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Content-Specific Broadcast Cellular Networks based on User Demand Prediction: A Revenue Perspective
The Long Term Evolution (LTE) broadcast is a promising solution to cope with
exponentially increasing user traffic by broadcasting common user requests over
the same frequency channels. In this paper, we propose a novel network
framework provisioning broadcast and unicast services simultaneously. For each
serving file to users, a cellular base station determines either to broadcast
or unicast the file based on user demand prediction examining the file's
content specific characteristics such as: file size, delay tolerance, price
sensitivity. In a network operator's revenue maximization perspective while not
inflicting any user payoff degradation, we jointly optimize resource
allocation, pricing, and file scheduling. In accordance with the state of the
art LTE specifications, the proposed network demonstrates up to 32% increase in
revenue for a single cell and more than a 7-fold increase for a 7 cell
coordinated LTE broadcast network, compared to the conventional unicast
cellular networks.Comment: 6 pages; This paper will appear in the Proc. of IEEE WCNC 201
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
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