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3. On Estimating Maximum Sum Rate of MIMO Systems with Successive Zero-Forcing Dirty Paper Coding and Per-antenna Power Constraint

Author

Pham, Thuy M., Farrell, Ronan, and Tran, Le-Nam

Subjects
Computer Science - Information Theory, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

In this paper, we study the sum rate maximization for successive zero-forcing dirty-paper coding (SZFDPC) with per-antenna power constraint (PAPC). Although SZFDPC is a low-complexity alternative to the optimal dirty paper coding (DPC), efficient algorithms to compute its sum rate are still open problems especially under practical PAPC. The existing solution to the considered problem is computationally inefficient due to employing high-complexity interior-point method. In this study, we propose two new low-complexity approaches to this important problem. More specifically, the first algorithm achieves the optimal solution by transforming the original problem in the broadcast channel into an equivalent problem in the multiple access channel, then the resulting problem is solved by alternating optimization together with successive convex approximation. We also derive a suboptimal solution based on machine learning to which simple linear regressions are applicable. The approaches are analyzed and validated extensively to demonstrate their superiors over the existing approach., Comment: 5 pages, 4 figures

Published
2019

5. Alternating Optimization for Capacity Region of Gaussian MIMO Broadcast Channels with Per-antenna Power Constraint

Author

Pham, Thuy M., Farrell, Ronan, and Tran, Le-Nam

Subjects
Computer Science - Information Theory
Abstract

This paper characterizes the capacity region of Gaussian MIMO broadcast channels (BCs) with per-antenna power constraint (PAPC). While the capacity region of MIMO BCs with a sum power constraint (SPC) was extensively studied, that under PAPC has received less attention. A reason is that efficient solutions for this problem are hard to find. The goal of this paper is to devise an efficient algorithm for determining the capacity region of Gaussian MIMO BCs subject to PAPC, which is scalable to the problem size. To this end, we first transform the weighted sum capacity maximization problem, which is inherently nonconvex with the input covariance matrices, into a convex formulation in the dual multiple access channel by minimax duality. Then we derive a computationally efficient algorithm combining the concept of alternating optimization and successive convex approximation. The proposed algorithm achieves much lower complexity compared to an existing interiorpoint method. Moreover, numerical results demonstrate that the proposed algorithm converges very fast under various scenarios., Comment: Accepted for publication in VTC2017-Spring conference (Long version)

Published
2017

6. Low-complexity Approaches for MIMO Capacity with Per-antenna Power Constraint

Author

Pham, Thuy M., Farrell, Ronan, and Tran, Le-Nam

Subjects
Computer Science - Information Theory
Abstract

This paper proposes two low-complexity iterative algorithms to compute the capacity of a single-user multiple-input multiple-output channel with per-antenna power constraint. The first method results from manipulating the optimality conditions of the considered problem and applying fixed-point iteration. In the second approach, we transform the considered problem into a minimax optimization program using the well-known MAC- BC duality, and then solve it by a novel alternating optimization method. In both proposed iterative methods, each iteration involves an optimization problem which can be efficiently solved by the water-filling algorithm. The proposed iterative methods are provably convergent. Complexity analysis and extensive numerical experiments are carried out to demonstrate the superior performance of the proposed algorithms over an existing approach known as the mode-dropping algorithm., Comment: Accepted for publication in VTCSpring-2017 Conference (Long version)

Published
2017

7. An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks with Interference Constraints

Author

Nguyen, Van-Dinh, Tran, Le-Nam, Duong, Trung Q., Shin, Oh-Soon, and Farrell, Ronan

