NSF Workshop on Bridging Gap Between Wireless Networking Technologies and Advances in Physical Layer Breakout Session 3

Participants Mario Gerla, Michael Honig, Tom Hou, Vijay Kumar, Tom Luo, Narayan Mandayam, Madhav Marathe, Anna Scaglione, Kang Shin, R Srikant, Aravind Srinivasan, Tan Wong, Lizhong Zheng

Summary A number of advances are being made at creating new and more realistic models at PHY layer E.g. DMT models that quantify reliability rate trade-off New technologies in form of Cognitive Radios, MIMO radios and Cooperative communication will likely result in new PHY layer abstractions Current algorithmic/optimization methods often do not use the new models Tradeoff between realistic models and tractable models New techniques in algorithm theory and combinatorial optimization need to be developed Higher layers can benefit from an understanding of the PHY layer

PHY layer models: 1 Holistic view of control and data needs to be taken New and more realistic models at PHY layer are now available E.g. what is the networking viewpoint on the Diversity- Multiplexing Gain Tradeoff, for wireless multihop networks ? Layered approach should be maintained to the extent possible; interfaces need to be created to expose information between layers for cross layer optimization Distinguish between models versus metrics

PHY Layer Models: 2 New technologies such as cognitive radios are currently being developed. PHY level abstractions for these new technologies would provide the first step to more realistic protocol design and algorithmic analysis

Issues in Optimization and Algorithms: 1 Many problems are cast as mixed (continuous as well as discrete variables) non-convex programs Very little work has been done in distributed optimization Good working definitions of distributed algorithms Robust Optimization: algorithms should work when instances vary slightly, proof techniques should work when models are varied slightly Fault Tolerant algorithms

Issues in Algorithms and Optimization: 2 Algorithms and analysis based on simple interference models such as disk graphs should not be discarded off hand Even though theorems might not extend, the proof techniques might still be extensible Simple models are amenable to analysis and one needs to quantify the additional gains that can be made by using more complicated models Generic algorithms: algorithms work for all successively complicated models, analysis depends on the model at hand

Issues in Networking Better systems and high level protocols can be built by understanding the PHY level abstractions E.g. Cognitive radio helps reduce the interference and enrich topology options (such as TCP fairness) New application areas such as Vehicular networks, Underwater networks, etc. require modifying PHY models Spatial and temporal variation in usage of spectrum can be used for better spectrum utilization

Summary A number of advances are being made at creating new and more realistic models at PHY layer E.g. DMT models that quantify reliability rate trade-off New technologies in form of Cognitive Radios, MIMO radios and Cooperative communication will likely result in new PHY layer abstractions Current algorithmic/optimization methods often do not use the new models Tradeoff between realistic models and tractable models New techniques in algorithm theory and combinatorial optimization need to be developed Identifying classes of integer programs and non-convex programs that can be solved efficiently: exploiting the structure of the underlying problems

What are we asked to Cover: 1 Comments on talks so far Specific Comments on Talks Important issues that are overlooked For physical layer folks: what are other recent advances at physical layer, what are their impact on wireless networking, future expected advances/breakthroughs at physical layer and how they will impact wireless networking research, where are the research gaps

What are we asked to Cover: 2 Wireless networking folks: Current status (where we are now), future expectations on wireless networking and research Challenges, desired technology advances/ breakthrough from the physical layer, new advances needed from theoretical perspective Algorithm design and optimization: Status and Open Problems Advances/breakthrough at the physical layer, new challenging problems arising from future wireless networking, identify research gaps

Models: Realistic yet computationally tractable Get provable bounds using realistic models Recent techniques in optimization have not been exploited Over head of managing additional parameters