Asymptotically Optimal Resource Block Allocation With Limited Feedback

TL;DR

This paper proposes a limited feedback resource allocation method for frequency-selective channels that achieves asymptotic optimality in sum-rate and fairness with minimal feedback, outperforming existing algorithms.

Contribution

It introduces a suboptimal, low-feedback approach that guarantees asymptotic optimality in sum-rate and fairness for large networks.

Findings

Achieves asymptotic optimality in sum-rate for various fading distributions.

Requires significantly less than one bit of feedback per user per channel.

Ensures full multiuser diversity and optimal fairness asymptotically.

Abstract

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 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, by contrast to all other limited feedback algorithms.