Subjects
Computer Science - Information Theory
Abstract

We consider a linear precoder design for an underlay cognitive radio multiple-input multiple-output broadcast channel, where the secondary system consisting of a secondary base-station (BS) and a group of secondary users (SUs) is allowed to share the same spectrum with the primary system. All the transceivers are equipped with multiple antennas, each of which has its own maximum power constraint. Assuming zero-forcing method to eliminate the multiuser interference, we study the sum rate maximization problem for the secondary system subject to both per-antenna power constraints at the secondary BS and the interference power constraints at the primary users. The problem of interest differs from the ones studied previously that often assumed a sum power constraint and/or single antenna employed at either both the primary and secondary receivers or the primary receivers. To develop an efficient numerical algorithm, we first invoke the rank relaxation method to transform the considered problem into a convex-concave problem based on a downlink-uplink result. We then propose a barrier interior-point method to solve the resulting saddle point problem. In particular, in each iteration of the proposed method we find the Newton step by solving a system of discrete-time Sylvester equations, which help reduce the complexity significantly, compared to the conventional method. Simulation results are provided to demonstrate fast convergence and effectiveness of the proposed algorithm., Comment: Accepted to appear in IEEE Trans. Vehicular Technology, 13 pages, 8 figures

Published
2016
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9. APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Author

Heinken, Almut, primary, Hulshof, Timothy Otto, additional, Nap, Bram, additional, Martinelli, Filippo, additional, Basile, Arianna, additional, O’Brolchain, Amy, additional, O’Sullivan, Neil Francis, additional, Gallagher, Celine, additional, Magee, Eimer, additional, McDonagh, Francesca, additional, Lalor, Ian, additional, Bergin, Maeve, additional, Evans, Phoebe, additional, Daly, Rachel, additional, Farrell, Ronan, additional, Delaney, Rose Marie, additional, Hill, Saoirse, additional, McAuliffe, Saoirse Roisin, additional, Kilgannon, Trevor, additional, Fleming, Ronan M.T., additional, Thinnes, Cyrille C., additional, and Thiele, Ines, additional

Published
2023
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13. A Predictive Model for Minimising Power Usage in Radio Access Networks

Author

Carolan, Emmett, McLoone, Seamus C., Farrell, Ronan, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin Sherman, Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert Y., Series editor, Agüero, Ramón, editor, Zinner, Thomas, editor, García-Lozano, Mario, editor, Wenning, Bernd-Ludwig, editor, and Timm-Giel, Andreas, editor

Published
2015
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14. ROUTE-TO-PA H2020 Project: Raising Open and User-Friendly Transparency-Enabling Technologies for Public Administrations

Author

Scarano, Vittorio, Malandrino, Delfina, Baker, Michael, Détienne, Françoise, Andriessen, Jerry, Pardijs, Mirjam, Ojo, Adegboyega, Porwol, Lukasz, Szufel, Przemyslaw, Kamiński, Bogumil, Meijer, Albert, Ruijer, Erna, Forrester, John, Clementino, Giuseppe, Trochidis, Ilias, Banos, Vangelis, Andriessen, Martin, van de Klashorst, Jan Pieter, Riordan, Pauline, Farrell, Ronan, Boscolo, Paolo, Palmisano, Elena, van der Waal, Sander, Gray, Jonathan, Satta, Matteo, Legale, Eric, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Katsikas, Sokratis K., editor, and Sideridis, Alexander B., editor

Published
2015
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22. Contributors

