1,186 research outputs found
Optimal Power Control and Scheduling under Hard Deadline Constraints for Continuous Fading Channels
We consider a joint scheduling-and-power-allocation problem of a downlink
cellular system. The system consists of two groups of users: real-time (RT) and
non-real-time (NRT) users. Given an average power constraint on the base
station, the problem is to find an algorithm that satisfies the RT hard
deadline constraint and NRT queue stability constraint. We propose a
sum-rate-maximizing algorithm that satisfies these constraints. We also show,
through simulations, that the proposed algorithm has an average complexity that
is close-to-linear in the number of RT users. The power allocation policy in
the proposed algorithm has a closed-form expression for the two groups of
users. However, interestingly, the power policy of the RT users differ in
structure from that of the NRT users. We also show the superiority of the
proposed algorithms over existing approaches using extensive simulations.Comment: Submitted to Asilomar 2017. arXiv admin note: text overlap with
arXiv:1612.0832
Delay Constrained Scheduling over Fading Channels: Optimal Policies for Monomial Energy-Cost Functions
A point-to-point discrete-time scheduling problem of transmitting
information bits within hard delay deadline slots is considered assuming
that the underlying energy-bit cost function is a convex monomial. The
scheduling objective is to minimize the expected energy expenditure while
satisfying the deadline constraint based on information about the unserved
bits, channel state/statistics, and the remaining time slots to the deadline.
At each time slot, the scheduling decision is made without knowledge of future
channel state, and thus there is a tension between serving many bits when the
current channel is good versus leaving too many bits for the deadline. Under
the assumption that no other packet is scheduled concurrently and no outage is
allowed, we derive the optimal scheduling policy. Furthermore, we also
investigate the dual problem of maximizing the number of transmitted bits over
time slots when subject to an energy constraint.Comment: submitted to the IEEE ICC 200
Optimal Energy Allocation For Delay-Constrained Traffic Over Fading Multiple Access Channels
In this paper, we consider a multiple-access fading channel where users
transmit to a single base station (BS) within a limited number of time slots.
We assume that each user has a fixed amount of energy available to be consumed
over the transmission window. We derive the optimal energy allocation policy
for each user that maximizes the total system throughput under two different
assumptions on the channel state information. First, we consider the offline
allocation problem where the channel states are known a priori before
transmission. We solve a convex optimization problem to maximize the
sum-throughput under energy and delay constraints. Next, we consider the online
allocation problem, where the channels are causally known to the BS and obtain
the optimal energy allocation via dynamic programming when the number of users
is small. We also develop a suboptimal resource allocation algorithm whose
performance is close to the optimal one. Numerical results are presented
showing the superiority of the proposed algorithms over baseline algorithms in
various scenarios.Comment: IEEE Global Communications Conference: Wireless Communications
(Globecom2016 WC
Delay-Optimal Buffer-Aware Probabilistic Scheduling with Adaptive Transmission
Cross-layer scheduling is a promising way to improve Quality of Service (QoS)
given a power constraint. In this paper, we investigate the system with random
data arrival and adaptive transmission. Probabilistic scheduling strategies
aware of the buffer state are applied to generalize conventional deterministic
scheduling. Based on this, the average delay and power consumption are analysed
by Markov reward process. The optimal delay-power tradeoff curve is the Pareto
frontier of the feasible delay-power region. It is proved that the optimal
delay-power tradeoff is piecewise-linear, whose vertices are obtained by
deterministic strategies. Moreover, the corresponding strategies of the optimal
tradeoff curve are threshold-based, hence can be obtained by a proposed
effective algorithm. On the other hand, we formulate a linear programming to
minimize the average delay given a fixed power constraint. By varying the power
constraint, the optimal delay-power tradeoff curve can also be obtained. It is
demonstrated that the algorithm result and the optimization result match each
other, and are further validated by Monte-Carlo simulation.Comment: 6 pages, 4 figures, accepted by IEEE ICCC 201
Energy-Efficient Transmission Scheduling with Strict Underflow Constraints
We consider a single source transmitting data to one or more receivers/users
over a shared wireless channel. Due to random fading, the wireless channel
conditions vary with time and from user to user. Each user has a buffer to
store received packets before they are drained. At each time step, the source
determines how much power to use for transmission to each user. The source's
objective is to allocate power in a manner that minimizes an expected cost
measure, while satisfying strict buffer underflow constraints and a total power
constraint in each slot. The expected cost measure is composed of costs
associated with power consumption from transmission and packet holding costs.
The primary application motivating this problem is wireless media streaming.
For this application, the buffer underflow constraints prevent the user buffers
from emptying, so as to maintain playout quality. In the case of a single user
with linear power-rate curves, we show that a modified base-stock policy is
optimal under the finite horizon, infinite horizon discounted, and infinite
horizon average expected cost criteria. For a single user with piecewise-linear
convex power-rate curves, we show that a finite generalized base-stock policy
is optimal under all three expected cost criteria. We also present the
sequences of critical numbers that complete the characterization of the optimal
control laws in each of these cases when some additional technical conditions
are satisfied. We then analyze the structure of the optimal policy for the case
of two users. We conclude with a discussion of methods to identify
implementable near-optimal policies for the most general case of M users.Comment: 109 pages, 11 pdf figures, template.tex is main file. We have
significantly revised the paper from version 1. Additions include the case of
a single receiver with piecewise-linear convex power-rate curves, the case of
two receivers, and the infinite horizon average expected cost proble
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