The final program can be found here.
Wed Feb 6 Thur Feb 7 Fri Feb 8
8.15am -  9.00am Registration Opens
9:00am -  9.10am Conference Opening Announcements Announcements
9.10am - 10.10am Keynote Talk
Prof. Xiaojing Huang
Keynote Talk
Dr. Zhibo Pang
##### ABB Corporate Research, Sweden
Keynote Talk
A/Prof. Vincent Y.F. Tan
##### National University of Singapore
10:10am- 10.40am Morning Tea Morning Tea Morning Tea
10.40am - 11.00am Invited Talk
A/Prof. Brian S. Krongold
##### University of Melbourne
Keynote Talk
Dr. David Soldani
Huawei Australia
(10.40am - 11.40am)
Invited Talk
Prof. Jinho Choi
##### Deakin University
11.00am - 11.20am Invited Talk
Prof. Wei Xiang
##### James Cook University
Invited Talk
Dr. Vera D. Miloslavskaya
##### University of Sydney
11.20am - 11.40am Invited Talk
A/Prof. Xiangyun (Sean) Zhou
Invited Talk
Dr. Shihao Yan
##### Macquarie University
11.40am - 12.00pm Invited Talk
Dr. Gayathri Kongara
Invited Talk
Dr. Ni Ding
Invited Talk
Dr. Wei Ni
##### CSIRO
12:00pm - 1.30pm Lunch Lunch Close
1.30pm - 3.10pm Poster Session
Poster Session
3.10pm - 3.40pm Afternoon Tea Afternoon Tea
3.40pm - 4.00pm Invited Talk
Dr. Sina Vafi
Invited Talk
##### Australian National University
4.00pm - 4.20pm Invited Talk
Dr. Kelvin Layton
Invited Talk
Dr. Lei Yang
##### University New South Wales
4.20pm - 4.40pm Invited Talk
Dr. Qinghua Guo
##### University of Wollongong
Invited Talk
Dr. Changyang She
##### University of Sydney
4.40pm - 5.00pm Invited Talk
Dr. Jingge Zhu
##### University of Melbourne
Presentations of Invited Demos
(4.40pm - 5.20pm)
5.00pm - 5.10pm Presentations by Shortlisted Candidates for Thesis Award
5.20pm - 6.30pm - Break
6.30pm - 9.30pm Banquet
(including awards)

#### Invited Talk

 Title: An Australia’s First – JCU’s Internet of Things Engineering Program Abstract: It is well known that the Internet of Things (IoT) technology is one of the most crucial digital infrastructure for the fourth industrial revolution that is revolutionizing the human society. Some consider IoT technology the frontend of big data. We would argue that IoT is a data-driven technology, which is composed of three layers, i.e., the data collection layer, data communications layer, and data processing layer. This three-layer stratification well explains why IoT technology serves as a foundation to and is inseparable from machine learning (ML) / artificial Intelligence (AI). James Cook University (JCU) prides itself on providing Australia’s first IoT Engineering degree program that is accredited by Engineers Australia. Huawei also set up its first official laboratory with JCU, the JCU-Huawei Narrowband IoT Laboratory opened in February 2017. This talk will walk you through the first IoT course created in Australia, as well as the IoT projects and industry engagement that have been achieved at JCU. Bio: Wei Xiang is currently the Founding Professor and Inaugural Head of Discipline of Internet of Things Engineering at James Cook University, Cairns, Australia. He is a TEDx Speaker, and an elected Fellow of the IET and Engineers Australia. He received the 2016 TNQ Innovation Award, 2017 Engineers Australia Cairns Engineer of the Year, and 2018 Pearcey Foundation Hall of Fame. He was a co-recipient of four Best Paper Awards at 2019 WiSATS, 2015 WCSP, 2011 IEEE WCNC, and 2009 ICWMC. He has been awarded several prestigious fellowship titles, including a Queensland International Fellow (2010-2011) by the Queensland Government, an Endeavour Research Fellow (2012-2013) by the Australian Government, a Smart Futures Fellow (2012-2015) by the Queensland Government, and a JSPS Invitational Fellow jointly by the Australian Academy of Science and Japanese Society for Promotion of Science (2014-2015). He is the Vice Chair of the IEEE Northern Australia Section. He was an Editor for IEEE Communications Letters (2015-2017), and is an Associate Editor for Springer’s Telecommunications Systems. He has published over 220 peer-reviewed papers, of which nearly 100 are IEEE journal articles. He has served in a large number of international conferences in the capacity of General Co-Chair, TPC Co-Chair, Symposium Chair, etc. His research interest falls under the broad areas of communications and signal processing, particularly the Internet of Things, and coding and signal processing for multimedia communications systems. Title: proving Computational Efficiency of Communication for Omniscience and Successive Omniscience Abstract: For a group of users in V where everyone observes a component of a discrete multiple random source, the process that users exchange data so as to reach omniscience, the state where everyone recovers the