Author

Abel, Dori, Abelman, Rebecca A., Abu Kar, Sarah, Abughazaleh, Saeed, Abu-Nejim, Husam, Afari, Maxwell Eyram, Ahmed, Hammood, Akoto, Shanice, Alabre, Kenneth, Ali, Fahad, Ali, Tanya, Alipanah-Lechner, Narges, Altunel, Erdem, Alzoebie, Lama, Anderson, Chelsie, Andrews, Christine B., Anthony, AnnGene, Arcand, Paul-Hugo, Arora, Sohrab, Asaro, Joseph A., Astashchanka, Anna, Atchison, Douglas, Aulakh, Sudeep K., Aurora, Taruna, Avasare, Rupali, Babadi, Baktash, Babar, Tania B., Bachman, Emelia Argyropoulos, Bains, Ashika, Balabanov, Roumen, Balaguera, Pedro, Balda, Javier, Bank, T. Caroline, Banwell, Brenda, Barrera, Francisco J., Barta, Samantha, Basman, Craig L., Bass, Crystal T., Beach, Scott R., Bediako, Persey, Belcher, Ren, Bell, Jennifer, Bellinghausen, Amy L., Bennett, Colleen E., Berkowitz, Carol D., Betageri, Omkar, Bharmal, Murtaza, Bhatt, Harikrashna B., Bhuptani, Prachi H., Bido-Medina, Richard O., Bielak, Kendall, Bilodeau, Courtney Clark, Bird, Lisa, Bishop, Erin, Bitar, Ghamar, Blakeney, Craig, Blatz, Allison M., Block, Brian, Bortz, Christina M., Bosse, Kristopher R., Bouton, Tara C., Bove, Mary E., Bowlby, Lynn A., Boyden, Sean, Brady, Mark F., Braide, Biobele, Brar, Jessica K., Bratman, Russell E., Brodowsky, Emily Chan, Brown, Austin K., Brown, Emily C.B., Brugnoni, Jessalyn, Bui, Eric, Burke, Christine, Burton, Lauren, Butler, Blythe, Butler, William, Byanova, Katerina L., Byg, Blaire, Callanan, Tucker C., Cantos, Adriana, Canty, Katherine W., Carragee, Eugene, Carrel-Lammert, Meredith, Castillo, Jorge J., Chait, Daniel, Chan, Philip A., Chapman, Arlene, Chatterjee-Paer, Sudeshna, Cheng, Vicky, Chien, Michael, Chilcote, Jeffrey, II, Childs, Candicee, Chopra, Amit, Chou, Stella T., Choudhury, Natasha, Christian, Cindy W., Chua, Winona, Chun, Andrew S., Claman, David, Clark, Grace V., Clark, Seth, Clarke, Ross, Clay, Alexandra, Clebak, Karl T., Cleland, Derrick, Cohen, Debbie L., Cohen, Lisa, Congdon, Morgan, Constantinou, Maria, Cooper, James Elliott, Corley, James Earl, Cosgrove, Abigail, Covarrubias, Oscar, Craine, Rebecca, Criddle, Sarah, Cristofaro, Patricia, Cunha, Joanne Szczygiel, Cunningham, Karlene, Cusano, Joseph, D’Alessandro, Frank B., Dado, Lynn, Daneshdoost, Shanaz, Daneshvari, Nicholas Omid, Darwiche, Sabrina, Das, Abhijit, DaSilva, Manuel F., De La Garza, Carlos, DeDent, Alison, Deeb, Mery, Denninger, John W., Desrochers, Colette, Devon, Erin Pete, Diaz, Joseph A., DiGrande, Kyle, Dina, Ayodeji, Doo, Kathleen, Drury, Brian, Dus, Bethany, Dutta, Roop, Dworkin, Myles, Dyster, Timothy G., Ebert, Eric, Edmund, Joseph, Edward, Heather L., Eggers, Erica, Eisenhower, Christine, Ellsworth, Pamela, Elnair, Radowan, Escamilla-Ocañas, César E., Espinosa, Patricio Sebastian, Faber, Mark D., Facer, Evan, Fadale, Paul D., Fallon, Ryan T., Farias, Michael J., Farkas, Jon, Farrand, Erica, Farrell, Ronan, Fay, Kevin S., Fender, Robert Zachary, Hernandez, Samantha Fernandez, Fernandez-Robles, Carlos, Ferreira, Jason D., Ferri, Fred F., Ferri, Heather, Fields, Jessica C., Fields, Lauren, Fischer, Staci A., Fiske, Hannah, Florez, Maria Camila Velez, Forbes, Kellie C., Forcier, Michelle, Ford, Sydney, Fort, Frank G., Fort, Glenn G., Frankel, Eitan S., Freeman, Nancy, Friedman, Ryan, Frisch, Daniel R., Fru, Relindis Azenwi, Fullenkamp, Lynn C., Gaffney, Michael, Garcia, Jose P., Garcia, Paloma Jolin, Ge, Jonathan S., Gesthalter, Yaron B., Ghimire, Anil, Gibson, Christopher, Gidwani, Nisha H., Gilek-Seibert, Katarzyna, Gilvaz, Vinit J., Glueck, Jacob, Goetz, Jennifer Leah, Goldburt, Alla, Goldsmith, Corey Elam, Rao Golla, Maheswara Satya Gangadhara, Gomez, Alexander, Slagle, Helen