WInnComm 2018 Technical Session Presentation Abstracts

Underlay Dynamic Spectrum Access (DSA) addresses the challenge of radio spectrum scarcity by allowing Cognitive Radios (CRs) in a secondary network (the "secondary users" - SUs) to share a spectrum band with an incumbent primary network (PN). In underlay DSA, CRs transmit over the same portion of the spectrum being used by the PN and at the same time, by limiting their transmit power level so that the interference they create on the PN remains below a tolerable threshold. Despite having been extensively studied, the main challenge in underlay DSA remains on how to establish the interference threshold for the PN links and how the SUs autonomously become aware of the interference they create on the PN, especially when following an ideal operating setup where there is no exchange of information between primary and secondary networks. In contrast to previous works that assume that SUs could access the control feedback channel in the PN, we will present a technique that does not rely on any information exchange between the networks but, instead, takes advantage of the use at the primary network of adaptive modulation and coding (AMC). More specifically, in the presented technique we propose an underlay DSA mechanism that only requires the estimation of the adapted modulation scheme being used at the nearest PN link (this can be achieved by performing modulation classification on the radio waveform from the PN link that the CR is passively listening to during spectrum sensing). Since the full AMC mode (modulation order and channel coding rate) chosen for transmission depends on the link signal-to-interference-plus-noise ratio (SINR), by estimating this mode, it is possible for a CR to learn the SINR experienced by a primary link. However, in a general setting with no exchange of information between the primary and the secondary network, while a CR can apply signal processing techniques to infer during spectrum sensing the modulation order being used by a primary transmitter, there is no previous known practical way to infer the experienced throughput of the primary link. The technique to be presented applies a non-linear autoregressive exogenous neural network (NARX-NN) cognitive engine in the SUs to infer, without tapping into feedback or control channels from another network, the full AMC mode used in the transmission of the other network, and to leverage this inference in the realization of a fully autonomous and distributed underlay DSA scheme. For this, the NARX-NN cognitive engine outputs an estimate of the throughput at the nearest primary link from using as inputs the estimated modulation order in the same link and the transmit power for a sequence of probe messages. Moreover, leveraging the use of adaptive modulation in the PN also allows the CR to establish the PN interference threshold. This is because the use in a radio link of adaptive modulation allows for the background noise to increase up to a certain level before the average throughput in the network starts to decrease. In the context of underlay DSA where the PN does not exchange any information with the SN, the interference imposed on the PN by an underlay-transmitting CR can be seen as a background noise that can be increased up to a level which does not affect the average throughput in the PN. In the technique to be presented, a CR couples these observations with the estimation of the full AMC mode in the nearest PN link provided by the cognitive engine to decide on its transmit power and other waveform settings. Simulation results will show that the presented technique is able to limit the reduction in PN relative average throughput within a prescribed target maximum value, while at the same time finding transmit settings for the SUs that will result in as large throughput in the SN as could be allowed by the PN interference limit. As such, while succeeding in its main goal of autonomously and distributedly determining the transmit power of the SUs such that the interference they create remains below the PN limit, the presented technique is also able to manage the tradeoff between the effect of the SN on the PN and the achievable throughput at the SN. Specifically, simulation results will show that for the proposed system with a target primary network maximum relative average throughput reduction of 2%, the achieved relative change is less than 2.5%, while at the same time achieving useful average throughput values in the secondary network between 132kbps and 50kbps. Moreover, it will be seen that the enabling factor in the operation of the proposed technique is the ability of the NARX neural network cognitive engine to accurately predict the modulation scheme and channel coding rate used in a primary link without the need to exchange information between the primary network and the secondary (e.g. access to feedback channels). This ability also results in a significant increase in the transmission opportunities in the SN compared to schemes with the same basic approach but able to only use modulation classification information from the primary link, instead of the full AMC mode.

