37,834 research outputs found
Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks
Emerging 5G systems will need to efficiently support both enhanced mobile
broadband traffic (eMBB) and ultra-low-latency communications (URLLC) traffic.
In these systems, time is divided into slots which are further sub-divided into
minislots. From a scheduling perspective, eMBB resource allocations occur at
slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively
overlapped at the minislot timescale, resulting in selective
superposition/puncturing of eMBB allocations. This approach enables minimal
URLLC latency at a potential rate loss to eMBB traffic.
We study joint eMBB and URLLC schedulers for such systems, with the dual
objectives of maximizing utility for eMBB traffic while immediately satisfying
URLLC demands. For a linear rate loss model (loss to eMBB is linear in the
amount of URLLC superposition/puncturing), we derive an optimal joint
scheduler. Somewhat counter-intuitively, our results show that our dual
objectives can be met by an iterative gradient scheduler for eMBB traffic that
anticipates the expected loss from URLLC traffic, along with an URLLC demand
scheduler that is oblivious to eMBB channel states, utility functions and
allocation decisions of the eMBB scheduler. Next we consider a more general
class of (convex/threshold) loss models and study optimal online joint
eMBB/URLLC schedulers within the broad class of channel state dependent but
minislot-homogeneous policies. A key observation is that unlike the linear rate
loss model, for the convex and threshold rate loss models, optimal eMBB and
URLLC scheduling decisions do not de-couple and joint optimization is necessary
to satisfy the dual objectives. We validate the characteristics and benefits of
our schedulers via simulation
Power adjustment and scheduling in OFDMA femtocell networks
Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces like office buildings, subways, and academic buildings. These networks can increase throughput for users, but edge users can suffer from co-channel interference, leading to service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS determines cell sizes using a Voronoi-Laguerre framework, then schedules users using a scheduling algorithm that includes vacancy requests to increase fairness in dense femtocell networks. We prove that our algorithm always terminate in a finite time, producing a configuration that guarantees user or area coverage. Simulation results show a decrease in outage probability of up to 50%, as well as an increase in Jain's fairness index of almost 200%
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