Ivana Marić, Ron Dabora and Andrea Goldsmith

Slides:



Advertisements
Similar presentations
Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 1 Chapter 11 Information.
Advertisements

Derives the optimal achievable rate for MISO secondary users under coexistence constraints Proposes practical strategy for cognition and cooperation in.
Relaying in networks with multiple sources has new aspects: 1. Relaying messages to one destination increases interference to others 2. Relays can jointly.
EE360: Lecture 13 Outline Cognitive Radios and their Capacity Announcements March 5 lecture moved to March 7, 12-1:15pm, Packard 364 Poster session scheduling.
5/21/20151 Mobile Ad hoc Networks COE 549 Capacity Regions Tarek Sheltami KFUPM CCSE COE
June 4, 2015 On the Capacity of a Class of Cognitive Radios Sriram Sridharan in collaboration with Dr. Sriram Vishwanath Wireless Networking and Communications.
Node Cooperation and Cognition in Dynamic Wireless Networks
Introduction to Cognitive radios Part two HY 539 Presented by: George Fortetsanakis.
A Graph-based Framework for Transmission of Correlated Sources over Multiuser Channels Suhan Choi May 2006.
Gaussian Interference Channel Capacity to Within One Bit David Tse Wireless Foundations U.C. Berkeley MIT LIDS May 4, 2007 Joint work with Raul Etkin (HP)
Coding Schemes for Multiple-Relay Channels 1 Ph.D. Defense Department of Electrical and Computer Engineering University of Waterloo Xiugang Wu December.
Tin Studio Established 07. In TAITUNG CITY Communication Signal Processing Lab Graduate Institute of Communication Engineering NCNU Cooperative Diversity.
International Technology Alliance In Network & Information Sciences International Technology Alliance In Network & Information Sciences 1 Cooperative Wireless.
Communication over Bidirectional Links A. Khoshnevis, D. Dash, C Steger, A. Sabharwal TAP/WARP retreat May 11, 2006.
When rate of interferer’s codebook small Does not place burden for destination to decode interference When rate of interferer’s codebook large Treating.
Addressing Deafness and Hidden Terminal Problem in Directional Antenna Based Wireless Multi-hop Networks Anand Prabhu Subramanian and Samir R. Das {anandps,
Information Theory for Mobile Ad-Hoc Networks (ITMANET): The FLoWS Project Thrust 2 Layerless Dynamic Networks Lizhong Zheng, Todd Coleman.
MD-based scheme could outperform MR-based scheme while preserving the source- channel interface Rate is not sufficient as source- channel interface, ordering.
User Cooperation via Rateless Coding Mahyar Shirvanimoghaddam, Yonghui Li, and Branka Vucetic The University of Sydney, Australia IEEE GLOBECOM 2012 &
Cooperative Communication in Sensor Networks: Relay Channels with Correlated Sources Brian Smith and Sriram Vishwanath University of Texas at Austin October.
Information Theory for Mobile Ad-Hoc Networks (ITMANET): The FLoWS Project Thrust 2 Overview: Layerless Dynamic Networks Lizhong Zheng.
Superposition encoding A distorted version of is is encoded into the inner codebook Receiver 2 decodes using received signal and its side information Decoding.
EE360: Lecture 9 Outline Announcements Cooperation in Ad Hoc Networks
Interference in MANETs: Friend or Foe? Andrea Goldsmith
MAIN RESULT: Depending on path loss and the scaling of area relative to number of nodes, a novel hybrid scheme is required to achieve capacity, where multihop.
Information Theory for Mobile Ad-Hoc Networks (ITMANET): The FLoWS Project Competitive Scheduling in Wireless Networks with Correlated Channel State Ozan.
Cognitive Radios Motivation: scarce wireless spectrum
We aim to exploit cognition to maximize network performance What is the side information at a cognitive node? What is the best encoding scheme given this.
The High, the Low and the Ugly Muriel Médard. Collaborators Nadia Fawaz, Andrea Goldsmith, Minji Kim, Ivana Maric 2.
Resource Allocation in Hospital Networks Based on Green Cognitive Radios 王冉茵
Information Theory for Mobile Ad-Hoc Networks (ITMANET): The FLoWS Project Thrusts 0 and 1 Metrics and Upper Bounds Muriel Medard, Michelle Effros and.
Jayanth Nayak, Ertem Tuncel, Member, IEEE, and Deniz Gündüz, Member, IEEE.
