TY - JOUR

T1 - Asymptotically Optimal Resource Block Allocation with Limited Feedback

AU - Bistritz, Ilai

AU - Leshem, Amir

N1 - Publisher Copyright:
© 2002-2012 IEEE.

PY - 2019/1

Y1 - 2019/1

N2 - Consider a channel allocation problem over a frequency-selective channel. There are K channels (frequency bands) and N users such that K=bN for some positive integer b. We want to allocate b channels (or resource blocks) to each user. Due to the nature of the frequency-selective channel, each user considers some channels to be better than others. The optimal solution to this resource allocation problem can be computed using the Hungarian algorithm. However, this requires knowledge of the numerical value of all the channel gains, which makes this approach impractical for large networks. We suggest a suboptimal approach that only requires knowing what the M-best channels of each user are. We find the minimal value of M such that there exists an allocation where all the b channels each user gets are among his M-best. This leads to the feedback of significantly less than one bit per user per channel. For a large class of fading distributions, including Rayleigh, Rician, m-Nakagami, and others, this suboptimal approach leads to both an asymptotically (in K) optimal sum rate and an asymptotically optimal minimal rate. Our non-opportunistic approach achieves (asymptotically) full multiuser diversity as well as optimal fairness in contrast to all other limited feedback algorithms.

AB - Consider a channel allocation problem over a frequency-selective channel. There are K channels (frequency bands) and N users such that K=bN for some positive integer b. We want to allocate b channels (or resource blocks) to each user. Due to the nature of the frequency-selective channel, each user considers some channels to be better than others. The optimal solution to this resource allocation problem can be computed using the Hungarian algorithm. However, this requires knowledge of the numerical value of all the channel gains, which makes this approach impractical for large networks. We suggest a suboptimal approach that only requires knowing what the M-best channels of each user are. We find the minimal value of M such that there exists an allocation where all the b channels each user gets are among his M-best. This leads to the feedback of significantly less than one bit per user per channel. For a large class of fading distributions, including Rayleigh, Rician, m-Nakagami, and others, this suboptimal approach leads to both an asymptotically (in K) optimal sum rate and an asymptotically optimal minimal rate. Our non-opportunistic approach achieves (asymptotically) full multiuser diversity as well as optimal fairness in contrast to all other limited feedback algorithms.

KW - Resource allocation

KW - channel state information

KW - multiuser diversity

KW - random bipartite graphs

UR - http://www.scopus.com/inward/record.url?scp=85055148088&partnerID=8YFLogxK

U2 - 10.1109/TWC.2018.2875706

DO - 10.1109/TWC.2018.2875706

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AN - SCOPUS:85055148088

SN - 1536-1276

VL - 18

SP - 34

EP - 46

JO - IEEE Transactions on Wireless Communications

JF - IEEE Transactions on Wireless Communications

IS - 1

M1 - 8500761

ER -