Handheld Wireless Prognostic Steering of Asset Sustainment Using Artificial Intelligence - Withdrawn

Many assets have sustainment solutions in place that are proposed to the user upon identification of a trigger/problem/failure. Presently, solutions are prioritized by the number of successful fault resolution executions. A requirement exists to utilize intuitive optimization algorithms to prioritize successful fault resolution at the handheld level or point of performance. This new methodology considers cost and performance (down time), as well as success rate - improving availability and affordability. A successful intuitive algorithm solution will prioritize user actions to prevent, mitigate or resolve challenges while minimizing the cost and downtime associated with each action. Today's high technology systems employ sophisticated sustainment systems to leverage advanced technology, artificial intelligence, and system self-maintenance. One of the key Business Analytics (BA) functions in Prognostics and Health Management (PHM) is the ability to manage the health of a system or component and efficiently return it to an operational status. Increasingly, original equipment manufacturers (OEM) are integrating a Diagnostic Steering Algorithm to Prioritize Pre-Packaged Maintenance Solutions into their Computerized Maintenance Management Systems (CMMS). This software tool is used to aid the maintainer in resolving faults and anomalies and is usually initiated from within a Work Order (WO) for every scheduled and unscheduled maintenance activity. This software module offers the maintainer a virtual equivalent to a legacy asset fault isolation manual. It provides the maintainer the ability to troubleshoot failure modes on components, subsystems, and systems more affectively closer to the point of performance while minimizing training resources. By using a smartphone application the user obviates having to return the system to maintenance, but rather returns the asset to service by selecting from a prioritized solution set at the point of performance. This proposal offers a solution utilizing the application of a learning algorithm that uses point of performance collected maintenance data to mature a Diagnostic Steering algorithm when Prioritizing Pre-Packaged Maintenance Solutions. Key challenges and major technical hurdles to be addressed: the accuracy, availability, and accessibility of the relevant data is essential to the success of this application. In addition, measuring the performance metrics relative to cost, performance, and success probability are critical but difficult to achieve. Employment of all measure of wireless data collection efforts is necessary. Thirdly, mastering the learning portion of the algorithm to teach itself after each iteration, find solutions faster, skip or eliminate risky paths, and recording results correctly in order to compare across several systems, geographies, environments, and utilization properties. The initial test trials for the proof of concept were conducted on a subsystem of a squadron of fighter jets, the data availability for our initial assessment was sufficient, but did not provide a high enough level of confidence. Second and third trails were better and encouraging to seek additional funding and test on other projects such as data centers and tug boats. One of the unexpected significant results from the test trials was the reduction of false positives, because the learning algorithm notices the trend in items that are returned to service as okay (RTOK). This proposal provides a novel approach to provide secure closed loop systems for monitoring, control, diagnostics, and prognostics of complex systems, facilities, communications, and environmental equipment using handheld wireless devices. This will serve to provide monitoring and control of operational system health and integrity along with associated data analytics in providing cost, performance, and probability of success for each recommended course of action.

Cognitive Anti-jamming Satellite-to-Ground Communications on NASA's SCaN Testbed

Machine learning aided cognitive anti-jamming communications is designed, developed and demonstrated on a live satellite-to-ground link. A wideband autonomous cognitive radio (WACR) is designed and implemented as a hardware-inthe- loop (HITL) prototype. The cognitive engine (CE) of the WACR is implemented on a PC while the software-defined radio (SDR) platform utilized two different radios for spectrum sensing and actual communications. The cognitive engine performs spectrum knowledge acquisition over the complete spectrum range available for the SATCOM system operation and learns an antijamming communications protocol to avoid both intentional jammers and inadvertent interferers using reinforcement learning. When the current satellite-to-ground link is jammed, the cognitive engine of the ground receiver directs the satellite transmitter to switch to a new channel that is predicted to be jammer-free for the longest possible duration. The end-to-end, closed-loop system was tested on the NASA's Space Communications and Networking (SCaN) testbed on the International Space Station (ISS). The experimental results demonstrated the feasibility of satellite-to-ground cognitive anti-jamming communications along with excellent anti-jamming capability of machine-learning aided cognitive protocols against several different types of jammers. Index Terms—Cognitive anti-jamming communications, cognitive radios, machine learning, Q-learning, reinforcement learning, satellite communications, wideband autonomous cognitive radios.

