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.
We study transmission rate control and performance delay in cognitive radio (CR) links from a cross-layer perspective.We assume a hierarchical CR network where the secondary users (SU) access the spectrum band in an opportunistic and noncooperative way. The SU goal is to transmit a fixed-size file (fixed amount of data packets) during the sojourn time of the primary users (PU's) idle state. We assume that the SU's support frames retransmission through an automatic repeat request (ARQ) mechanism. By formulating the problem as a Markov decision process, we demonstrate that there is always an optimal stationary rate adaptation policy, and we propose a simple algorithm to obtain it. We derive an exact c1osed form expression for the probability of successful transmission as a function of the PU's access probability and the signal-to noise ratio at the link receiver. We also study the performance delay, understood as the time required to transmit the entire data file, taking into account frames retransmission. To do that, we analyze the Markov process associated with the optimal rate policy in the transform domain. Then, using probabilistic f1ow graph techniques, we derive exact closed-form expressions for the statistical distribution of transmission delay.
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