9,266 research outputs found
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
Algorithmic Aspects of Energy-Delay Tradeoff in Multihop Cooperative Wireless Networks
We consider the problem of energy-efficient transmission in delay constrained
cooperative multihop wireless networks. The combinatorial nature of cooperative
multihop schemes makes it difficult to design efficient polynomial-time
algorithms for deciding which nodes should take part in cooperation, and when
and with what power they should transmit. In this work, we tackle this problem
in memoryless networks with or without delay constraints, i.e., quality of
service guarantee. We analyze a wide class of setups, including unicast,
multicast, and broadcast, and two main cooperative approaches, namely: energy
accumulation (EA) and mutual information accumulation (MIA). We provide a
generalized algorithmic formulation of the problem that encompasses all those
cases. We investigate the similarities and differences of EA and MIA in our
generalized formulation. We prove that the broadcast and multicast problems
are, in general, not only NP hard but also o(log(n)) inapproximable. We break
these problems into three parts: ordering, scheduling and power control, and
propose a novel algorithm that, given an ordering, can optimally solve the
joint power allocation and scheduling problems simultaneously in polynomial
time. We further show empirically that this algorithm used in conjunction with
an ordering derived heuristically using the Dijkstra's shortest path algorithm
yields near-optimal performance in typical settings. For the unicast case, we
prove that although the problem remains NP hard with MIA, it can be solved
optimally and in polynomial time when EA is used. We further use our algorithm
to study numerically the trade-off between delay and power-efficiency in
cooperative broadcast and compare the performance of EA vs MIA as well as the
performance of our cooperative algorithm with a smart noncooperative algorithm
in a broadcast setting.Comment: 12 pages, 9 figure
Optimal CSMA-based Wireless Communication with Worst-case Delay and Non-uniform Sizes
Carrier Sense Multiple Access (CSMA) protocols have been shown to reach the
full capacity region for data communication in wireless networks, with
polynomial complexity. However, current literature achieves the throughput
optimality with an exponential delay scaling with the network size, even in a
simplified scenario for transmission jobs with uniform sizes. Although CSMA
protocols with order-optimal average delay have been proposed for specific
topologies, no existing work can provide worst-case delay guarantee for each
job in general network settings, not to mention the case when the jobs have
non-uniform lengths while the throughput optimality is still targeted. In this
paper, we tackle on this issue by proposing a two-timescale CSMA-based data
communication protocol with dynamic decisions on rate control, link scheduling,
job transmission and dropping in polynomial complexity. Through rigorous
analysis, we demonstrate that the proposed protocol can achieve a throughput
utility arbitrarily close to its offline optima for jobs with non-uniform sizes
and worst-case delay guarantees, with a tradeoff of longer maximum allowable
delay
Q-CSMA: Queue-Length Based CSMA/CA Algorithms for Achieving Maximum Throughput and Low Delay in Wireless Networks
Recently, it has been shown that CSMA-type random access algorithms can
achieve the maximum possible throughput in ad hoc wireless networks. However,
these algorithms assume an idealized continuous-time CSMA protocol where
collisions can never occur. In addition, simulation results indicate that the
delay performance of these algorithms can be quite bad. On the other hand,
although some simple heuristics (such as distributed approximations of greedy
maximal scheduling) can yield much better delay performance for a large set of
arrival rates, they may only achieve a fraction of the capacity region in
general. In this paper, we propose a discrete-time version of the CSMA
algorithm. Central to our results is a discrete-time distributed randomized
algorithm which is based on a generalization of the so-called Glauber dynamics
from statistical physics, where multiple links are allowed to update their
states in a single time slot. The algorithm generates collision-free
transmission schedules while explicitly taking collisions into account during
the control phase of the protocol, thus relaxing the perfect CSMA assumption.
More importantly, the algorithm allows us to incorporate mechanisms which lead
to very good delay performance while retaining the throughput-optimality
property. It also resolves the hidden and exposed terminal problems associated
with wireless networks.Comment: 12 page
Cross-layer Congestion Control, Routing and Scheduling Design in Ad Hoc Wireless Networks
This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc
wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints.
By dual decomposition, the resource allocation problem
naturally decomposes into three subproblems: congestion control,
routing and scheduling that interact through congestion price.
The global convergence property of this algorithm is proved. We
next extend the dual algorithm to handle networks with timevarying
channels and adaptive multi-rate devices. The stability
of the resulting system is established, and its performance is
characterized with respect to an ideal reference system which
has the best feasible rate region at link layer.
We then generalize the aforementioned results to a general
model of queueing network served by a set of interdependent
parallel servers with time-varying service capabilities, which
models many design problems in communication networks. We
show that for a general convex optimization problem where a
subset of variables lie in a polytope and the rest in a convex set,
the dual-based algorithm remains stable and optimal when the
constraint set is modulated by an irreducible finite-state Markov
chain. This paper thus presents a step toward a systematic way
to carry out cross-layer design in the framework of “layering as
optimization decomposition” for time-varying channel models
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