The High Frequency (HF) band, ranging from 3 -30 MHz, has been a popular medium for maintaining long-range communications at a low cost by bouncing signals off the ionosphere. As a means of making communications in the HF band less reliant on manual operation, the Automatic Link Establishment (ALE) protocol was introduced. ALE has been adopted in multiple military and NATO standards, serving as a guideline for the design of second and third generation (2G/3G) HF radios. Under ALE, a station performs a link quality analysis (LQA) to evaluate the viability of available frequencies for transmitting to another station based on a specific ranking (i.e. BER, SNR). Once this test is completed, the station will then utilize the channels with the highest LQA scores for initial linking attempts. However, due to the frequent ionospheric variations/instability, the stored LQA scores may not accurately reflect the current channel conditions. In addition, if the available channels are unsuitable for usage, a link may not be established. Future revisions of ALE will require being able to sense the channel and adapt its linking process accordingly in order to maintain reliability and more effectively prevent collisions between users. Thus, the objective of this paper is to present a survey of research efforts that investigate different cognitive/spectrum sensing approaches that can be used to improve the performance of ALE.

Boosting Artificial Intelligence in Software Defined Systems with Open Infrastructure

Software infrastructure is playing an increasing role in both terminals and clouds in recent years, such as software communication architecture (SCA) for terminals and OpenStack for clouds. The common benefit is that they both enable a system to decouple applications from the platform with a standardized application programming interface (API). As a component or a higher-level application in SCA systems, artificial intelligence (AI) is significant to meet various challenges, which potentially include wireless network planning, resource optimization, complicated decision making and in-depth knowledge discovery. Meanwhile, open clouds are evolving into intelligent clouds, which allow users to use AI functions to perform cognitive tasks such as analysis, gaining insights from data or transfer learning, which is also known as AI-as-a-Service (AIaaS). In this presentation, we would like to talk about how to take advantages of AlaaS in the open infrastructure, to boost AI and innovations in software defined systems.

Towards an Ontology for SCA APIs

The Software Communication Architecture (SCA) is an implementation-independent architectural framework that specifies a standardized infrastructure for a software defined radio. The intent behind the standardization of the SCA was to support waveform and component portability, interchangeability of implementations, interoperability of software and hardware components, software reuse, and architecture scalability for communications platforms. To the best our knowledge, there are no open-source implementations of the SCA. SCA has many APIs. All SCA APIs and the SCA itself are specified in UML. While UML tools provide some methods for syntactically constraining the development of a specification of a system, they don't support the capability of verifying or enforcing the semantic constraints. In this paper, we are presenting our work on creating a SCA Ontology (SCA4.1) that can be used as a base ontology in the radio domain to help with prototyping the SCA APIs. This ontology is based on Nuvio (Northeastern and VIStology) ontology, a new foundational ontology developed by our team, inspired by the original Cognitive Radio Ontology (CRO), Quantity, Units, Dimensions and Types Ontology (QUDT), DOLCE ultralight ontology (DUL) and Situation Theory ontology (STO-L). The top-level structure, properties and the relationships between the classes and objects will be presented to show the components and interfaces of the SCA. We will then discuss the evaluation of this ontology based on 1) coverage of knowledge, 2) inference capability, 3) precision in defining classes and 4) extendibility.

The middleware is among the three infrastructural system software. Embedded software defined systems (SDS), e.g., the SDR system, also deeply depend on the middleware. These systems are generally component-based, constructed using the model driven architecture and enabled by PIM, PSM and the transformation technique. Inter-component interaction and reconfiguration/reconstruction in either component level or application level on heterogeneous platforms are basic operations in the SDS, which are enabled by current middleware techniques, including CORBA, ICE, COM+, RMI, RPC, etc. Whereas, most available middlewares focus on large-scale distributed network computing systems, of which the size and weight are not well adapted to embedded systems. Especially, the SCA-based SDR system shifts its middleware from CORBA to transfer mechanism demonstrates the above issues exist in the off-the-shelf middleware. For this reason, we initiated a program to craft a function-complete but small and efficient middleware, named RPCExpress, for the embedded SDS. Initial efforts are demonstrated to be quite fruitful. The first edition of RPCExpress enables component-based software development, supports a set of semantics of IDL and provides an IDL to C++ transformation tool. Component-based evaluation is carried out on a practical embedded platform, and the integration with SCA is on the way. The experimental result demonstrates that the RPCExpress outperforms the off-the-shelf middleware in efficiency and footprint.