entire source, is called communication for omniscience (CO). We first consider how to improve the existing complexity O(|V|^2 * SFM(|V|) of minimizing the sum of communication rates in CO, where SFM(|V|) denotes the complexity of minimizing a submodular function. We reveal some structured property in an existing coordinate saturation algorithm: the resulting rate vector and the corresponding partition of V are segmented in $\alpha$, the estimation of the minimum sum-rate. A parametric (PAR) algorithm is then proposed where, instead of a particular $\alpha$, we search the critical points that fully determine the segmented variables for all $\alpha$ so that they converge to the solution to the minimum sum-rate problem and the overall complexity reduces to O(|V| * SFM(|V|)). For the successive omniscience (SO), we consider how to attain local omniscience in some complimentary user subset so that the overall sum-rate for the global omniscience still remains minimum. While the existing algorithm only determines a complimentary user subset in O(|V| * SFM(|V|)) time, we show that, if a lower bound on the minimum sum-rate is applied to the segmented variables in the PAR algorithm, not only a complimentary subset, but also an optimal rate vector for attaining the local omniscience in it are returned in O(|V| * SFM(|V|)) time. Finally, we show how the proposed PAR algorithm is applied to the hierarchical clustering task based on the multivariate mutual information measure. Bio: Ni Ding received the B.C.A. degree from the Shanghai Second Polytechnic University, China, in 2005, the B.E. degree in Telecommunications (1st class honors) from the University of New South Wales, Australia, in 2012 and PhD degree in Electrical Engineering from the Australian National University (ANU) in 2017. From September 1998 to August 2006, She was an associate engineer in Shanghai Telecom, China. In 2016, the last year of her PhD study at ANU, she was employed by the Institute of Network Coding, The Chinese University of Hong Kong, as a junior research assistant for three months. She is now a postdoctoral fellow at Data61. Her research interests generally include optimizations in information theory, wireless communications, signal processing and machine learning. She is currently interested in the discrete and combinatorial optimization problems raised in the following areas: information theoretic privacy-preserving techniques , lossless and lossy data compression, optimal information flow over a graph/hypergraph, discrete event control in cross-layer adaptive modulation and game theory (in particular, the games with strong structures, e.g., supermodular and convex games). Title: A submodularity-based agglomerative clustering algorithm for the privacy funnel Abstract: We propose an efficient iterative agglomerative clustering (IAC) algorithm for the privacy funnel (PF) based on the minimization of the difference of submodular functions (IAC-MDSF). The aim of PF is to maintain an acceptable mutual information between the useful data $X$ and released data $\hat X$ while minimizing the privacy leakage, which is measured as the mutual information between released data $\hat X$ and sensitive data $S$. Our IAC-MDSF algorithm starts with the original alphabet for $X$ and iteratively merges the elements in the current alphabet that minimizes the Lagrangian function $I(S; \hat X) - \lambda I(X; \hat X)$. We prove that the best merge in each iteration of IAC-MDSF can be searched efficiently over all subsets of $\hat X$ by existing MDSF algorithms. We show that the IAC-MDSF algorithm also applies to the information bottleneck (IB), a dual problem to PF. Using a real dataset, we show that our IAC-MDSF algorithm outperforms the existing iterative pairwise merge approaches for both PF and IB and is computationally much less complex.We propose an efficient iterative agglomerative clustering (IAC) algorithm for the privacy funnel (PF) based on the minimization of the difference of submodular functions (IAC-MDSF). The aim of PF is to maintain an acceptable mutual information between the useful data $X$ and released data $\hat X$ while minimizing the privacy leakage, which is measured as the mutual information between released data $\hat X$ and sensitive data $S$. Our IAC-MDSF algorithm starts with the original alphabet for $X$ and iteratively merges the elements in the current alphabet that minimizes the Lagrangian function $I(S; \hat X) - \lambda I(X; \hat X)$. We prove that the best merge in each iteration of IAC-MDSF can be searched efficiently over all subsets of $\hat X$ by existing MDSF algorithms. We show that the IAC-MDSF algorithm also