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

Lyapunov Optimization-Based Latency-Bounded Allocation Using Deep Deterministic Policy Gradient for 11ax Spatial Reuse

With the growing demand for wireless local area network (WLAN) applications that require low latency, orthogonal frequency-division multiple access (OFDMA) has been adopted for uplink and downlink transmissions in the IEEE 802.11ax standard to improve the spectrum efficiency and reduce the latency. In IEEE 802.11ax WLANs, OFDMA resource allocation that guarantees latency, called latency-bounded resource allocation, is more challenging than that in cellular networks because severe unmanaged interference from overlapping basic service sets is enhanced due to the concurrent-transmission mechanism newly employed in IEEE 802.11ax. To improve the downlink OFDMA resource allocation with the unmanaged interference caused by IEEE 802.11ax concurrent transmissions, we propose Lyapunov optimization-based latency-bounded allocation with reinforcement learning (RL). We focus on the transmission-queue size for each station (STA) at the access point that determines the STA latency. Using Lyapunov optimization, we formulate the resource-allocation problem with the queue-size constraints in a form that can be solved using RL (i.e., a Markov decision process) and prove the upper bound of the queue size. Our simulation results demonstrated that the proposed method, which uses an RL algorithm with a deep deterministic policy gradient, satisfied the queue-size constraints. This means that the proposed method met the latency requirements, while some baseline methods failed to meet them. Furthermore, the proposed method achieved a higher fairness index than the baseline methods

IEEE 802.11ax: challenges and requirements for future high efficiency wifi

The popularity of IEEE 802.11 based wireless local area networks (WLANs) has increased significantly in recent years because of their ability to provide increased mobility, flexibility, and ease of use, with reduced cost of installation and maintenance. This has resulted in massive WLAN deployment in geographically limited environments that encompass multiple overlapping basic service sets (OBSSs). In this article, we introduce IEEE 802.11ax, a new standard being developed by the IEEE 802.11 Working Group, which will enable efficient usage of spectrum along with an enhanced user experience. We expose advanced technological enhancements proposed to improve the efficiency within high density WLAN networks and explore the key challenges to the upcoming amendment.Peer ReviewedPostprint (author's final draft