The IEEE 802.11 standard conformant wireless communication stations have multi-rate transmission capability. To achieve greater communication efficiency, multirate capable stations use rate-adaptation to select appropriate transmission rate according to variations in the channel quality. This paper proposes a novel rate-adaptation scheme where the decision of rate selection relies on the mutual feedback of a transmitter and receiver pair. As an important feature of a rate-adaptation scheme, our proposed solution is highly responsive when compared with existing rateadaptation scheme. Using the mutual-feedback, it has been made possible to achieve frame-loss differentiation with just three frames, avoiding the use of RTS/CTS and further delays in this process. Unlike the previous approaches, the feedback is delivered to the transmitter without any changes to the standard frame format. Various performance tests assert the suitability of our proposed solution in different test scenarios over existing rate-adaptation schemes.
International audienceIn this paper we present the results of real-life localization experiments performed in an unprecedented cooperative and heterogeneous wireless context. These measurements are based on ZigBee and orthogonal frequency division multiplexing (OFDM) devices, respectively endowed with received signal strength indicator (RSSI) and round trip delay (RTD) estimation capabilities. More particularly we emulate a multi-standard terminal, moving in a typical indoor environment, while communicating with fixed OFDM-based femto-base stations (Femto-BSs) and with other mobiles or fixed anchor nodes (through peer-to-peer links) forming a wireless sensor network (WSN). We introduce the measurement functionalities and metrics, the scenario and set-up, providing realistic connectivity and obstruction conditions. Out of the experimental data, preliminary positioning results based on cooperative and geometric algorithms are finally discussed, showing benefits through mobile-to-mobile cooperation, selective hybrid data fusion and detection of unreliable nodes
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