ANDRO (United States)

The proliferation of spectrum-dependent systems and the reduction in Federally-owned spectrum has challenged Radio Access Networks (RANs) to keep pace with requirements for increased data demands. Particularly, Department of Defense (DoD) bandwidth-intensive applications, such as the Internet of Military Things (IoMT), Command and Control (C2), and decentralized or distributed networks all share the need for ubiquitous wireless connectivity with limited spectrum resources. The paper presents a novel concept of Hierarchical Dynamic Spectrum Sharing (H-DSS) architecture standardization that leverages novel mission policy-based Dynamic Spectrum Access (DSA) and agnostic implementation within the DoD spectrum management tools lifecycle to meet bandwidth challenges. H-DSS and mission policy-based DSA are enablers for RANs to cooperate, coordinate, and dynamically share resources in decentralized or distributed Federal and non-Federal spectrum applications.

Detectors based upon the noble elements, especially liquid xenon as well as liquid argon, as both single- and dual-phase types, require reconstruction of the energies of interacting particles, both in the field of direct detection of dark matter (weakly interacting massive particles WIMPs, axions, etc.) and in neutrino physics. Experimentalists, as well as theorists who reanalyze/reinterpret experimental data, have used a few different techniques over the past few decades. In this paper, we review techniques based on solely the primary scintillation channel, the ionization or secondary channel available at non-zero drift electric fields, and combined techniques that include a simple linear combination and weighted averages, with a brief discussion of the application of profile likelihood, maximum likelihood, and machine learning. Comparing results for electron recoils (beta and gamma interactions) and nuclear recoils (primarily from neutrons) from the Noble Element Simulation Technique (NEST) simulation to available data, we confirm that combining all available information generates higher-precision means, lower widths (energy resolution), and more symmetric shapes (approximately Gaussian) especially at keV-scale energies, with the symmetry even greater when thresholding is addressed. Near thresholds, bias from upward fluctuations matters. For MeV-GeV scales, if only one channel is utilized, an ionization-only-based energy scale outperforms scintillation; channel combination remains beneficial. We discuss here what major collaborations use.

In this position paper, the authors argue the need for a novel framework that provides flexibility, autonomy and optimizes the use of scarce resources to ensure reliable communication during next-generation space missions. To this end, the authors present the shortcomings of existing space architectures and the challenges in realizing adaptive autonomous space-networking. In this regard, the authors aim to jointly exploit the immense capabilities of deep reinforcement learning (DRL) and cross-layer optimization by proposing an artificial intelligence-based cognitive cross-layer decision engine to bolster next-generation space missions. The presented software-defined cognitive cross-layer decision engine is designed for the resource-constrained Internet-of-Space-Things. The framework is designed to be flexible to accommodate varying (with time and location) requirements of multiple space missions such as reliability, throughput, delay, energy-efficiency among others. In this work, the authors present the formulation of the cross-layer optimization for multiple mission objectives that forms the basis of the presented framework. The cross-layer optimization problem is then modeled as a Markov Decision Process to be solved using deep reinforcement learning (DRL). Subsequently, the authors elucidate the DRL model and concisely explain the deep neural network architecture to perform the DRL. This position paper concludes by providing the different phases of the evaluation plan for the proposed cognitive framework.

We design, develop, and experimentally validate a complete integrated software/hardware platform for airborne cognitive networking in both indoor and outdoor environments. We first present the concept of all-spectrum cognitive networking and describe a distributed algorithm for maximizing network spectral efficiency by jointly optimizing channel access code-waveforms and routes in a multi-hop network. We then discuss system design parameters and implementation details for setting up a software-defined radio (SDR) testbed that enables reconfigurability at the physical (PHY), medium-access control (MAC), and network (NET) layers of the network protocol stack, either by a user or by means of autonomous decisions. Our algorithmic developments toward spectrally-efficient cognitive networking are software optimized on heterogeneous multi-core general-purpose processor-based SDR architectures by leveraging the design of a novel software-radio framework that offers self-optimization and real-time adaptation capabilities at the PHY, MAC, and NET layers of the network protocol stack. We verify our system design approach in a large-scale testbed deployment of ten terrestrial and one airborne SDR platforms at the Stockbridge Controllable Contested Environment at the Air Force Research Laboratory, Rome, NY, USA. Proof-of-concept experimental results from both indoor and outdoor testbed deployments show that the proposed system can be used to build all-spectrum cognitive networks that withstand intentional interference at PHY and NET layers and can cognitively coexist with non-cross-layer optimized networks.