B. Gomez, Greenblatt, H. Karl, Greenfield, Morgan, Gugerty, Lindsay, Gultawatvichai, Patan, Gunning, Samantha, Guo, David, Gupta, Simran, Guttadauria, Brittany, Gwinn, Meghan, Hadi, Mohanad, Hafeez, Muhammad Ubaid, Hall, Hayden, Hall, Sarah, Handa, Sajeev, Hanna, John R., Harbord, Nikolas, Hardacker, Kyle, Hardesty, Anna, Haribhakti, Nirav, Harris, Daniel J., Hartmann, Abigail, Hartnett, Davis A., Hartvigsen, Nathan, Hawkins, Brian, He, Elaine, Heinle, Rachel Wright, Helm, Leesha A., Helm, Matthew F., Herlevich, Thomas, Hermez, Klodia M., Hilliard, Ross W., Hocker, Nathaniel, Hoffman, R. Scott, Holton, Siri M., Homer, Alexander S., Hook, Benjamin E., Hsu, Raymond, Huang, Laurence, Hull, Mariam, Hume, Anne L., Hussain, Zilla, Huynh, Donny V., Hyder, Sarah, Ingraham, Caitlin, Insalaco, Louis F., Jackson, James, Jafari-Esfahani, Shadi, Jain, Rishubh, Jain, Vanita B.D., Jain, Vipul V., Jamal, Fariha, Janigian, Robert H., Jr., Jasuja, Sonia, Javier, Noelle Marie, Jayachandran, Nishant, Jetty, Vybhav, Johnson, Michael P., Johnson, Steven D., Jones, Amanda, Joseph, Tatiana, Joseph-Senatus, Ganaelle, Joshi, Shyam, Justice, Joshua, Kabashneh, Sohaip, Kadiyala, Vishnu, Kalligeros, Markos, Karthikeyan, Vanji, Kaske, Erika, Kasper, Vania, Kass, Joseph S., Kass, Naomi R., Kasthuri, Viknesh S., Katz, Luca, Kazakova, Vera, Keim, Jennifer L., Kensey, Nicholas, Kent, Victoria, Kerrigan, Danielle, Keselman, Dennis, Khare, Sarthak, Khatri, Surya, Khawaja, Hussain R., Khurana, Saranya S., Kifle, Betelhem, Kim, Chan Woo, Kim, Jinseong, Kisare, Samantha, Kohrman, Samuel I., Kokkirala, Aravind Rao, Konstantino, Yuval, Kontos, Nicholas, Kopyt, Nelson, Kothadia, Savan, Koti, Ajay S., Koulouridis, Ioannis, Kreider, Timothy R., Kron, Kevin, Krumm, Ilana, Kuhn, Elizabeth, Kumbar, Lalathaksha, Kuo, Sheng-Han, Kuris, Eren O., Kurss, David I., Kutschke, Michael J., Laband, William F., LaCombe, Philip, Lam, Uyen T., Lam, Wilson, Laredo, Jonathan, Larijani, Mary, Le, Peter, Leahy, Caroline, Leard, Lorriana E., Leavitt, David A., Leba, Christopher T., Lee, Hyung Jin, Lee, Kachiu C., LeGros, Tracy Leigh, Lemme, Nicholas J., L’Etoile, Nathan, Li, Jian, Lim, Carol, Lin, Chi-Ying (Roy), Lippincott, Jordan, Liu, Jonathan, Logue, Teresa C., Longtain, Riley, López, Gabriela, Lord, Kito Akin, Loscalzo, Kirsten, Lucas, Mathew J., Lucier, David J., Jr., Ma’ayeh, Marwan, MacLean, Rachel L., MacMurdo, Maeve G., Madva, Elizabeth N., Maher, Lewena, Makai, Gretchen, Malpica, Luis, Marks, Jonathan, Pinheiro, Ana Paula Marques, Marr, Mollie C., Martinez, Adali, Matin, Adiba, Mazzaglia, Peter J., Mbuyi, Nadine N., McCulloh, Russell J., McDonald-McGinn, Donna M., McGowen, Alexandra, McGraw, Katherine Elizabeth, McKenna, Jaclyn, McNamara, Kelly, Meleveedu, Kapil S., Mendez-Miller, Megan, Mercado, Jorge, Mercado, Osvaldo, Merritt, Rory, Michaels, Marian G., Michel, Jeremy, Mikolasko, Brian D., Milner, John D., Min, May, Minami, Taro, Mohamed, Mouhand F., Najjar-Mojarrab, Javad, Molinari, John, Montgrain, Philippe, Moon, Heesung, Moore, Michael Z., Moore, Shawn D., Morelli, Leah, Morrissey, Patrick J., Mughal, Aleem I., Mukkamalla, Shiva Kumar R., Mullen, Marguerite A., Mulliken, Alexandra, Mumtaz, Tayebah, Muraina, Fadeke, Mustafa, Ahmad, Myers, Ellen, Mylhousen, Emily, Nadaud, Jack, Nadella, Akash, Nadella, Mohnish, Nadjarian, Albert, Nagpal, Nikita, Nathan, Miriam, Naveed, Asim, Nawaiseh, Ahmad Al, Neff, Robert, Neithardt, Adrienne B., Nestlerode, Christina, Niazi, Azfar K., Nisavic, Mladen, Nokoff, Natalie, Noorbhai, Ibrahim Z.D., Novak, James E., Novarra, Austin, John, Audrey R. Odom, Okewunmi, Jeffrey, Oliver, Rebecca, Olszewski, Adam