TY - GEN

T1 - Efficient and asymptotically optimal resource block allocation

AU - Bistritz, Ilai

AU - Leshem, Amir

N1 - Publisher Copyright:
© 2018 IEEE.

PY - 2018/6/8

Y1 - 2018/6/8

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) for each user. Due to the nature of the frequency-selective channel, each user considers some channels to be better than others. Allocating each user only good channels will result in better performance than an allocation that ignores the selectivity of the channel. The optimal solution for 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 a feedback of significantly less than one bit per user per channel. For a large class of fading distributions, including Rayleigh, Rician, m-Nakagami and more, this suboptimal approach leads to both an asymptotically (in K) optimal sum-rate and asymptotically optimal minimal rate. Our non-opportunistic approach achieves asymptotically full multiuser diversity and optimal fairness, in contrast to all existing 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) for each user. Due to the nature of the frequency-selective channel, each user considers some channels to be better than others. Allocating each user only good channels will result in better performance than an allocation that ignores the selectivity of the channel. The optimal solution for 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 a feedback of significantly less than one bit per user per channel. For a large class of fading distributions, including Rayleigh, Rician, m-Nakagami and more, this suboptimal approach leads to both an asymptotically (in K) optimal sum-rate and asymptotically optimal minimal rate. Our non-opportunistic approach achieves asymptotically full multiuser diversity and optimal fairness, in contrast to all existing limited feedback algorithms.

KW - Channel State Information

KW - Multiuser Diversity

KW - Random Bipartite Graphs

KW - Resource Allocation

UR - http://www.scopus.com/inward/record.url?scp=85049164424&partnerID=8YFLogxK

U2 - 10.1109/WCNC.2018.8376960

DO - 10.1109/WCNC.2018.8376960

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85049164424

T3 - IEEE Wireless Communications and Networking Conference, WCNC

SP - 1

EP - 6

BT - 2018 IEEE Wireless Communications and Networking Conference, WCNC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Wireless Communications and Networking Conference, WCNC 2018

Y2 - 15 April 2018 through 18 April 2018

ER -