Rate Bounds for MIMO Relay Channels Using Precoding Caleb K. Lo, Sriram Vishwanath and Robert W. Heath, Jr. Wireless Networking and Communications Group.
1 On the Channel Capacity of Wireless Fading Channels C. D. Charalambous and S. Z. Denic School of Information Technology and Engineering, University of.
October 28, 2005 Single User Wireless Scheduling Policies: Opportunism and Optimality Brian Smith and Sriram Vishwanath University of Texas at Austin October.
Multicast Scaling Laws with Hierarchical Cooperation Chenhui Hu, Xinbing Wang, Ding Nie, Jun Zhao Shanghai Jiao Tong University, China.
Information Theory for Mobile Ad-Hoc Networks (ITMANET): The FLoWS Project Collision Helps! Algebraic Collision Recovery for Wireless Erasure Networks.
March 18, 2005 Network Coding in Interference Networks Brian Smith and Sriram Vishwanath University of Texas at Austin March 18 th, 2005 Conference on.
EE360: Lecture 13 Outline Capacity of Cognitive Radios Announcements Progress reports due Feb. 29 at midnight Overview Achievable rates in Cognitive Radios.
1)Effect of Network Coding in Graphs Undirecting the edges is roughly as strong as allowing network coding simplicity is the main benefit 2)Effect of Network.
The Capacity of Interference Channels with Partial Transmitter Cooperation Ivana Marić Roy D. Yates Gerhard Kramer Stanford WINLAB, Rutgers Bell Labs Ivana.
PROJECT DOMAIN : NETWORK SECURITY Project Members : M.Ananda Vadivelan & E.Kalaivanan Department of Computer Science.
Bridging the Gap: A Deterministic Model for Wireless Links David Tse Wireless Foundations U.C. Berkeley NSF Wireless Networks Workshop Aug 27, 2007 TexPoint.
Introduction to Cognitive radios Part two
EECS 290S: Network Information Flow
Capacity region of large wireless networks
Group Multicast Capacity in Large Scale Wireless Networks
Joint Source, Channel, and Network Coding in MANETs
Advanced Wireless Networks
Layerless Dynamic Networks
Tilted Matching for Feedback Channels
Resource Allocation in Non-fading and Fading Multiple Access Channel
Howard Huang, Sivarama Venkatesan, and Harish Viswanathan
Independent Encoding for the Broadcast Channel
Wireless Communication Co-operative Communications
Presented by Hermes Y.H. Liu
Su Yi Babak Azimi-Sadjad Shivkumar Kalyanaraman
Wireless Communication Co-operative Communications
Capacity Regions for Wireless AdHoc Networks
Jinhua Jiang, Ivana Marić, Andrea Goldsmith, and Shuguang Cui Summary
Miguel Griot, Andres I. Vila Casado, and Richard D. Wesel
Unequal Error Protection: Application and Performance Limits
Cornel Zlibut, Undergraduate Junior Tennessee State University
Communication Strategies and Coding for Relaying
Information Theory for Mobile Ad-Hoc Networks (ITMANET): The FLoWS Project Collision Helps! Algebraic Collision Recovery for Wireless Erasure Networks.
ACHIEVEMENT DESCRIPTION
ACHIEVEMENT DESCRIPTION
Towards characterizing the capacity of the building block of MANETs
Baofeng Ji,Bingbing Xing,Huahong Ma Chunguo Li,Hong Wen,Luxi Yang
Lihua Weng Dept. of EECS, Univ. of Michigan
Presentation transcript:

Ivana Marić, Ron Dabora and Andrea Goldsmith Relaying in Networks with Multiple Communicating Pairs: Interference Forwarding Ivana Marić, Ron Dabora and Andrea Goldsmith Summary Introduction Motivation Channel Model ACHIEVEMENT DESCRIPTION Relaying in network with multiple sources has aspects not present in the relay networks: Relaying messages to one destination increases interference to others Relays can jointly encode messages from multiple sources There are many relevant encoding strategies Encoding strategies for networks with multiple sources are not well understood and developed Current approach: multihop routing Time shares between data streams (no joint encoding) Does not exploit broadcast or interference We consider smallest network that captures relaying for multiple sources: the interference channel with a relay Previous work: Sridharan, Vishwanath, Jafar and Shamai [ISIT, 2008] Rates and degrees of freedom when the relay is cognitive Sahin and Erkip [Asilomar 2007, CTW 2008] Various relaying strategies for forwarding information to intended receivers have been proposed Capacity of networks are still unknown; one of the key reasons: we don’t know how to handle and exploit interference In relay networks: Relays forward data for a single source-destination pair Cooperative strategies are well developed and known to bring gains Cooperative strategies exploit the broadcast nature of wireless medium In networks with multiple sources: The center issue is coping with interference created by simultaneous transmissions Networks with multiple sources contain broadcast, multicast, relay and interference channel elements as their building blocks ASSUMPTIONS AND LIMITATIONS: To demonstrate interference forwarding gains, we