J., Onyeaka, Henry K., Orchowski, Lindsay M., Osorio, Marcela, Osorio, Luis, O’Sullivan, Cassidy, Outlaw, Jasmine, Oyelakin, Ayotomide, Pace, Paolo G., Paddock, John E., Painter, David F., Palmieri, Alicia, Pappas-Taffer, Lisa, Paranandi, Sneha, Parikh, Mihir, Partin, Michael T., Patel, Birju B., Patel, Melissa D., Patel, Minta, Patel, Nima R., Patel, Pranav M., Patel, Vishal I., Patrick, Brett, Paudel, Shreedhar, Pelcovits, Ari, Perez, David L., Perez, Jose J. Hermina, Pétron, Clément, Pfeuti, Courtney, Pham, Lily C., Phillips, Katharine A., Phillips, Oliver W., Phillips, Tara M., Piana, Lauren E., Portnoff, Brandon S., Pou, Sovijja, Prashar, Rohini, Pulice, Christine, Punko, Diana, Purkayastha, Ayan, Qawi, Imrana, Qu, David, Quesada, Adrian, Quinn, Matthew S., Racchi, Nicholas, Rachu, Gregory S., Rajadurai, Suvithan, Ramachandran, Lintu, Rana, Tanvi, Rathore, Bharti, Rathore, Ritesh, Raukar, Neha P., Ravindra, Lakshmi, Reagan, John L., Reardon, P.K., Reddy, Bharathi V., Reddy, Snigdha T., Reed-Weston, Anne, Reginato, Anthony M., Reichart, James P., Reus, Victor I., Reuter, Matthew, Riaz, Sana, Ricci, Brittany, Rich, Harlan G., Richter, Savanah, Rivera, Lauren Davis, Robertson, Melena J., Roby, Lauren C., Roche, Meaghan, Rodriguez, Alejandra E. Morfin, Gómez, Perla M. Romero, Ron, Hayley A., Rossi, Michael, Rubin, David, Rubin, Lila, Rushworth, Parker, Rutstein, Beth H., Sadeghi, Noushine, Sahota, Ruchi Jalota, Saks, Emily, Sall, Jeffrey W., Salvi, Joshua D., Sanchez, Frank A., Sanchez, Martha C., Sanka, Pranavi, Santhosh, Lekshmi, Santos, Linda Herrera, Sauck, Emily, Saylor, Leah, Scheuing, William J., Sciscione, Anthony, Scott, Matthew Thomas, Sederdahl, Bethany K., Seeber, Richard E., II, Sellers, Kyle, Sen, Aritra, Sewell, Rachel, Shaban, Hesham, Shah, Ankur, Shah, Isha, Shakil, Omair, Sharma, Animesh, Sharma, Yuvraj, Sharp, Lydia, Sherman, Alexander, Shill, Jessica E., Shimanovsky, Alexei, Shipp, Michael M., Shlossman, Philip A., Siddiqui, Khawja A., Siegel, Alyssa, Sigman, Mark, Singh, Harinder P., Singh, Jasneet, Singh, Manjot, Slingsby, Brett, Sloane, Amy J., Smick, Alexandra H., Smith, Jeanette G., Smith, Matthew J., Sohaney, Ryann, Soi, Vivek, Soinski, Rebecca, Soler, Maria E., Soman, Sandeep, Sorg, Emily, Sosnoski, Olivia, Sperati, C. John, Stanford, Nathan, Steele, Emery, Steele, Jacqueline, Stern, Ella, Stier, James, Stockwell, Philip, Subramanian, Lakshmi, Suen, Angela, Suh, Edward, Sundaramoorthy, Anjali, Sunkara, Naveena, Supanakorn, Varut, Suri, Priyasha, Sweeney, Hannah, Takasaki, Kaoru, Takei, Ryosuke, Tan, Laren, Tarawneh, Mohammad, Taylor, Alan, Taylor, Kathryn Marie, Taylor, Matthew A., Taylor, S. Trevor, Tenney-Soeiro, Rebecca, Thomas, Anthony G., Thompson, Stefani, Tien, Alexandra Meyer, Tien, David Robbins, Tierney, Anna-Marie, Toma, Helen, Lopez, Kim Tran, Tryforos, Margaret, Tsaban, Gal, Tschoe, Christine, Tungate, Robert, Turn, Christina S., Uduman, Junior, Umanath, Kausik, Ungar, Stephanie P., Vakili, Babak, van Deventer, Ella, Vemula, Ridhima, Venado, Aida, Vollum, Kathryn G., Vorel, Ethan, Walson, Ifeanyi, Wang, Angela, Wang, Emily, Wang, Li, Wehner, Linsey, Westreich, Roi, Wheeler, Ericka, Wighton, Nakia, Wildman, Alexis, Wilson, Annise, Winikor, Jared M., Wininger, Bryce, Winschel, Julia M., Wold, Jaclyn, Wolpaw, Adam J., Wolpert, Marlene Fishman, Wong, Elisabeth A., Wylie, John, Yang, Anna, Yao, Xuan, Yesodharan, Gemini, Yeung, Jennifer, Yin, Chun H., Yip, Agustin G., Yu, Diana H., Yu, Elizabeth An, Yung, Jeffrey, Zainelabdin, Sarah, Zambrano, Juliana, Zargarian, Emin, Zha, B. Shoshana, Zhang, Helen, Zhao, Leon, Zhou, Diana X., Ziaie, Navid, and Zorin, Danylo