applies to the information bottleneck (IB), a dual problem to PF. Using a real dataset, we show that our IAC-MDSF algorithm outperforms the existing iterative pairwise merge approaches for both PF and IB and is computationally much less complex. Bio: Parastoo Sadeghi is an Associate Professor at the Research School of Engineering, the Australian National University, Canberra, Australia. She received the BSc and MSc degrees in electrical engineering from Sharif University of Technology, Tehran, Iran, in 1995 and 1997, respectively, and the PhD degree in electrical engineering from the University of New South Wales, Sydney, Australia, in 2006. She is currently serving as an Associate Editor of Coding Techniques for the IEEE Transactions of Information Theory. Her research interests are mainly in the areas of network coding, information theory, wireless communications theory, and signal processing. Title: Compute-Forward Multiple Access (CFMA) Abstract: We discuss Compute-Forward Multiple Access (CFMA), a novel multiple access technique which is provably capacity achieving, and at the same time enjoys low-complexity practical implementations. This attractive feature makes CFMA a promising candidate for NOMA (Non-Orthogonal Multiple Access) techniques. Bio: Jingge Zhu received the PhD degree from the Ecole Polytechnique Federale Lausanne (EPFL), Switzerland, in 2016. From 2016 to 2018, he was a postdoctoral researcher at the University of California, Berkeley, USA. He is currently a Lecturer in the Department of Electrical and Electronic Engineering at the University of Melbourne. His research interests include information theory with applications to communication, computation, and control systems. Bio: Jingge Zhu received the PhD degree from the Ecole Polytechnique Federale Lausanne (EPFL), Switzerland, in 2016. From 2016 to 2018, he was a postdoctoral researcher at the University of California, Berkeley, USA. He is currently a Lecturer in the Department of Electrical and Electronic Engineering at the University of Melbourne. His research interests include information theory with applications to communication, computation, and control systems. Title: Design of Error-Correction Codes Decodable as Polar Codes Abstract: In this talk I will give an introduction to polar codes and describe a class of linear block error-correction codes which may be efficiently decoded in the same way as polar codes. First, we look at structure of classical polar codes and discuss its relation to other well-known codes, e.g. Reed-Muller codes, Bose-Chaudhuri-Hocquenghem codes and generalized concatenated codes. Second, we consider methods for soft-decision decoding of polar codes and discuss correction capability of polar codes. Third, we identify variations of polar codes demonstrating higher correction capability compared to classical polar codes. Bio: Vera Miloslavskaya received B.Sc., M.Sc. and PhD degrees from the Peter the Great St. Petersburg Polytechnic University (SPbPU) in 2010, 2012 and 2015, respectively. During her years in SPbPU, she participated in research projects for Huawei, Samsung and EMC. These projects were related to wireless communication and storage systems. 2016-2018 she has been with Cloud Crowding Corp. Datomia, as a Research Scientist. She is currently a Postdoctoral Researcher in Telecommunications in the School of Electrical and Information Engineering at the University of Sydney. Her research interests are coding theory, information theory, distributed storage systems and wireless communications. Title: Physical Layer Security: Perfect Secrecy, Partial Secrecy, Covertness Abstract: This talk will provide a brief discussion on performance metrics used in physical layer security. In particular, most existing studies make use of metrics defined based on perfect secrecy conditions, which will be discussed first. We will then look at some recently developed secrecy metrics based on partial secrecy and discuss its advantages. Towards the end of the talk, we will move beyond secrecy of content and discuss covertness of communication which is an increasingly hot topic in physical layer security. Bio: Xiangyun (Sean) received the Ph.D. degree from the Australian National University in 2010. Currently, he works as an Associate Professor at the Australian National University. His research interests are in the areas of communication theory and wireless networks with recent focus on physical layer security, backscatter communications, energy harvesting and wireless powered communications, machine-to-machine communications. http://users.cecs.anu.edu.au/~xyzhou/Default.html Title: Gaussian Signalling for