, similar to the way in which memory chips are utilized. An example chip has been laid out in CMOS technology in a 40-pin standard frame. Comparisons to previous algorithms are discussed.>

A technique for analyzing robots with fourth-order inverse kinematic solutions is introduced. The solution of the inverse kinematics problem is restated as a pencil of conics. In this representation, the order of the solution reduces when one of the conics in the pencil becomes degenerate and noncentral. Properties of conics are exploited to define and develop special manipulator geometries. The method is illustrated by a detailed examination of all revolute (R) robots which have a three-axis wrist (i.e. 3R regional manipulators). All criteria for reduction of the inverse kinematics of this type of robot are determined, resulting in some new robot designs. These designs constitute all regional robots with kinematics that require the solution of only quadratic equations.>

An object-space hidden-line algorithm for higher-dimensional scenes has been designed and implemented. Scenes consist of convex hulls of any dimension, each of which is compared against the edges of all convex hulls not eliminated by a hyperdimensional clipper, a depth test after sorting and a minimax text. Hidden and visible elements are determined in accordance with the dimensionality of the selected viewing hyperspace. When shape alone is the attribute of interest, hidden-line elimination need be performed only in that hyperspace. The algorithm is of value in the production of shadows of hyperdimensional models, including but not limited to four-dimensional space-time models, the hyperdimensional elementary catastrophe models and multivariate statistical models.

This paper describes results of test and evaluation of a commercial voice concentrator applied to AUTOVON interswitch trunks between Feldberg, Germany and Ft. Detrick, MD. By virtue of time assignment speech interpolation (TASI) techniques, the voice concentrator provided approximately a 2-to-1 compression of voice channels onto trunks, with a configuration of 17 channels onto nine trunks selected for this AUTOVON application. Tests consisted of: 1) signaling performance characterization through the voice concentrator, 2) voice channel characterization of the nine selected trunks before and after cutover of the voice concentrator, 3) performance characterization of data signals operated through voice concentrator channels, 4) traffic data collection and analysis, and 5) user subjective evaluation. Test results indicated acceptable performance for the intended application, which was limited primarily to voice signals. Since completion of the testing and subsequent analysis, the Defense Communications Agency (DCA) has accepted the voice concentrator system for operational use with AUTOVON IST's in the Defense Communications System (DCS).

The paper describes the results of simulation experiments of a tree organized multicomputer now being constructed in the Department of Computer Science at Stony Brook.

The development of LSI components has induced a substantial research effort into the possibility of applying conglomerations of small processors on some computational problems previously limited to large processors. The primary advantages of “multi-microprocessor architectures” are their potential for improvements in cost, reliability, and possibly speed over conventional large computers. However, the realization of these advantages requires improvements in the techniques necessary to translate problems into parallel algorithms for multi-microprocessors, and in the architectures of the multi-microprocessors themselves. This paper describes an architecture currently being investigated at SUNY - Stony Brook. The first section discusses various strategies of design. The second section describes the structure of the proposed system. Finally, the third section discusses some classes of problems for which the architecture is suited.

A study of the linear feedback shift registers corresponding to a subset of nonprimitive irreducible polynomials overGF(2)has uncovered a class of sequences with interesting structures and cyclic correlation properties. These families of sequences are made up of interleaved identical sequences which are from primitive irreducible polynomials. Furthermore, they have correlation functions which are two or three valued, being constant at zero or a small value throughout most of their length with the exception of a small number of impulses. Each interval between such impulses on the correlograms uniquely corresponds to (and thus uniquely identifies) the member sequence or sequences producing it. It is shown that these families of sequences have direct application as error-correcting codes.

The complexity of the switching networks necessary to realize arbitrary combinational functions is studied. Asymptotic upper and lower bounds for fully defined functions are well known, while lower bounds also exist for partially defined combinational functions. The present paper supplements these results with the upper bounds for the partially defined functions. The results have possible relevance to pattern recognition.

A method of network synthesis for general combinational functions that uses a number of fixed threshold logic units not greatly in excess of the theoretical minimum is described. It can be used with adaptive logic in which case a minority of the interconnections are simply adapted once according to the rule originally suggested by Hebb. This network on its own has no capacity for ``generalization,'' and if used for pattern recognition must therefore be operated in conjunction with a suitable property filter, such as that of the previous paper.[1] In this case the present network could perform as a trainable ``categorizer.''

A property filter is developed that is suitable for recognizing translation-rotation-dilation classes of two-dimensional images. Invariant outputs corresponding to such classes are obtained by employing two successive sampled spatial harmonic transforms. The required analyses are equivalent to taking inner products of pairs of vectors only one of which is variable in each case. Subsequently, the necessary network may be realized with fixed threshold logic, independent of the character classes to be recognized. The effectiveness of the property filter has been confirmed with printed and handwritten numerals by coupling it to a standard adaptive categorizer of a type assuming linear separability. There is further evidence to show that performance is improved by coupling a categorizer that does not assume linear separability.

It has been conjectured [1] that the number Rnof threshold functions of n arguments has the limiting form: Limn→∞log2Rn/n2= const. Bounds previously obtained [2], [3] show that such a constant would have to lie between ⅓ and one. In the present note this constant is shown to have a lower bound of ½.1The result is extended to the number Rnmof threshold functions defined on m minterms of n arguments and suggests the more general form in the limit of large n, m/n. {logm/nRnm/n} = const. with the same limits for the constant, providing that the minterms are spread out in a certain sense.