Pub Date : 2021-12-02DOI: 10.1177/15485129211063369
D. Hayhurst, J. Colombi, David W. Meyer
The use of aggregated combat modeling in the cislunar environment has been demonstrated to inform acquisition decisions for the United States Space Force (USSF). First, the cislunar space is hypothesized as a future strategic conflict environment. As such, Lanchester, Lotka–Volterra, and Brackney models could be appropriate to describe such conflict. All models encompass a system of differential equations which parametrically capture the dynamics between friendly and hostile forces. While the Brackney model was constructed to explain two-dimensional land battle, this article adapts it for the respective three-dimensional space domain and applies it to strategic procurement. The analysis demonstrates the pre-eminence of Space Domain Awareness (SDA) in certain contexts while recognizing conditions in which spacecraft survivability holds greater importance.
{"title":"Aggregated space combat modeling","authors":"D. Hayhurst, J. Colombi, David W. Meyer","doi":"10.1177/15485129211063369","DOIUrl":"https://doi.org/10.1177/15485129211063369","url":null,"abstract":"The use of aggregated combat modeling in the cislunar environment has been demonstrated to inform acquisition decisions for the United States Space Force (USSF). First, the cislunar space is hypothesized as a future strategic conflict environment. As such, Lanchester, Lotka–Volterra, and Brackney models could be appropriate to describe such conflict. All models encompass a system of differential equations which parametrically capture the dynamics between friendly and hostile forces. While the Brackney model was constructed to explain two-dimensional land battle, this article adapts it for the respective three-dimensional space domain and applies it to strategic procurement. The analysis demonstrates the pre-eminence of Space Domain Awareness (SDA) in certain contexts while recognizing conditions in which spacecraft survivability holds greater importance.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80088603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-25DOI: 10.1177/15485129211061702
N. Gholizadeh, Joseph M. Hood, R. Dougal
The Linear Implicit Quantized State System (LIQSS) method has been evaluated for suitability in modeling and simulation of long duration mission profiles of Naval power systems which are typically characterized by stiff, non-linear, differential algebraic equations. A reference electromechanical system consists of an electric machine connected to a torque source on the shaft end and to an electric grid at its electrical terminals. The system is highly non-linear and has widely varying rate constants; at a typical steady state operating point, the electrical and electromechanical time constants differ by three orders of magnitude—being 3.2 ms and 2.7 s respectively. Two important characteristics of the simulation—accuracy and computational intensity—both depend on quantization size of the system state variables. At a quantization size of about 1% of a variable’s maximum value, results from the LIQSS1 method differed by less than 1% from results computed by well-known continuous-system state-space methods. The computational efficiency of the LIQSS1 method increased logarithmically with increasing quantization size, without significant loss of accuracy, up to some particular quantization size, beyond which the error increased rapidly. For the particular system under study, a “sweet spot” was found at a particular quantum size that yielded both high computational efficiency and good accuracy.
{"title":"Evaluation of Linear Implicit Quantized State System method for analyzing mission performance of power systems","authors":"N. Gholizadeh, Joseph M. Hood, R. Dougal","doi":"10.1177/15485129211061702","DOIUrl":"https://doi.org/10.1177/15485129211061702","url":null,"abstract":"The Linear Implicit Quantized State System (LIQSS) method has been evaluated for suitability in modeling and simulation of long duration mission profiles of Naval power systems which are typically characterized by stiff, non-linear, differential algebraic equations. A reference electromechanical system consists of an electric machine connected to a torque source on the shaft end and to an electric grid at its electrical terminals. The system is highly non-linear and has widely varying rate constants; at a typical steady state operating point, the electrical and electromechanical time constants differ by three orders of magnitude—being 3.2 ms and 2.7 s respectively. Two important characteristics of the simulation—accuracy and computational intensity—both depend on quantization size of the system state variables. At a quantization size of about 1% of a variable’s maximum value, results from the LIQSS1 method differed by less than 1% from results computed by well-known continuous-system state-space methods. The computational efficiency of the LIQSS1 method increased logarithmically with increasing quantization size, without significant loss of accuracy, up to some particular quantization size, beyond which the error increased rapidly. For the particular system under study, a “sweet spot” was found at a particular quantum size that yielded both high computational efficiency and good accuracy.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79793132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-21DOI: 10.1177/15485129211043874
George W. Clark, T. Andel, J. McDonald, T. Johnsten, T. Thomas
Robotic systems are no longer simply built and designed to perform sequential repetitive tasks primarily in a static manufacturing environment. Systems such as autonomous vehicles make use of intricate machine learning algorithms to adapt their behavior to dynamic conditions in their operating environment. These machine learning algorithms provide an additional attack surface for an adversary to exploit in order to perform a cyberattack. Since an attack on robotic systems such as autonomous vehicles have the potential to cause great damage and harm to humans, it is essential that detection and defenses of these attacks be explored. This paper discusses the plausibility of direct and indirect cyberattacks on a machine learning model through the use of a virtual autonomous vehicle operating in a simulation environment using a machine learning model for control. Using this vehicle, this paper proposes various methods of detection of cyberattacks on its machine learning model and discusses possible defense mechanisms to prevent such attacks.