Covert Communications Abstract: In covert communications, a transmitter desires to transmit information to a legitimate receiver without being detected by a warden. In this talk, we examine the optimality of Gaussian signalling for covert communications. Considering additive white Gaussian noise at both the receiver and the warden, we prove that Gaussian signalling is optimal in terms of maximizing the mutual information between transmitted and received signals subject to one covertness constraint based on KL divergence. More interestingly, we also prove that Gaussian signalling is not optimal for covert communications subject to another covertness constraint, for which as we explicitly show a skew-normal signalling can outperform Gaussian signalling in terms of achieving higher mutual information subject to the same covertness constraint. We also clarify some challenges and future research directions in the context of covert communications. Bio: Shihao Yan received the Ph.D degree in Electrical Engineering from The University of New South Wales, Sydney, Australia, in 2015. He received the B.S. in Communication Engineering and the M.S. in Communication and Information Systems from Shandong University, Jinan, China, in 2009 and 2012, respectively. From 2015 to 2017, he was a Postdoctoral Research Fellow in the Research School of Engineering, The Australian National University, Canberra, Australia. He is currently a Research Fellow in the School of Engineering, Macquarie University, Sydney, Australia. He has published about 30 journal articles, out of which 10 are first-authored IEEE Transactions and 1 is a highly cited article. His current research interests are in the areas of wireless communications and statistical signal processing, including physical layer security, covert communications, and location spoofing detection. Title: Ultra-reliable Low-latency Communications: scenarios, solutions, and open issues Abstract: Ultra-reliable low-latency communications (URLLC) has been considered as one of the three new application scenarios in the 5th Generation (5G) New Radio (NR), where the physical layer design aspects have been specified. With the 5G NR, we can guarantee the reliability and latency in radio access networks. However, for scenarios where the transmission involves both radio access and wide area core networks, the delay in radio access networks only contributes to part of the end-to-end (E2E) delay. In this talk, we outline the delay components and packet loss probabilities in three typical communication scenarios of URLLC, i.e., local area networks, mobile edge computing networks, and wide-area large-scale networks. Then, we summarize possible solutions in these three scenarios respectively. Finally, we discuss the open issues in URLLC. Bio: Changyang She (S’12-M’17) received his B. Eng degree in Honors College (formerly School of Advanced Engineering) of Beihang University (BUAA), Beijing, China in 2012 and Ph.D. degree in School of Electronics and Information Engineering of BUAA in 2017. From 2017 to 2018, he was a postdoctoral research fellow in Singapore University of Technology and Design. Since 2018, he has become a postdoctoral research associate in the University of Sydney. His research interests lie in the areas of ultra-reliable and low-latency communications, tactile internet, big data/machine learning for resource allocation in wireless networks and energy efficient transmission in 5G communication systems. Title: Machine-Type Communication for the IoT Abstract: The Internet of Things (IoT) is the network to provide the connectivity for physical devices including sensors, which will have a huge impact on future industry as well as our daily life, In cellular systems (e.g., 5G), machine-type communication (MTC) is considered for the IoT with random access. For example, in the long term evolution-advanced (LTE-A) system, random access channel (RACH) procedure and narrowband IoT (NB-IoT) have been proposed for MTC to support massive connectivity. In this talk, we discuss MTC and a new random access scheme, called compressive random access (CRA) with its key features. We also show that CRA is an attractive scheme not only for random access, but also for physical-layer authentication as lightweight security in cellular IoT. Bio: Jinho Choi was born in Seoul, Korea. He received B.E. (magna cum laude) degree in electronics engineering in 1989 from Sogang University, Seoul, and M.S.E. and Ph.D. degrees in electrical engineering from Korea Advanced Institute of Science and Technology (KAIST) in 1991 and 1994, respectively. He is with the School of In- formation Technology, Deakin