Channel-predictive link layer ARQ protocols in wireless networks
Department of Electrical and Computer Engineering
Master of Science
De, Swades K.
Automatic Repeat Request (ARQ) protocol
Communication performance over a wireless channel should be considered according to two main parameters: energy and throughput. The reliable data transfer is a key to these goals. The reliable node-to-node data transfer is performed by link layer protocols. One prominent approach is Automatic Repeat Request (ARQ) protocol. The traditional ARQ protocols attempt to recover the erroneously transmitted frames by retransmitting those frames, regardless of the channel state. Since this channel state unaware behaviour may cause unnecessary retransmissions, traditional ARQ protocols are expected to be energy inefficient. Some ideas have been proposed such as stochastic learning automaton based ARQ, and channel probing based ARQ. However, these algorithms do not attempt to estimate the channel's existing condition. Instead, the retransmission decision is made according to a simple feedback, on whether the previous frame was successful.
This thesis presents four proposed algorithms, which incorporates the channel state estimate in the feedback process to judiciously select a frame (re)transmission timing instant. Algorithms have been applied on Stop-and-Wait (S-W) ARQ, and the performance have been compared with respect to simple S-W ARQ, and probing based S-W ARQ. In probing based ARQ, when the channel deteriorates, transmitter starts probing channel periodically, but the periodicity is chosen arbitrarily, regardless of the fading state. In contrast, the proposed algorithms estimate the channel's existing condition by using feedbacks, and the probing interval is chosen according to the Average Fading Duration (AFD) of received signal. Simulations are performed with Rayleigh Fading Channel. The performance results show that at the cost of some additional delay, significiant gain on energy saving and throughput performance can be achieved when AFD based intelligent probing is done.
njit-etd2007-004 (80 pages ~ 3,019 KB pdf)
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Created March 30, 2011