{"title":"Detection and defense of cyberattacks on the machine learning control of robotic systems","authors":"George W. Clark, T. Andel, J. McDonald, T. Johnsten, T. Thomas","doi":"10.1177/15485129211043874","DOIUrl":"https://doi.org/10.1177/15485129211043874","url":null,"abstract":"Robotic systems are no longer simply built and designed to perform sequential repetitive tasks primarily in a static manufacturing environment. Systems such as autonomous vehicles make use of intricate machine learning algorithms to adapt their behavior to dynamic conditions in their operating environment. These machine learning algorithms provide an additional attack surface for an adversary to exploit in order to perform a cyberattack. Since an attack on robotic systems such as autonomous vehicles have the potential to cause great damage and harm to humans, it is essential that detection and defenses of these attacks be explored. This paper discusses the plausibility of direct and indirect cyberattacks on a machine learning model through the use of a virtual autonomous vehicle operating in a simulation environment using a machine learning model for control. Using this vehicle, this paper proposes various methods of detection of cyberattacks on its machine learning model and discusses possible defense mechanisms to prevent such attacks.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79549068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-22DOI: 10.1177/15485129211051377
Scott M. Katalenich, M. Jacobson
Expeditionary contingency bases (non-permanent, rapidly built, and often remote outposts) for military and non-military applications represent a unique opportunity for renewable energy. Conventional applications rely upon diesel generators to provide electricity. However, the potential exists for renewable energy, improved efficiency, and energy storage to largely offset the diesel consumed by generators. This paper introduces a new methodology for planners to incorporate meteorological data for any location worldwide into a planning tool in order to minimize air pollution and carbon emissions while simultaneously improving the energy security and energy resilience of contingency bases. Benefits of the model apply not just to the military, but also to any organization building an expeditionary base—whether for humanitarian assistance, disaster relief, scientific research, or remote community development. Modeling results demonstrate that contingency bases using energy efficient buildings with batteries, rooftop solar photovoltaics, and vertical axis wind turbines can decrease annual generator diesel consumption by upward of 75% in all major climate zones worldwide, while simultaneously reducing air pollution, carbon emissions, and the risk of combat casualties from resupply missions.