Published
2025
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24. Acceleration of Digital Pre- Distortion Training Using Selective Partitioning

Author

Loughman, Meabh, Byrne, Declan, Farrell, Ronan, Dooley, John, Loughman, Meabh, Byrne, Declan, Farrell, Ronan, and Dooley, John

Abstract

In recent years model and Digital Pre-Distortion dimension reduction has been widely researched. The oper- ations involved when running DPD are often far less than those needed during the training of the DPD coefficients. The proposed partitioned Least Squares (LS) adaptation allows a selected subset of DPD coefficients to be updated while the remaining coefficients are held constant. This technique allows a more adaptive training procedure, improved interpretability of the important DPD coefficient’s during training and the ability to partition the DPD function into specific groups. The Frisch-Waugh-Lovell (FWL) theorem is exploited to partition the coefficients of a DPD basis function trained using LS regression. The proposed methodology was experimentally validated with a Generalized Memory Polynomial (GMP) DPD function, used to linearize a 5W power amplifier (PA) driven by a 40MHz 5G-NR signal.

Published
2022

33. Early Stopping Criterion for Recursive Least Squares Training of Behavioural Models

Author

Loughman, Meabh, Barton, Sinead, Farrell, Ronan, Dooley, John, Loughman, Meabh, Barton, Sinead, Farrell, Ronan, and Dooley, John

Abstract

As the physical makeup of cellular base-stations evolve into systems with multiple parallel transmission paths the effort involved in modelling these complex systems increases considerably. One task in particular which contributes to signal distortion on each signal path, is the power amplifier. In power amplifier modelling, Recursive Least Squares has been used in the past to train Volterra models with memory terms, however instability can occur when training the model weights. This manuscript provides a computationally efficient technique to detect the onset of instability and subsequently to inform the decision when to stop adaptive training of dynamic nonlinear behavioural models and avoid the onset of instability. This technique is experimentally validated using four different signal modulation schemes.

