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Channel Feedback Reduction Schemes for Opportunistic Scheduling in Multicast OFDMA Systems. Soomin Ko , Yung-Jun Yoo , and Byeong Gi Lee Seoul National University, Korea. Introduction. Multicast transmission Disseminating data to a group of recivers Opportunistic scheduling
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Channel Feedback Reduction Schemes for Opportunistic Scheduling in Multicast OFDMA Systems SoominKo, Yung-Jun Yoo, and ByeongGi Lee Seoul National University, Korea
Introduction • Multicast transmission • Disseminating data to a group of recivers • Opportunistic scheduling • Require channel feedback • OFDMA • Feedback load increase • Channel feedback reduction
Introduction • Characteristics of Multicast transmission • User with the poorest channel condition dictates the transmission rate • Main concept • Reduce the feedback of the users with good average channel condition • Can reduce the total feedback load of a group substantially at a marginal loss of throughput
System model • Downlink multicast OFDMA system • Group : set of users subscribing to the same content • G groups, Kg users in each group, • Mi.i.d. channels, L MCS levels • q(g,k),m : MCS level of channel m for user (g,k) • q(g,k),mfeed: MCS level of channel m that is fed back by user (g,k) using a feedback reduction scheme
System model • Group scheduling • Based on the feedback, q(g,k),mfeed, BS selects which group to transmit to, g* • Transmission rate is determined by the poorest user in the selected group, qg*,mfeed = minkq(g*,k),mfeed • CS (Cumulative mass function-based Scheduling) algorithm • Scheduling metric : based on the CMF (Cumulative Mass Function) of qg,mfeed • Fg,mfeed(l)=1-k(1-F(g,k),mfeed(l)), Fgfeed(l)
System model • CS algorithm (1) • User (g,k) reports q(g,k),mfeed • BS makes qg,mfeed = minkq(g,k),mfeed • BS generates a uniform random variable in the interval [Fgfeed(qg,mfeed-1),Fgfeed(qg,mfeed)) • Scheduling metric for group g
System model • CS algorithm (2) • BS allocates channel m to the group whose scheduling metric is the largest, g* • BS transmits a data to group g* through channel m at the rate r(qg*,mfeed) • Average throughput of group g • Tgfeed=MKg/Glr(l)(Fgfeed(l)G-Fgfeed(l-1)G)
Proposed Scheme • Divides the users in each group into two sets • Based on the average channel condition • PCU (Poor Channel Users) • Bottom Kg,PCU-th users • GCU (Good Channel Users) • Other users • Adopts different feedback policy • PCU • Feed back fine-grained condition of the channel • GCU • Feed back coarse-grained condition of the channel
Proposed Scheme • PCU policy • Feed back the real MCS levels of all the channels • GCU policies • FGB (Feedback reduction by Good users with Bit-map) • Users in GCU adopt Bit-map based feedback • FGAB (Feedback reduction by Good users with Advanced Bit-map) • Users in GCU adopt Advanced Bit-map based feedback
Proposed Scheme • Feedback frame structure
FGB scheme • BS broadcasts the threshold g • Users in GCU make the bit-map • Bit for channel m: 1 if q(g,k),m g , 0 otherwise • MCS level of channel m fed back by user (g,k)
FGB scheme • Determining threshold g • Calculate TgFGB for various values of g • TgFGB=MKg/Glr(l)(FgFGB(l)G-FgFGB(l-1)G) • Calculate FgFGB(l) • FgFGB(l)=1-k(1-F(g,k)FGB(l)) • Calculate F(g,k)FGB(l) • For a user in PCU: • For a user in GCU: • Choose g with the largest TgFGB
FGAB scheme • BS broadcasts the threshold g • Users in GCU make the bit-map • Bit for channel m: 1 if q(g,k),m g , 0 otherwise • Additionally sends MCS level of the lowest channel among its M channels • MCS level of channel m fed back by user (g,k)
Numerical results • Simulation environment • M=50 channels • Average SNR of user (g,k) : Uniform random variable in [0,20] dB • Kg=5, vary Kg,PCU from 4 to 0 • Feedback load per group
Numerical results • Normalized average total throughput for varied feedback load
Conclusions • Two feedback reduction schemes, FGB and FGAB • Noting the characteristics of multicast system • Reduce the total feedback load by sacrificing the feedback of the users with good average channel • Significant reduction in total feedback load with marginal loss of throughput