{"title":"Renewable energy and energy storage to offset diesel generators at expeditionary contingency bases","authors":"Scott M. Katalenich, M. Jacobson","doi":"10.1177/15485129211051377","DOIUrl":"https://doi.org/10.1177/15485129211051377","url":null,"abstract":"Expeditionary contingency bases (non-permanent, rapidly built, and often remote outposts) for military and non-military applications represent a unique opportunity for renewable energy. Conventional applications rely upon diesel generators to provide electricity. However, the potential exists for renewable energy, improved efficiency, and energy storage to largely offset the diesel consumed by generators. This paper introduces a new methodology for planners to incorporate meteorological data for any location worldwide into a planning tool in order to minimize air pollution and carbon emissions while simultaneously improving the energy security and energy resilience of contingency bases. Benefits of the model apply not just to the military, but also to any organization building an expeditionary base—whether for humanitarian assistance, disaster relief, scientific research, or remote community development. Modeling results demonstrate that contingency bases using energy efficient buildings with batteries, rooftop solar photovoltaics, and vertical axis wind turbines can decrease annual generator diesel consumption by upward of 75% in all major climate zones worldwide, while simultaneously reducing air pollution, carbon emissions, and the risk of combat casualties from resupply missions.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89809457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-08DOI: 10.1177/15485129211051385
S. Noel, Stephen Purdy, Annie O’Rourke, E. Overly, Brianna Chen, Christine DiFonzo, Joseph Chen, George Sakellis, Mandira Hegde, Mano Sapra, Corrine M. Araki, Jeremy Martin, Ben Koehler, J. Keenan, Timothy Coen, William W. Watson, Jerry Harper, Kevin Jacobs
This paper describes the Cyber Situational Understanding (Cyber SU) Proof of Concept (CySUP) software system for exploring advanced Cyber SU capabilities. CySUP distills complex interrelationships among cyberspace entities to provide the “so what” of cyber events for tactical operations. It combines a variety of software components to build an end-to-end pipeline for live data ingest that populates a graph knowledge base, with query-driven exploratory analysis and interactive visualizations. CySUP integrates with the core infrastructure environment supporting command posts to provide a cyber overlay onto a common operating picture oriented to tactical commanders. It also supports detailed analysis of cyberspace entities and relationships driven by ad hoc graph queries, including the conversion of natural language inquiries to formal query language. To help assess its Cyber SU capabilities, CySUP leverages automated cyber adversary emulation to carry out controlled cyberattack campaigns that impact elements of tactical missions.
{"title":"Graph analytics and visualization for cyber situational understanding","authors":"S. Noel, Stephen Purdy, Annie O’Rourke, E. Overly, Brianna Chen, Christine DiFonzo, Joseph Chen, George Sakellis, Mandira Hegde, Mano Sapra, Corrine M. Araki, Jeremy Martin, Ben Koehler, J. Keenan, Timothy Coen, William W. Watson, Jerry Harper, Kevin Jacobs","doi":"10.1177/15485129211051385","DOIUrl":"https://doi.org/10.1177/15485129211051385","url":null,"abstract":"This paper describes the Cyber Situational Understanding (Cyber SU) Proof of Concept (CySUP) software system for exploring advanced Cyber SU capabilities. CySUP distills complex interrelationships among cyberspace entities to provide the “so what” of cyber events for tactical operations. It combines a variety of software components to build an end-to-end pipeline for live data ingest that populates a graph knowledge base, with query-driven exploratory analysis and interactive visualizations. CySUP integrates with the core infrastructure environment supporting command posts to provide a cyber overlay onto a common operating picture oriented to tactical commanders. It also supports detailed analysis of cyberspace entities and relationships driven by ad hoc graph queries, including the conversion of natural language inquiries to formal query language. To help assess its Cyber SU capabilities, CySUP leverages automated cyber adversary emulation to carry out controlled cyberattack campaigns that impact elements of tactical missions.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76248273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-08DOI: 10.1177/15485129211051751
J. Anderson, Jessica M Astudillo, Zachary Butcher, Matthew D Cornman, Anthony J Correale, James B. Crumpacker, Nathaniel C Dennie, Alexandra R Gaines, Mark A. Gallagher, John C Goodwill, Emily S. Graves, Donald B Hale, Kimberly G Holland, B. D. Huffman, M. McGee, Nicholas A Pollack, Rachel C. Ramirez, Camero Song, Emmie K Swize, Erick A Tello, Jesse G. Wales, J. C. Walker, A. B. Wilson, William F. Wilson, Kylie E Wooten, M. Zawadzki
We demonstrate a new approach to conducting a military force structure study under uncertainty. We apply the stochastic preemptive goal program approach, described by Ledwith et al., to balance probabilistic goals for military force effectiveness and the force’s cost. We use the Bayesian Enterprise Analytic Model (BEAM), as described in “Probabilistic Analysis of Complex Combat Scenarios,” to evaluate effectiveness, expressed in terms of the probability of achieving campaign objectives, in three hypothetical scenarios. We develop cost estimates along with their uncertainty to evaluate the force’s research and development, production, and annual operating and support costs. Our summary depicts how the trade-off between various prioritized goals influences the recommended robust force. Our approach enables defense leaders to balance risk in both force effectiveness in various scenarios along with risk in different types of cost categories.
