Abstract
Abstract 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.
Highlights
In wireless networks, mobile devices are usually battery powered with a limited amount of energy
We present a crosslayer adaptive algorithm that dynamically maximizes the average utility function of a carrier sense multiple access (CSMA)-based wireless link
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
Summary
Mobile devices are usually battery powered with a limited amount of energy. 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). The numerical simulations show the benefits of the proposed adaptation algorithms in terms of energy efficiency, and highlight the trade-off among energy consumption, smoothed data rate, and delay in links of a CSMA network They show that the use of suitable Markov model for the wireless channel improves performance of the adaptation algorithm, mainly for slow fading channels. Where the action and state vectors as well as the per stage utility function are defined similar to the FTH problem We consider both the first- and second-order models for the channel state by applying appropriate format of sk. The system state definition can support uncorrelated, first- and second-order channel models so we do not limit the solution to any specific channel model
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