As a classical transfer mechanism, CORBA has been widely used in SCA-based software radio systems. However, the communication efficiency of the middleware based on CORBA, such as TAO and omniORB, is not very satisfactory, and this has become one of the challenges that constrain SCA technology to be further widely applied. In SCA 4.1 specifications, the transfer mechanism becomes more flexible. We try to improve the efficiency of the transfer mechanism from two aspects: inter-chip and intra-chip, we call them remote-call and local-call separately. Considering the remote-call delay is not low, improving the local-call efficiency becomes the premier task. This achievement also provides more possibilities and options for component granularity within GPPs. On this basis, we are working on optimizing the efficiency of inter-chip communications.

In order to facilitate interoperability and portability of software defined radio components, the conformance to SDR standards (including APIs and behavior specifications) is really important. Due to the huge number of requirements and to ensure reproducibility of the conformance assessment, a testing methodology is necessary to show SDR requirements coverage and test result status as well as an automatic test bench to ease tests execution. This presentation will show how to use a structured approach to develop and maintain a test bench for the assessment to SDR standards conformance. The presentation will be made by Alain Ribault, CTO of KEREVAL. KEREVAL is a French testing Lab. Draft plan: 1) Needs for the assessment of SDR conformance 2) Testing methodology a. From the SDR requirements to the tests b. Test design process c. Compliance checkpoints definition d. Modeling e. Testing generation 3) The test bench 4) Test of SDR components: an example 5) Conclusion / Q&A

Using SCAv4.1 Tools to Develop Applications and Platforms

Software Defined Systems is the industry's response to the ever-increasing complexity and flexibility requirements of today's electronic systems. The Software Communications Architecture (SCA) enables the fulfillment of such requirements for complex Heterogeneous Embedded Distributed Systems (HEDS). In this tutorial, NordiaSoft will demonstrate how SCA software can be developed using tools that combine model-driven design (MDD) and rapid application development (RAD). Using Zero Merge code generation technology, the business source code for an SCA component (modulator, encoder, etc.) is kept separate from the infrastructure code that deals with deploying, instantiating, configuring, connecting, inter-process communications, etc. This tutorial will show how the proposed approach provides the greatest potential of reuse for intellectual property. This SCAv4.1 tutorial will also illustrate concepts like application creation, packaging, installation, deployment and execution using an SCA FM Waveform Application.

Mission Adaptable Ground Station for Small Satellites

The technological growth for Small Satellites (i.e. CubeSats, etc.), and the increasing collaboration in this domain, prompted the development of a Software Defined Radio ground station to address scalability, interoperability, and adaptability using the Software Communications Architecture 4.1 Standard. SCA enables the use of one ground station platform for many different small satellites, allowing the SCA-based ground station to switch from one satellite system to another as they become visible.

Porting an SCA 2.2.2 platform and applications to SCA 4.1 - ADLINK's experience Withdrawn

ADLINK is preparing its DTP 4700 Development and Test Platform for SCA 4.1. The "DTP" is a Linux-based hardware platform that integrates many devices common to SDR development including FGPA, DSP, GPS and a full transceiver stack. The DTP SCA software, including applications was originally developed for SCA 2.2.2 using Spectra CX, ADLINK's SCA development tool set. This talk will examine: • The approaches we evaluated (and sometimes rejected) for porting to an entire platform from 2.2.2 to 4.1 • The tools we built to reduce our migration pain. • The architectural changes we elected to make along the way (moving from an ORB-Everywhere approach to FPGA/DSP to MHAL) During the talk, Spectra CX 4 (for SCA 4.1) will be used to demonstrate some of the migration techniques used.

Invited Presentations