DF has 2 phases: MAC from users to relay, BC from relay to users

1. ACHIEVEMENT DESCRIPTION STATUS QUO END-OF-PHASE GOAL COMMUNITY CHALLENGE NEW INSIGHTS The Multi-Way Relay ChannelDeniz Gündüz, Aylin Yener, Andrea Goldsmith and Vincent Poor Exact capacity regions are hard to obtain even with three nodes Random codes are capacity achieving in most models Decode-and-forward relaying used in most practical systems • Consider inter and/or intra cluster reception • Combine structured and random codes • Non-symmetric achievable points of capacity region MAIN RESULT: MODEL: Users form clusters. Each user in the cluster wants messages of all other users in the same cluster Communication is enabled by the relay. ASSUMPTIONS AND LIMITATIONS: No signal received from other users Symmetric capacity for a symmetric system is analyzed Joint source-channel coding techniques to achieve higher rates Structured codes might provide higher rates than random coding in networks • Can we scale structured codes to multiple users? • Design of practical codes based on joint source-channel coding techniques Compress-and-forward relaying achieves symmetric rates within a constant bit of the capacity. Gap decays with increasing number of users. The Multi-way Relay Channel Deniz Gündüz, Aylin Yener, Andrea Goldsmith and H. Vincent Poor Upper bound System Model Introduction • Assume • i) one user from each cluster does not have a message to transmit • ii) only those users want to decode the other messages • Cut around the sources is a symmetric multiple access channel (MAC) with L(K-1) users • Cut around the sink terminals is a symmetric Gaussian broadcast channel (BC) with L messages of rate (K-1)R each • Define C(x)=0.5log(1+x) • Relays commonly used for connectivity, coverage extension and/or for improved reliability • More effective relaying can be devised when multiple users want to exchange information simultaneously over a single relay terminal • We consider multiple nodes that want to exchange information among themselves. Moreover, we consider multiple such clusters communicating simultaneously over a single relay terminal. We term this model as the multi-way relay channel (mRC). • Consider a total of N users grouped into L clusters of K≥ 2 distinct users each, i.e., N=KL • L=1, K=2 → two-way relay channel • Gaussian mRC • Interested in the achievable symmetric rates Compress-and-Forward (CF) Full Data-Exchange Model (Pr = KP) Decode-and-Forward (DF) Amplify-and-Forward (AF) • Relay quantizes its received signal and broadcasts to users • Consider time division among clusters in MAC phase as well as in BC phase (prevents multiple user clusters from interfering with each other's signals) • Transmission from relay = ‘Wyner-Ziv coding over broadcast channel’ • CF achieves rates within bits of the capacity, which decays with increasing K or L • DF has 2 phases: • MAC from users to relay, • BC from relay to users • In BC phase, consider time division among clusters. For broadcasting within each timeslot, relay exploits side information (own messages) at users • Time division among clusters. Due to symmetry, equal time allocation maximizes achievable symmetric rate • Within timeslot of each cluster, all users in that cluster transmit, and relay scales its received signal and broadcasts to all users • Each user subtracts its own transmit signal from their received signal and decodes other messages Pairwise Data-Exchange Model Insights and Future Work Pairwise Data-Exchange Model Conclusions • Multiple two-way relay channels (K=2) • Achievability with lattice codes • Relay decodes modulo sums of all message pairs, then broadcast each pair's sum only to users in that pair by time-division among pairs Insights • CF scheme: more essential for larger networks • Lattice coding: hard to generalize to exchange networks of more than two users Future work • More general models, characterization of non-symmetric rate points • Practical code design for large networks • CF scheme achieves a symmetric rate within a constant bit offset from the capacity • Constant gap decays to zero with increasing number of users/ clusters • Nested lattice codes for multiple clusters with two users each. Lattice coding outperforms other schemes only for a single cluster, but falls short of the CF scheme with increasing number of clusters.