University, Burwood, VIC 3125, Australia, as a Professor. Prior to joining Deakin in 2018, he was with Swansea University, United Kingdom, as a Professor/Chair in Wireless, and Gwangju Institute of Science and Technology (GIST), Korea, as a Professor. His research interests include the Internet of Things (IoT), wireless communications, and statistical signal processing. He authored two books published by Cambridge University Press in 2006 and 2010. Prof. Choi received the 1999 Best Paper Award for Signal Processing from EURASIP, 2009 Best Paper Award from WPMC (Conference), and is Senior Member of IEEE. Currently, he is an Editor of IEEE Trans. Communications and IEEE Wireless Communications Letters and had served as an Associate Editor or Editor of other journals including IEEE Communications Letters, Journal of Communications and Networks (JCN), IEEE Trans. Vehicular Technology, and ETRI journal. Title: Compensation for channels with data-dependent distortions Abstract: Communication channels that can be modelled with data-dependent noise on the received symbols are considered. This includes satellite and optical channels with various types of distortion. A new demapper is proposed to incorporate the covariance of the received symbol clusters to capture the varying noise statistics across the constellation. Two communication scenarios are considered, demonstrating the demapper is advantageous when a system is dominated by distortion as opposed to thermal noise. Channel coding considerations are presented, and reductions up to 4 dB in the required SNR are achieved. Bio: Kelvin Layton is a research fellow at the Institute for Telecommunications Research at the University of South Australia. He completed his B. Eng and B. Info Tech degrees at Queensland University of Technology in 2005. He worked as a software engineer from 2006-2008 and then completed his PhD degree from the University of Melbourne in 2013. He developed new technologies in magnetic resonance imaging (MRI) before switching to telecommunications research. His current research interests are in signal processing and estimation algorithms with applications to satellite communications, beamforming and high-speed downlinks. Title: An efficient bound for burst error correcting capability of binary cyclic cyclic codes Abstract: Burst error has a high impact on the quality of telecommunication and storage systems since it affects more than one sequence of symbols. The effect of this error can be mitigated by a reliable Forward Error Correcting (FEC) code. Depending on the code’s characteristics, errors are corrected only if the allocated length of code is greater than the length of error patterns. In this case, an optimum code can be formed when its burst error correcting capability meets a particular bound, named as Reiger bound. Binary cyclic codes are amongst the most important types of burst error correcting codes. There are several methods that verify their performance, which are mainly constructed based on the cyclic property. As one of the newest methods, the burst error correcting capability of a cyclic code can be determined based on its parity check circulant matrix. It is constructed by specifying the length and position of the unique maximal zero spans from module-2 sums of the matrix columns. Its complexity is proportional to the number of columns involved in the calculations. Hence, a large number of sums are evident for codes with a large number of parity bits, whose correcting capabilities are close or equal to the Reiger bound value. In order to simplify complexity of the above technique, algorithms for estimating burst error correcting capability of cyclic codes are demonstrated. These are formed based on structure of the parity check polynomial and properties of the circulant matrix. Then, a method for constructing cyclic codes with the optimum (or high) performance in correction of the burst error is presented. Bio: Sina Vafi received the PhD degree from The University of Wollongong (UoW) in 2006. After completion of his PhD, at UoW, he was involved in a project relevant to design and implementation of broadband wireless mesh networks. In 2007, he joined Charles Darwin University (CDU) as a lecture and is currently a Senior Lecturer of Electrical and Electronics Engineering. At CDU, he also leads research projects on algebraic coding theory and its applications for wireless video transmission systems. He has been a TPC member of international conferences as well as reviewers for number of IEEE and IET journals. He has also five year’s industry experience in research, design and implementation of optical and broadband