{"title":"Stochastic preemptive goal programming of Air Force weapon systems mix","authors":"J. Anderson, Jessica M Astudillo, Zachary Butcher, Matthew D Cornman, Anthony J Correale, James B. Crumpacker, Nathaniel C Dennie, Alexandra R Gaines, Mark A. Gallagher, John C Goodwill, Emily S. Graves, Donald B Hale, Kimberly G Holland, B. D. Huffman, M. McGee, Nicholas A Pollack, Rachel C. Ramirez, Camero Song, Emmie K Swize, Erick A Tello, Jesse G. Wales, J. C. Walker, A. B. Wilson, William F. Wilson, Kylie E Wooten, M. Zawadzki","doi":"10.1177/15485129211051751","DOIUrl":"https://doi.org/10.1177/15485129211051751","url":null,"abstract":"We demonstrate a new approach to conducting a military force structure study under uncertainty. We apply the stochastic preemptive goal program approach, described by Ledwith et al., to balance probabilistic goals for military force effectiveness and the force’s cost. We use the Bayesian Enterprise Analytic Model (BEAM), as described in “Probabilistic Analysis of Complex Combat Scenarios,” to evaluate effectiveness, expressed in terms of the probability of achieving campaign objectives, in three hypothetical scenarios. We develop cost estimates along with their uncertainty to evaluate the force’s research and development, production, and annual operating and support costs. Our summary depicts how the trade-off between various prioritized goals influences the recommended robust force. Our approach enables defense leaders to balance risk in both force effectiveness in various scenarios along with risk in different types of cost categories.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77293783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-08DOI: 10.1177/15485129211052284
Pradeep Kumar, Subodh Srivastava, Y. Padma Sai
The evolution of magnetic resonance imaging (MRI) leads to the study of the internal anatomy of the breast. It maps the physical features along with functional characteristics of selected regions. However, its mapping accuracy is affected by the presence of Rician noise. This noise limits the qualitative and quantitative measures of breast image. This paper proposes recasted nonlinear complex diffusion filter for sharpening the details and removal of Rician noise. It follows maximum likelihood estimation along with optimal parameter selection of complex diffusion where the overall functionality is balanced by regularization parameters. To make recasted nonlinear complex diffusion, the edge threshold constraint “k” of diffusion coefficient is reformed. It is replaced by the standard deviation of the image. It offers a wide range of threshold as variability present in the image with respect to edge. It also provides an automatic selection of “k” instead of user-based value. A series of evaluation has been conducted with respect to different noise ratios further quality improvement of MRI. The qualitative and quantitative assessments of evaluations are tested for the Reference Image Database to Evaluate Therapy Response (RIDER) Breast database. The proposed method is also compared with other existing methods. The quantitative assessment includes the parameters of the full-reference image, human visual system, and no-reference image. It is observed that the proposed method is capable of preserving edges, sharpening the details, and removal of Rician noise.