Automatic modulation classification (AMC) is a key component of intelligent communication systems used in various military and cognitive radio applications. In AMC, it is desired to increase the number of different modulation formats that can be classified, reduce the computational complexity of classification, and improve the robustness and accuracy of the classifier. Generally, AMC techniques are classified into feature based (FB) and likelihood based (LB) classifiers. In this paper, we propose a novel hierarchical hybrid automatic modulation classifier (HH-AMC) that employs both feature based and likelihood based classifiers to improve performance and reduce complexity. As another major contribution of this paper, we implement and evaluate the performance of HH-AMC over-the-air (OTA) using software defined radios (SDRs) to demonstrate the feasibility of the proposed scheme in practice. Experimental evaluation shows high probability of correct classification (P cc ) for both linear and non-linear modulation formats including BPSK, QPSK, 8-PSK, 16-QAM, 32-QAM, CPFSK, GFSK and GMSK under lab conditions.

This paper identifies several important aspects of current Feature Selective Validation (FSV) methodologies that are embodied in IEEE Standard 1597.1 for the Validation of CEM Computer Modeling and Simulations. The FSV method facilitates comparisons of sets of electromagnetic (EM) observable data for a given problem to determine “levels of agreement” across amplitude and feature variables. Areas of future revision to this standard are presented that will further enhance the standard's utility for performing Computational Electromagnetic (CEM) technique validation for a wide range of problems. In particular, we consider the utility of the N-dimensional FSV and revisit applications of the Amplitude Difference Measure (ADM), Feature Difference Measure (FDM) and the Global Difference Measure (GDM). This is discussed within the context of large complex system problems that present interesting challenges to the FSV method due to the potentially wide dynamic range of the data. Certain use cases for scattering cross section, system-level coupling, and large system-level EMC problems require a somewhat modified approach in computing the GDM based on how the FDM and ADM are weighted. For the current 1-D FSV, unweighted or incorrectly weighted amplitude and feature measures can potentially lead to inconclusive or even misleading results. These issues are addressed and future revisions to the IEEE Standard 1597.1 are highlighted.

We consider the problem of joint admission control and resource allocation in a secondary code-division network coexisting with a narrow-band primary system. Our objective is to find the maximum number of admitted secondary links and then find the optimal transmitting powers and code sequences of those secondary links such that the total energy consumption of the secondary network is minimized subject to the conditions that primary interference temperature constraints, secondary Signal-to-Interference-plus-Noise Ratio (SINR) constraints and secondary peak power constraints are all satisfied. This is an NP-hard optimization problem which motivates the development of efficient suboptimal algorithms. We propose a novel iterative algorithm to solve this problem in a computationally efficient manner. Numerical results demonstrate that the proposed algorithm provides excellent solutions that result in large admitted percentage of secondary links and high energy efficiency.

This paper discusses the results of exploratory research and development to apply and demonstrate a heuristics, knowledge-based approach for analyzing the electromagnetic compatibility (EMC) of co-located radio frequency (RF) spread spectrum frequency hopping transceivers mounted on an unmanned airborne vehicle (UAV) platform. In particular, an expert system pre-processor is used to set up the initial problem and assure the availability of a valid geometry model which is used to compute geodesic losses in the frequency domain. A knowledge base is constructed to contain essential modeling rules and “scripts” describing the steps involved in a validated, bottoms-up/top-down EMC analysis methodology. Problem reasoning is first performed on the system geometry in the pre-processing stage. An expert system based post-processor is then used to “monitor” the signal environment in the time domain and select the interference rejection scheme(s) appropriate for mitigating the effects of interferers present at a victim receptor port. Various interference rejection schemes are considered based on the interferer type and signal environment characteristics. This is necessary since a single interference rejection scheme cannot realistically be expected to suppress all types of interference that may be present.

Interference rejection techniques need to be employed to insure the proper operation of electronic systems used in a severe electromagnetic interference environment. This paper discusses a knowledge-based interference rejection approach for DSSS systems. This innovative approach uses an expert system so as to monitor the signal environment to determine the interfering signal and select suitable interference rejection schemes from a library of preselected techniques. The effectiveness of the approach is demonstrated by means of computer simulations.

Various methods exist for effectively comparing multiple sets of electromagnetic observable data on a given problem for validation and verification (V&V) purposes. Oftentimes, the goal is to cross-validate measurements with computer simulations or compare the results of multiple simulation runs in order to gain insight about error mechanisms and their control so as to ensure consistency, accuracy as well as repeatability. These methods are used to determine the degree of convergence (joint agreement) of such data and to investigate where and why disagreements may arise. Recent work has led to the development of robust cross-validation methods that can be applied to electromagnetic observables acquired through measurement or simulation and across any independent variable. This paper briefly discusses several of these methods as applied to cross-validating results derived from multiple simulation-based techniques in which alternative physics formalisms and numerical solutions may be employed.