2 research outputs found
Is Deadline Oblivious Scheduling Efficient for Controlling Real-Time Traffic in Cellular Downlink Systems?
The emergence of bandwidth-intensive latency-critical traffic in 5G Networks,
such as Virtual Reality, has motivated interest in wireless resource allocation
problems for flows with hard-deadlines. Attempting to solve this problem brings
about two challenges: (i) The flow arrival and the channel state are not known
to the Base Station (BS) apriori, thus, the allocation decisions need to be
made online. (ii) Wireless resource allocation algorithms that attempt to
maximize a reward will likely be unfair, causing unacceptable service for some
users. We model the problem as an online convex optimization problem. We
propose a primal-dual Deadline-Oblivious (DO) algorithm, and show it is
approximately 3.6-competitive. Furthermore, we show via simulations that our
algorithm tracks the prescient offline solution very closely, significantly
outperforming several existing algorithms. In the second part, we impose a
stochastic constraint on the allocation, requiring a guarantee that each user
achieves a certain timely throughput (amount of traffic delivered within the
deadline over a period of time). We propose the Long-term Fair Deadline
Oblivious (LFDO) algorithm for that setup. We combine the Lyapunov framework
with analysis of online algorithms, to show that LFDO retains the
high-performance of DO, while satisfying the long-term stochastic constraints
Dynamic Power Control for Time-Critical Networking with Heterogeneous Traffic
Future wireless networks will be characterized by heterogeneous traffic
requirements. Such requirements can be low-latency or minimum-throughput.
Therefore, the network has to adjust to different needs. Usually, users with
low-latency requirements have to deliver their demand within a specific time
frame, i.e., before a deadline, and they co-exist with throughput oriented
users. In addition, the users are mobile and they share the same wireless
channel. Therefore, they have to adjust their power transmission to achieve
reliable communication. However, due to the limited power budget of wireless
mobile devices, a power-efficient scheduling scheme is required by the network.
In this work, we cast a stochastic network optimization problem for minimizing
the packet drop rate while guaranteeing a minimum throughput and taking into
account the limited-power capabilities of the users. We apply tools from
Lyapunov optimization theory in order to provide an algorithm, named Dynamic
Power Control (DPC) algorithm, that solves the formulated problem in realtime.
It is proved that the DPC algorithm gives a solution arbitrarily close to the
optimal one. Simulation results show that our algorithm outperforms the
baseline Largest-Debt-First (LDF) algorithm for short deadlines and multiple
users.Comment: Submitted in a journa