Adaptive access and rate control of CSMA for energy, rate, and delay optimization
Mahdi Khodaian
2
Jess Prez
0
Babak H Khalaj
2
Pedro M Crespo
1
0
Department of Communication Engineering, University of Cantabria
, Santander,
Spain
1
CEIT and TECNUN (University of Navarra)
, 20009, San Sebastian,
Spain
2
Department of Electrical Engineering, Sharif University of Technology
, Tehran,
Iran
In this article, we present a cross-layer adaptive algorithm that dynamically maximizes the average utility function. A per stage utility function is defined for each link of a carrier sense multiple access-based wireless network as a weighted concave function of energy consumption, smoothed rate, and smoothed queue size. Hence, by selecting weights we can control the trade-off among them. Using dynamic programming, the utility function is maximized by dynamically adapting channel access, modulation, and coding according to the queue size and quality of the time-varying channel. We show that the optimal transmission policy has a threshold structure versus the channel state where the optimal decision is to transmit when the wireless channel state is better than a threshold. We also provide a queue management scheme where arrival rate is controlled based on the link state. Numerical results show characteristics of the proposed adaptation scheme and highlight the trade-off among energy consumption, smoothed data rate, and link delay.
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access schemes are used to efficiently exploit channel
resources. In such systems, there are more users than
available channels, and at any given time only a subset of
users can access the channels. Therefore, the optimality of
channel access decision is crucial in random access
networks. Random access is widely used in ad hoc networks
as it can be implemented in a distributed manner.
Wireless local area networks (WLAN) and practical personal or
sensor networks usually use random access control in
their ad hoc operation mode [9,10]. On the other hand, it
is shown recently that CSMA protocols can achieve
maximum stable throughput [11] while keeping bounded
queuing delay [12], and it can achieve a collision free WLAN
[13].
Optimization of random access networks was first
proposed in order to achieve single hop proportional fairness
for slotted ALOHA networks [14]. Different types of
fairness are also considered and random access control is
modeled as a utility maximization problem in [15]. In
addition, the cross-layer optimization problem of random
access control and transmission control protocol is solved
as a network utility maximization problem [16].
Newtonlike algorithms are also provided for energy and
throughput optimization with end-to-end delay constraint in multi
hop random access network [17]. However, in the
aforementioned articles static transmission probability was
used and opportunity of time varying and adaptive control
was ignored.
On the other hand, queue-based random access
algorithms were studied in [18], where access probabilities
are assumed to be adapted based on queue sizes.
Stability of the proposed algorithms was verified and their
delay performance was shown to surpass fixed
optimization algorithms. Also a heuristic differential queue-based
scheduling algorithm is proposed in [19] which shows
superior performance compared to 802.11 through
experimental results. However, such queue-based
algorithms are inappropriate for fading channels and
prioritize links with low channel quality, which results in low
energy efficiency [20].
In this article, we propose cross-layer adaptive
algorithms; derived from dynamic programming, for
distributed optimization of the links in CSMA-based wireless
networks operating in mobile environments. As a
performance metric, we define the per stage utility of the link as
a weighted concave function of energy consumption,
smoothed data rate, and smoothed queue size in the link,
where the weights are assigned based on the desired
tradeoff among them. The algorithms maximize the average
utility by dynamically adapting the channel access decision
and transmit data rate (by selecting different modulation
and coding schemes) according to the queue size of the
link and the availability and quality of the time-varying
channel (channel state is assumed to be known at the
transmitter). Both, finite-time horizon (FTH) and
infinitetime horizon (ITH) problems are considered. In the first
case, the utility sum is maximized for a finite time period,
whereas in the second case, the long-term average utility
is maximized.
We consider a mobile environment with frequency-flat
time-varying channel response. This requires suitable
models of the wireless channel dynamics. Here, we use
finite-state Markov chains (FSMC) to model channel
dynamics, such that channel time-correlation at network
links is partially exploited by the proposed algorithms.
Although the physical wireless channel is inherently
nonMarkovian, it has been shown that stationary Markov
chains can capture the essence of the channel dynamics
[21]. Many transmission adaptation algo (...truncated)