considered scenario in which the relay can observe the signal from only one source and can thus forward only one of the two messages MAIN RESULT: We determined conditions under which having a relay enhance the interference improves the performance. We also obtained capacity in the special case HOW IT WORKS: The relay forwards a message to a receiver that is not interested in that message, thus increasing the interference at that receiver. This allows the receiver to decode and cancel the interference, and decode its message in the clear channel dest1 dest2 encoder 1 encoder 2 relay Compare the rates to outer bounds Further develop strategies for forwarding in the presence of interference Consider more general scenarios in which interference enhancement needs to be combined with other relaying strategies Apply this strategy to larger networks END-OF-PHASE GOAL STATUS QUO Two messages: Rates: In networks with multiple sources, relays can help beyond forwarding useful information, by increasing interference at the receivers. This allows receivers to decode the interference and cancel it prior to decoding their desired messages Encoding: Decoding: COMMUNITY CHALLENGE NEW INSIGHTS Prize level: Capacity results for networks with multiple sources We present new relaying strategy: interference forwarding We proposed a new relaying strategy for networks with multiple sources. We showed that it can improve the rate performance and that it achieves capacity in a certain scenario. Capacity Result Gaussian Channels Assumptions Achievable Rates We define strong interference conditions as: The presence of the relay changes the strong interference conditions The relay can ‘push’ a receiver into the strong interference regime where decoding of interfering message is optimal We evaluated these results for the Gaussian channels: dest1 dest2 encoder 1 encoder 2 relay Theorem: Any rate pair (R1,R2) that satisfies (2) ? satisfied for any distribution p(x1)p(x2,x3)p(y1,y2|x1,x2,x3) (1) Conditions (2) are analogous to the strong interference conditions derived by Costa and El Gamal for the interference channel Conditions (2) imply that the flow of information from each source to the non-intended receiver is better than to the intended receiver Consequently, receivers can decode the undesired messages for ‘free’ and hence experience no interference To illustrate gains from interference forwarding, we consider the special case (shown in Figures): The relay cannot observe signal sent from source 1 Then, it can only forward message W2 thus improving rate R2 From the perspective of the other receiver, the relay is interference forwarding Can relay help also receiver 1 and improve rate R1? for any distribution p(x1)p(x2,x3)p(y1,y2|x1,x2,x3) Noise: Powers Rates in the Thm. are achieved by: Single-user encoding at the encoder 1 to send W1 Decode-and-forward at the encoder 2 and the relay to send message W2 The channel degradedness condition: (3) Theorem: When (2)-(3) hold, rates (1) are the capacity region. In strong interference, decoding both messages is optimal Insights and Future Work Comparison with Rate Splitting Numerical Results for Gaussian Channel Conclusions Without the relay, the channel reduces to the interference channel (IC) The best known rates for IC are achieved with rate splitting: Demonstrated gains from interference forwarding Interference forwarding: Can improve the performance through interference cancellation Can hurt the receiver by increasing interference Achieves capacity in a special scenario of strong interference It ‘pushes’ receiver in strong interference regime where the receiver can decode both messages We determined conditions under which decoding interference is optimal Can be realized through decode, compress -and-forward Can be combined with other encoding schemes dest1 dest2 encoder 1 encoder 2 relay Insights: When relaying for multiple sources: Jointly encode messages (network coding approach) Exploit broadcast Forward messages and interference Future work: Develop and evaluate transmission strategies that unify above approaches Analyze the general case of the interference channel with a relay Further develop strategies for relaying in the presence of interference Without the relay: interference channel in strong interference With relay, h13=0: no interference forwarding With relay, h13>0: interference forwarding Interference forwarding enlarges the rate region It facilitates interference cancellation In the case when the relay can only use interference forwarding, can the relay still help? We compare the rates achieved with and without the relay Proposition: When strong relay-rcvr1 link strong source2-relay link for any distribution p(x1)p(x2,x3)p(y1,y2|x1,x2,x3) interference forwarding outperforms rate splitting (no relaying).