Diversity in synchronization for scheduled OFDM time-division cooperative transmission

Lin, Qiongjie; Weitnauer, Mary Ann

doi:10.1109/milcom.2015.7357504

Cited by 2 publications

(2 citation statements)

References 14 publications

(24 reference statements)

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“…The expected total discounted rewards of the non-CT network, which are constant, are also plotted for reference. The CT overhead is modeled as an extended packet transmission time, which is a result of the extra preamble symbols in front of the packet payload to achieve diversity and synchronization for the purpose of range extension [12, 48]. Specifically, the horizontal axis in Figure 9 is the CT overhead, quantified in terms of the fraction of the total packet length, from 0 to 10%.…”

Section: Numerical Evaluationsmentioning

confidence: 99%

“…CT range extension has been experimentally demonstrated in [8], and it shows significant impact on Layer Two and Layer Three of a WSN by eliminating the “energy hole” in EC network [9, 10] and by providing better Quality of Service in EH networks [11]. Time-division CT (TDCT) is one type of cooperation, in which the source node multicasts the packet, and the cooperators that decode the packet forward it in orthogonal time slot [12]. Our previous work OSC-MAC [13] shows notable lifetime improvement over state-of-the-art MAC protocols by using TDCT to balance energy.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Multihop Wireless Sensor Networks with MAC, Queuing, and Cooperation

Lin

Weitnauer

2016

International Journal of Distributed Sensor Networks

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We present a Markovian decision process (MDP) framework for multihop wireless sensor networks (MHWSNs) to bound the network performance of both energy constrained (EC) networks and energy harvesting (EH) networks, both with and without relay cooperation. The model provides the fundamental performance limit that a MHWSN can theoretically achieve, under the general constraints from medium access control, routing, and energy harvesting. We observe that the analyses for EC and EH networks fall into two branches of MDP theory, which are finite-horizon process and infinite-horizon process, respectively. The performance metrics for EC and EH networks are different. For EC networks, an appropriate metric is the network lifetime; for EH networks, an appropriate metric is, for example, the network throughput. To efficiently solve the models with high dimension, for the EC networks, we propose a novel computational algorithm by taking advantage of the stochastic shortest path structure of the problem; for the EH networks, we propose a dual linear programming based algorithm by considering the sparsity of the transition matrix. Under the unified MDP framework, numerical results demonstrate the advantages of cooperation for the optimal performance, in both EC and EH networks.

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