Traffic Driven Resource Allocation in Heterogenous Wireless Networks

Most work on wireless network resource allocation use physical layer performance such as sum rate and outage probability as the figure of merit. These metrics may not reflect the true user QoS in future heterogenous networks (HetNets) with many small cells, due to large traffic variations in overlapping cells with complicated interference conditions. This paper studies the spectrum allocation problem in HetNets using the average packet sojourn time as the performance metric. To be specific, in a HetNet with $K$ base terminal stations (BTS's), we determine the optimal partition of the spectrum into $2^K$ possible spectrum sharing combinations. We use an interactive queueing model to characterize the flow level performance, where the service rates are decided by the spectrum partition. The spectrum allocation problem is formulated using a conservative approximation, which makes the optimization problem convex. We prove that in the optimal solution the spectrum is divided into at most $K$ pieces. A numerical algorithm is provided to solve the spectrum allocation problem on a slow timescale with aggregate traffic and service information. Simulation results show that the proposed solution achieves significant gains compared to both orthogonal and full spectrum reuse allocations with moderate to heavy traffic.Comment: 6 pages, 5 figures IEEE GLOBECOM 2014 (accepted for publication

Traffic-Driven Spectrum Allocation in Heterogeneous Networks

Next generation cellular networks will be heterogeneous with dense deployment of small cells in order to deliver high data rate per unit area. Traffic variations are more pronounced in a small cell, which in turn lead to more dynamic interference to other cells. It is crucial to adapt radio resource management to traffic conditions in such a heterogeneous network (HetNet). This paper studies the optimization of spectrum allocation in HetNets on a relatively slow timescale based on average traffic and channel conditions (typically over seconds or minutes). Specifically, in a cluster with $n$ base transceiver stations (BTSs), the optimal partition of the spectrum into $2^n$ segments is determined, corresponding to all possible spectrum reuse patterns in the downlink. Each BTS's traffic is modeled using a queue with Poisson arrivals, the service rate of which is a linear function of the combined bandwidth of all assigned spectrum segments. With the system average packet sojourn time as the objective, a convex optimization problem is first formulated, where it is shown that the optimal allocation divides the spectrum into at most $n$ segments. A second, refined model is then proposed to address queue interactions due to interference, where the corresponding optimal allocation problem admits an efficient suboptimal solution. Both allocation schemes attain the entire throughput region of a given network. Simulation results show the two schemes perform similarly in the heavy-traffic regime, in which case they significantly outperform both the orthogonal allocation and the full-frequency-reuse allocation. The refined allocation shows the best performance under all traffic conditions.Comment: 13 pages, 11 figures, accepted for publication by JSAC-HC

Scalable Spectrum Allocation for Large Networks Based on Sparse Optimization

Joint allocation of spectrum and user association is considered for a large cellular network. The objective is to optimize a network utility function such as average delay given traffic statistics collected over a slow timescale. A key challenge is scalability: given $n$ Access Points (APs), there are $O(2^n)$ ways in which the APs can share the spectrum. The number of variables is reduced from $O(2^n)$ to $O(nk)$, where $k$ is the number of users, by optimizing over local overlapping neighborhoods, defined by interference conditions, and by exploiting the existence of sparse solutions in which the spectrum is divided into $k+1$ segments. We reformulate the problem by optimizing the assignment of subsets of active APs to those segments. An $\ell_0$ constraint enforces a one-to-one mapping of subsets to spectrum, and an iterative (reweighted $\ell_1$) algorithm is used to find an approximate solution. Numerical results for a network with 100 APs serving several hundred users show the proposed method achieves a substantial increase in total throughput relative to benchmark schemes.Comment: Submitted to the IEEE International Symposium on Information Theory (ISIT), 201