Published
2021

34. Multi-Operator Connectivity Sharing for Reliable Networks: A Data-Driven Risk Analysis

Author

Gomes, Andre, Kibilda, Jacek, Farhang, Arman, Farrell, Ronan, DaSilva, Luiz A., Gomes, Andre, Kibilda, Jacek, Farhang, Arman, Farrell, Ronan, and DaSilva, Luiz A.

Abstract

A key distinction between today's and future networks is the appetite for reliable communication to support emerging critical-communication services. In this paper, we study multi-operator connectivity as a form of redundancy to support the design of reliable networks and investigate its trade-offs. This approach is motivated by 3GPP standardisation initiatives of dual-connectivity and similar techniques in industrial wired networks. We deploy a risk awareness performance metric to assess reliability: this superquantile metric accounts for periods of connectivity shortfalls. Our analysis shows that multi-operator connectivity brings significant reliability gains, in particular when network deployments by different operators exhibit high complementarity in coverage. We also explore the effects of multi-connectivity on spectral efficiency in times of high demand for bandwidth. Our study is based on a real-world dataset comprising signal strength indicators of three mobile operators in Dublin, Ireland.

Published
2021

39. Analysis and Enhancement of the LoRaWAN Adaptive Data Rate Scheme

Author

Finnegan, Joseph, Farrell, Ronan, Brown, Stephen, Finnegan, Joseph, Farrell, Ronan, and Brown, Stephen

Abstract

The adaptive data rate (ADR) algorithm is a key component of the LoRaWAN protocol which controls the performance of a LoRaWAN Network. This modifies the data rate parameter of a device based on the current wireless conditions. In this article, we present substantive enhancements for the End Device and Network Server which reduce the convergence time for LoRaWAN devices to reach their optimal data rate. We extend the LoRaWAN module in ns-3 by adding ADR, enabling the simulation of realistic LoRaWAN networks, and add the implementation of the new enhancements in this module. The simulations show that these modifications can result in a significant reduction of the data rate convergence time for LoRaWAN devices and lead to an increased overall packet delivery rate for the network in a dynamic network environment. Our contributions are a publicly available implementation of ADR in ns-3, an analysis of the original algorithm behavior, and a novel version of the algorithm with enhancements that improve performance in every case while remaining easily integrable into an existing LoRaWAN system.

Published
2020

40. Analysis and Design of Outphasing Transmitter Using Class-E Power Amplifiers With Shunt Capacitances and Shunt Filters

Author

Afanasyev, Pavel, Grebennikov, Andrei, Farrell, Ronan, Dooley, John, Afanasyev, Pavel, Grebennikov, Andrei, Farrell, Ronan, and Dooley, John

Abstract

In this paper, a novel outphasing power amplifier (PA) based on class-E amplifiers with shunt capacitances and shunt filters is proposed. The new design provides high drain efficiency for both peak and back-off power levels. A mathematical model for the class-E power amplifier with shunt capacitance and shunt filter is presented. The proposed model enables derivation of load circuit parameters that provide optimum drain efficiency for the peak and back-off power levels using closed form mathematical expressions. Based on this model, an outphasing power amplifier is designed and subsequently implemented using microstrip transmission lines and a GaN HEMT devices. The fabricated power amplifier prototype is optimized for 2.14 GHz and provides drain efficiency of over 60% for back-off power levels up to 8.5 dB. The amplifier demonstrates a 44.3% drain efficiency for 64QAM OFDM modulated signal with 20 MHz bandwidth. Adjacent channel leakage ratio (ACLR) of −39.5 dB and error vector magnitude (EVM) of 0.9 % were achieved after the application of a memory polynomial linearization algorithm.

