Scalable Spectrum Allocation and User Association in Networks With Many Small Cells

Abstract

A scalable framework is developed to allocate radio resources across a large number of densely deployed small cells with given traffic statistics on a slow timescale. Joint user association and spectrum allocation is first formulated as a convex optimization problem by dividing the spectrum among all possible transmission patterns of active access points (APs). To improve scalability with the number of APs, the problem is reformulated using local patterns of interfering APs. To maintain global consistency among local patterns, inter-cluster interaction is characterized as hyper-edges in a hyper-graph with nodes corresponding to neighborhoods of APs. A scalable solution is obtained by iteratively solving a convex optimization problem for bandwidth allocation with reduced complexity and constructing a global spectrum allocation using hyper-graph coloring. Numerical results demonstrate the proposed solution for a network with 100 APs and several hundred user equipments. For a given quality of service (QoS), the proposed scheme can increase the network capacity several fold compared to assigning each user to the strongest AP with full-spectrum reuse.

Scalable spectrum allocation for large networks based on sparse optimization

“…The optimization problem is convex if u is concave in r = [r 1 , · · · , r k ]. As in [6], we will take u to be the average packet delay, given by…”

“…The objective is to optimize a network utility, such as average delay, given traffic statistics over a geographic region that change slowly relative to channel fading. Our approach builds on our prior work [4], [6] in which for a network of n APs and k mobiles (or User Equipments (UEs)), the spectrum is partitioned into 2 n patterns, corresponding to all possible subsets of active APs. The problem is to optimize the widths of spectrum segments, associated with the different patterns, along with the user association under each pattern.…”

“…Each AP has its own interference neighborhood, which captures the interference from nearby APs. The challenge with the approach in [6] is to ensure that the spectrum assigned to each particular AP is consistent across the neighborhoods to which it belongs. To accomplish that, the spectrum is discretized and a coloring algorithm is proposed to ensure that the local patterns of active APs are globally consistent.…”

“…The approach to scalability presented here has the advantage of eliminating the combinatorial coloring problem that arises in [6]. Instead, a new 0 constraint is introduced.…”

“…SYSTEM MODEL As in [6], we consider a network containing the set of APs N = {1, · · · , n} and the set of UEs K = {1, · · · , k}. Each "UE" is actually associated with a particular location, and could refer to a group of nearby mobiles.…”