{"title":"Recasted nonlinear complex diffusion method for removal of Rician noise from breast MRI images","authors":"Pradeep Kumar, Subodh Srivastava, Y. Padma Sai","doi":"10.1177/15485129211052284","DOIUrl":"https://doi.org/10.1177/15485129211052284","url":null,"abstract":"The evolution of magnetic resonance imaging (MRI) leads to the study of the internal anatomy of the breast. It maps the physical features along with functional characteristics of selected regions. However, its mapping accuracy is affected by the presence of Rician noise. This noise limits the qualitative and quantitative measures of breast image. This paper proposes recasted nonlinear complex diffusion filter for sharpening the details and removal of Rician noise. It follows maximum likelihood estimation along with optimal parameter selection of complex diffusion where the overall functionality is balanced by regularization parameters. To make recasted nonlinear complex diffusion, the edge threshold constraint “k” of diffusion coefficient is reformed. It is replaced by the standard deviation of the image. It offers a wide range of threshold as variability present in the image with respect to edge. It also provides an automatic selection of “k” instead of user-based value. A series of evaluation has been conducted with respect to different noise ratios further quality improvement of MRI. The qualitative and quantitative assessments of evaluations are tested for the Reference Image Database to Evaluate Therapy Response (RIDER) Breast database. The proposed method is also compared with other existing methods. The quantitative assessment includes the parameters of the full-reference image, human visual system, and no-reference image. It is observed that the proposed method is capable of preserving edges, sharpening the details, and removal of Rician noise.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77150710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-08DOI: 10.1177/15485129211051386
S. Noel, Vipin Swarup, K. Johnsgard
This paper describes an approach for improving cyber resilience through the synthesis of optimal microsegmentation policy for a network. By leveraging microsegmentation security architecture, we can reason about fine-grained policy rules that enforce access for given combinations of source address, destination address, destination port, and protocol. Our approach determines microsegmentation policy rules that limit adversarial movement within a network according to assumed attack scenarios and mission availability needs. For this problem, we formulate a novel optimization objective function that balances cyberattack risks against accessibility to critical network resources. Given the application of a particular set of policy rules as a candidate optimal solution, this objective function estimates the adversary effort for carrying out a particular attack scenario, which it balances against the extent to which the solution restricts access to mission-critical services. We then apply artificial intelligence techniques (evolutionary programming) to learn microsegmentation policy rules that optimize this objective function.
{"title":"Optimizing network microsegmentation policy for cyber resilience","authors":"S. Noel, Vipin Swarup, K. Johnsgard","doi":"10.1177/15485129211051386","DOIUrl":"https://doi.org/10.1177/15485129211051386","url":null,"abstract":"This paper describes an approach for improving cyber resilience through the synthesis of optimal microsegmentation policy for a network. By leveraging microsegmentation security architecture, we can reason about fine-grained policy rules that enforce access for given combinations of source address, destination address, destination port, and protocol. Our approach determines microsegmentation policy rules that limit adversarial movement within a network according to assumed attack scenarios and mission availability needs. For this problem, we formulate a novel optimization objective function that balances cyberattack risks against accessibility to critical network resources. Given the application of a particular set of policy rules as a candidate optimal solution, this objective function estimates the adversary effort for carrying out a particular attack scenario, which it balances against the extent to which the solution restricts access to mission-critical services. We then apply artificial intelligence techniques (evolutionary programming) to learn microsegmentation policy rules that optimize this objective function.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79509405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-05DOI: 10.1177/15485129211050403
Joel Alanya-Beltran, Ravi Shankar, Patteti Krishna, Selva Kumar S
Ubiquitous multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks (UMNs) have emerged as an important technology for enabling security and other applications that need continuous monitoring. Their implementation, however, could be obstructed by the limited bandwidth available due to many wireless users. In this paper, bidirectional long short-term memory (LSTM)-based MIMO-NOMA detector is analyzed considering imperfect successive interference cancelation (SIC). Simulation results demonstrate that the traditional SIC MIMO-NOMA scheme achieves 15 dB, and the deep learning (DL) MIMO-NOMA scheme achieves 11 dB for 10 5 number of iterations. There is a gap of 4 dB which means that the DL-based MIMO-NOMA performs better than the traditional SIC MIMO-NOMA techniques. It has been observed that when the channel error factor increases from 0 to 1, the performance of DL decreases significantly. For the channel error factor value less than 0.07, the DL detector performance much better than the SIC detector even though the perfect channel state information (CSI) is considered. The DL detector’s performance decreases significantly where variations between the actual and expected channel states occurred, although the DL-based detectors’ performance was able to sustain its predominance within a specified tolerance range.