Published
2020

41. Performance Investigation of Peak Shrinking and Interpolating the PAPR Reduction Technique for LTE-Advance and 5G Signals

Author

Mohammady, Somayeh, Farrell, Ronan, Malone, David, Dooley, John, Mohammady, Somayeh, Farrell, Ronan, Malone, David, and Dooley, John

Abstract

Orthogonal frequency division multiplexing (OFDM) has become an indispensable part of waveform generation in wideband digital communication since its first appearance in digital audio broadcasting (DAB) in Europe in 1980s, and it is indeed in use. As has been seen, the OFDM based waveforms work well with time division duplex operation in new radio (NR) systems in 5G systems, supporting delay-sensitive applications, high spectral efficiency, massive multiple input multiple output (MIMO) compatibility, and ever-larger bandwidth signals, which has demonstrated successful commercial implementation for 5G downlinks and uplinks up to 256-QAM modulation schemes. However, the OFDM waveforms suffer from high peak to average power ratio (PAPR), which is not desired by system designers as they want RF power amplifiers (PAs) to operate with high efficiency. Although NR offers some options for maintaining the efficiency and spectral demand, such as cyclic prefix based (CP-OFDM), and discrete Fourier transform spread based (DFT-S-OFDM) schemes, which have limiting effects on PAPR, the PAPR is still as high as 13 dB. This value increases when the bandwidth is increased. Moreover, in LTE-Advance and 5G systems, in order to increase the bandwidth, and data-rate, carrier aggregation technology is used which increases the PAPR the same way that bandwidth increment does; therefore, it is essential to employ PAPR reduction in signal processing stage before passing the signal to PA. In this paper, we investigate the performance of an innovative peak shrinking and interpolation (PSI) technique for reducing peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) based signals at waveform generation stage. The main idea behind the PSI technique is to extract high peaks, scale them down, and interpolate them back into the signal. It is shown that PSI technique is a possible candidate for reducing PAPR without compromising on computational complexity, compatibl

Published
2020

42. A Novel Physical Layer Authentication With PAPR Reduction Based on Channel and Hardware Frequency Responses

Author

Ramabadran, Prasidh, Afanasyev, Pavel, Malone, David, Leeser, Miriam, McCarthy, Darragh, O'Brien, Bill, Farrell, Ronan, Dooley, John, Ramabadran, Prasidh, Afanasyev, Pavel, Malone, David, Leeser, Miriam, McCarthy, Darragh, O'Brien, Bill, Farrell, Ronan, and Dooley, John

Abstract

Next generation wireless communications such as 5G are expected to feature wide channel bandwidths on the order of hundreds of MHz. As bandwidths increase, circuit impairments caused by frequency dependent behaviour such as ripple and tilt in gain and group delay become more significant. PAPR of OFDM signals also increase with increasing number of subcarriers. Transmitter circuit characterisation for the wide-band frequency response is needed to pre-compensate the signal to be transmitted. In this article, we propose a novel scheme which uses the circuit characteristics combined with the channel response to generate the keys for encrypting signals to provide an additional tier of security at the physical layer. The modulated constellation of the signal of interest is encrypted by dispersing its phases in addition to encrypting the bits using Diffie Hellman scheme. It is also shown that the method is able to reduce the PAPR of OFDM signals. This scheme is experimentally validated from end-to-end on a millimetre wave wireless link at 28.9 GHz demonstrating security against a well-positioned eavesdropper and a reduction of PAPR by 3.5 dB in a 2048 point OFDM signal with 1664 active QPSK modulated sub-carriers.

Published
2020

43. Network Sharing for Reliable Networks: A Data-Driven Study

Author

Gomes, Andre, Kibilda, Jacek, Farhang, Arman, Farrell, Ronan, DaSilva, Luiz A., Gomes, Andre, Kibilda, Jacek, Farhang, Arman, Farrell, Ronan, and DaSilva, Luiz A.

Abstract

The next generation of mobile networks will bring an appetite for reliable communication, which enables emerging critical-communication services. In this paper, we present network sharing between operators as a way to provide increased reliability while using the already existing mobile network infrastructure. Our results indicate that network sharing significantly improves network performance during periods of connectivity shortfalls, benefiting the design of reliable networks. Our conclusions are drawn from a real-world dataset of signal quality indicators for three mobile operators in Dublin, Ireland.

Published
2020

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