wireless networks. Title: Partially Information-Coupled Codes Abstract: Spatially coupled codes, such as SC-LDPC codes and SC Turbo-like codes, have shown considerable coding gain over their uncoupled counterparts. However, these codes are generally tightly coupled in the sense that several coupled code blocks have to be decoded jointly to achieve a reasonable BER performance. This leads to an increased decoder implementation complexity compared to the uncoupled counterparts. We propose the concept of partially information coupling in which a code block shares a part of its information bits with its neighboring code blocks. This coupling concept can explore the coupling gain while keep the underlying decoder for each code block unchanged. We apply this concept to construct partially information-coupled Turbo codes, LDPC codes and Polar codes. Simulation results demonstrated that these codes can achieve considerable coding gains compared to their uncoupled counterparts. Bio: Lei Yang received his B.E. and M.Sc. degrees in electronics engineering from Beihang University, Beijing, China, in 2004 and 2007, respectively, and his Ph.D. degree in communications and information system from Beijing Institute of Technology, Beijing, China, in 2015. He is awarded DECRA in 2018. He is currently a Research Fellow at The University of New South Wales, Sydney, Australia. His current research interests include ultra-high-speed ultra-reliable commutations, spectrum-efﬁcient coded modulation, physical layer network coding and iterative signal processing. Title: Decentralize Fog Computing for Future Mobile Edge Cloud Abstract: Fog computing is a promising technique which brings computing and storage capability of networks to the point of data capture, and hence provides fast response and alleviates congestions at network backbones. The effective design and operation of fog computing remain unaddressed, given the sheer size of an edge cloud and potentially selfish behaviours of edge devices. This talk presents a few recent breakthroughs in which we are able to decentralize the operations of fog computing with asymptotically diminishing optimality loss. We are also able to incentivise selfish edge servers to participate in fog computing, and discourage their selfish behaviours by designing distributed tit-for-tat mechanisms. Bio: Dr Wei Ni was awarded BE and PhD degrees in Electronics Engineering in 2000 and 2005, respectively, both from Fudan University, Shanghai, China. He is a Team Leader at the CSIRO, and an adjunct professor at the University of Technology Sydney (UTS), since 2009. Prior to this, he was a Senior Researcher at Nokia Devices R&D (Jan 2008 – March 2009), and a Deputy Project Manager at Alcatel-Lucent Bell Labs Research & Innovation Centre (Jan 2005 – Dec 2007). His research interest covers stochastic optimization, game theory and graph theory, as well as their applications to integrity, security and efficiency of Cyber Physical System. Dr Ni has published over 150 journal and conference papers. He has also contributed significantly to the industry with successful proposals and launches of an Alcatel-Lucent internal venture and two projects, 25 patents and 10 accepted standard proposals. His scientific breakthroughs include the world's fastest wireless broadband demonstrator; patented adaptive topology of software-defined radio network; and quality-of-service provision to unreliable energy-harvesting powered wireless links. Dr Ni serves as Vice Chair of IEEE VTS NSW chapter and Editor of IEEE Trans. Wireless Communications since 2018, served as Secretary of IEEE VTS NSW chapter (2015 – 2018), Editorial Board Member for Hindawi Journal of Engineering (2012 – 2015), Track Chair of VTC2017-spring, Track Co-chair of VTC2016-spring, Publication Chair of BodyNet 2015, Student Travel Grant Chair of WPMC 2014, TPC member of ICC’16-CoCoNet, ICCC’15, ICC’14, CHINACOM 2014, EICE2014, and IEEE WCNC 2010. He is a Senior Member of IEEE. Title: Message Passing Based Bayesian Receiver for Grant Free NOMA Abstract: Grant free non-orthogonal multiple access (NOMA) is promising to support massive connectivity for internet-of-things and machine-type communications in future wireless networks. In grant free NOMA, user activity detection is performed at receiver to identify active users, so that transmission latency and control signalling overhead can be significantly reduced. We study Bayesian receivers to achieve joint user activity detection and channel estimation / joint user activity and multiuser detection in grant free NOMA, and their efficient implementations with message passing techniques. Bio: Dr Qinghua Guo