Asymptotically Optimal Resource Block Allocation With Limited Feedback

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.

Multi-User Small Base Station Association via Contextual Combinatorial Volatile Bandits

“…However, it is shown in earlier works [11], [12] that an equal power allocation yields performance very close to optimal. Therefore, the assignment problem appears to be more challenging than power allocation [13]. Since each SBS can only serve a limited number of users, the user association problem in a small cell network is much more challenging than the typical classical cellular user association problem [6].…”

“…Earlier works solve the user-SBS associations for multiple SBSs or user-channel association for single SBS without considering the user mobility or volatile nature of the network [5], [6], [13], [18]. It is worth noting that user mobility and volatility can quickly degrade the performance of the solution considered to be optimal for the static scenario.…”

“…We assume that the orthogonal RBs allow fixed airtime usage fraction for each SBS in the system and fixed modulation and/or coding scheme (MCS), and thus, the transmission rate r is fixed for all SBSs. Furthermore, SBS m adopts FDMA, i.e., the SBS's spectrum is divided into q m channels, and thus, the number of users served by SBS m is upper bounded by q m [13], [16], or it can allocate q m 1 For an integer M > 0, [M ] = {1, . .…”

“…two-dimensional time-frequency orthogonal resource blocks (RBs) [37]. Since each SBS can serve multiple users, we refer each of its partitioned slot (e.g., resource blocks (RBs) [37] or subcarriers [13]) as a channel, i.e., SBS channel (SBSC). The set of such channels for SBS m is denoted by C m and its cardinality is restricted by the capacity q m of SBS m, i.e., C m := {c m,1 , .…”

“…We consider a very general heterogeneous network, where a user may experience different channel gains, not only over SBSs but also over SBSCs [13], [37], [38]. Note that our proposed model includes as a special case a simplified scenario, where there is only a single SBS with numerous SBSCs.…”