{"title":"Investigation of Bi-Directional LSTM deep learning-based ubiquitous MIMO uplink NOMA detection for military application considering Robust channel conditions","authors":"Joel Alanya-Beltran, Ravi Shankar, Patteti Krishna, Selva Kumar S","doi":"10.1177/15485129211050403","DOIUrl":"https://doi.org/10.1177/15485129211050403","url":null,"abstract":"Ubiquitous multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks (UMNs) have emerged as an important technology for enabling security and other applications that need continuous monitoring. Their implementation, however, could be obstructed by the limited bandwidth available due to many wireless users. In this paper, bidirectional long short-term memory (LSTM)-based MIMO-NOMA detector is analyzed considering imperfect successive interference cancelation (SIC). Simulation results demonstrate that the traditional SIC MIMO-NOMA scheme achieves 15 dB, and the deep learning (DL) MIMO-NOMA scheme achieves 11 dB for 10 5 number of iterations. There is a gap of 4 dB which means that the DL-based MIMO-NOMA performs better than the traditional SIC MIMO-NOMA techniques. It has been observed that when the channel error factor increases from 0 to 1, the performance of DL decreases significantly. For the channel error factor value less than 0.07, the DL detector performance much better than the SIC detector even though the perfect channel state information (CSI) is considered. The DL detector’s performance decreases significantly where variations between the actual and expected channel states occurred, although the DL-based detectors’ performance was able to sustain its predominance within a specified tolerance range.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81455632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1177/15485129211049782
Ravi Shankar, B. Sarojini, H. Mehraj, A. Kumar, Rahul Neware, Ankur Singh Bist
In this work, the deep learning (DL)-based fifth-generation (5G) non-orthogonal multiple access (NOMA) detector is investigated over the independent and identically distributed (i.i.d.) Nakagami-m fading channel conditions. The end-to-end system performance comparisons are given between the DL NOMA detector with the existing conventional successive interference cancelation (SIC)-based NOMA detector and from results, it has been proved that the DL NOMA detector performance is better than the convention SIC NOMA detector. In our analysis, the long-short term memory (LSTM) recurrent neural network (RNN) is employed, and the results are compared with the minimum mean square estimation (MMSE) and least square estimation (LS) detector’s performance considering all practical conditions such as multipath fading and nonlinear clipping distortion. It has been shown that with the increase in the relay to destination (RD) channel gain, the bit error rate (BER) improves. Also, with the increase in fading parameter m, the BER performance improves. The simulation curves demonstrate that when the clipping ratio (CR) is unity, the performance of the DL-based detector significantly improves as compared to the MMSE and LS detector for the signal-to-noise ratio (SNR) values greater than 15 dB and it proves that the DL technique is more robust to the nonlinear clipping distortion.
{"title":"Impact of the learning rate and batch size on NOMA system using LSTM-based deep neural network","authors":"Ravi Shankar, B. Sarojini, H. Mehraj, A. Kumar, Rahul Neware, Ankur Singh Bist","doi":"10.1177/15485129211049782","DOIUrl":"https://doi.org/10.1177/15485129211049782","url":null,"abstract":"In this work, the deep learning (DL)-based fifth-generation (5G) non-orthogonal multiple access (NOMA) detector is investigated over the independent and identically distributed (i.i.d.) Nakagami-m fading channel conditions. The end-to-end system performance comparisons are given between the DL NOMA detector with the existing conventional successive interference cancelation (SIC)-based NOMA detector and from results, it has been proved that the DL NOMA detector performance is better than the convention SIC NOMA detector. In our analysis, the long-short term memory (LSTM) recurrent neural network (RNN) is employed, and the results are compared with the minimum mean square estimation (MMSE) and least square estimation (LS) detector’s performance considering all practical conditions such as multipath fading and nonlinear clipping distortion. It has been shown that with the increase in the relay to destination (RD) channel gain, the bit error rate (BER) improves. Also, with the increase in fading parameter m, the BER performance improves. The simulation curves demonstrate that when the clipping ratio (CR) is unity, the performance of the DL-based detector significantly improves as compared to the MMSE and LS detector for the signal-to-noise ratio (SNR) values greater than 15 dB and it proves that the DL technique is more robust to the nonlinear clipping distortion.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78680960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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