received his PhD degree in electronic engineering from City University of Hong Kong in 2008. He is now an Associate Professor with the School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, and an Adjunct Associate Professor with the School of Engineering, University of Western Australia. Dr Guo was a recipient of the inaugural ARC DECRA in 2012. His research interest includes telecommunications, communication signal processing, optoelectronic signal processing, etc. Title: Role of Software Defined radio in 5G-like studies at Monash University Abstract: In this presentation, I will be sharing my experience on the role of Software Defined radio (SDR) in the evaluation of waveforms for 5G systems. Specifically, my presentation will focus on massive machine type communications (mMTC) operating in asynchronous transmission environments. Standard CP-OFDM and various versions of filtered-OFDM waveforms are currently under consideration for extreme mobile broadband (eMBB) application of 5G systems. However, our study on SDR platform reveal that these waveform solutions are not well suited for mMTC scenarios. A comparison of time and frequency characteristics of the three potential candidate waveforms on the SDR platform will be presented. Over the air performance of the three waveforms in asynchronous environments will be further discussed. Bio: Dr Gayathri Kongara is a lecturer at Monash University in the Faculty of Engineering with technical experience in the areas of Telecommunications and Signal processing. At Monash, she established a new capability in the Electrical and computer systems engineering (ECSE) department to teach advanced radio system design concepts using hands-on experimentation on the software defined radio (SDR) platform. She is currently working on the design and prototyping of 5G Physical layer (PHY) at Monash. Prior to joining Monash, she held a research-engineering role at the Wireless Research Center (WRC) at University of Canterbury, NZ. Title: Coordinated Multipoint OFDM Transmission for Maximising Capacity or Energy Harvesting Abstract: We highlight a few of our works in the area of coordinated multipoint OFDM transmission for both maximising capacity or harvested energy in a multiple-input, single-output (MISO) system. Optimisation problems are formulated based on distributed antennas having knowledge of channel state information (CSI) for all subchannels. When the CSI is magnitude plus phase, we utilise co-phasing at the coordinated transmission points to formulate and solve convex optimisation problems. The results are some interesting theorems that, at first glance, may even seem counterintuitive to classical waterfilling theory. Simulation results are given to demonstrate these theorems and look deeper into the intuition behind them. Finally, we briefly outline our work on non-coherent (magnitude-only CSI) coordinated transmission for capacity maximisation and energy harvesting in MISO systems. Bio: Brian Krongold received the B.S., M.S. and Ph.D. degrees in electrical engineering in 1995, 1997, and 2001, respectively, from the University of Illinois at Urbana-Champaign (UIUC). He was as a Research Assistant at the Coordinated Science Laboratory at UIUC from 1995 to 2001. From December 2001 to December 2004, he was a Research Fellow in the ARC Special Research Centre for Ultra-Broadband Information Networks in the Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Australia. He then received an ARC Postdoctoral Research Fellowship and held this from 2005 to 2007. In the first half of 2011, Brian was on sabbatical at Alcatel-Lucent (now Nokia) Bell Labs in Murray Hill, New Jersey, USA. He is currently an Associate Professor at the University of Melbourne. His research interests include both wireless and optimal communication systems.Brian Krongold received the B.S., M.S. and Ph.D. degrees in electrical engineering in 1995, 1997, and 2001, respectively, from the University of Illinois at Urbana-Champaign (UIUC). He was as a Research Assistant at the Coordinated Science Laboratory at UIUC from 1995 to 2001. From December 2001 to December 2004, he was a Research Fellow in the ARC Special Research Centre for Ultra-Broadband Information Networks in the Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Australia. He then received an ARC Postdoctoral Research Fellowship and held this from 2005 to 2007. In the first half of 2011, Brian was on sabbatical at Alcatel-Lucent (now Nokia) Bell Labs in Murray Hill, New Jersey, USA. He is currently an Associate Professor at the University of Melbourne. His research interests include both wireless and optimal communication systems.