Adaptive Negotiation for Block Acknowledgment Session Management

International audienceThe expansion of wireless applications in dense environments raises many technical issues. The 802 standards need to adapt and enhance the network quality by developing new technologies. The block acknowledgment (BA) mechanism was introduced in the IEEE 802.11e standard to improve medium access control (MAC) efficiency. It requires the exchange of many control frames to establish a session with each user, which turns into an issue for networks in dense environments as it causes increased overhead and latency. This paper deals with the optimization of the BA session management procedure. We propose a modified block acknowledgment session control mechanism which reduces the overhead and latency compared to the original one

Efficient Deep Unfolding for SISO-OFDM Channel Estimation

In modern communication systems, channel state information is of paramount importance to achieve capacity. It is then crucial to accurately estimate the channel. It is possible to perform SISO-OFDM channel estimation using sparse recovery techniques. However, this approach relies on the use of a physical wave propagation model to build a dictionary, which requires perfect knowledge of the system's parameters. In this paper, an unfolded neural network is used to lighten this constraint. Its architecture, based on a sparse recovery algorithm, allows SISO-OFDM channel estimation even if the system's parameters are not perfectly known. Indeed, its unsupervised online learning allows to learn the system's imperfections in order to enhance the estimation performance. The practicality of the proposed method is improved with respect to the state of the art in two aspects: constrained dictionaries are introduced in order to reduce sample complexity and hierarchical search within dictionaries is proposed in order to reduce time complexity. Finally, the performance of the proposed unfolded network is evaluated and compared to several baselines using realistic channel data, showing the great potential of the approach

Efficient Deep Unfolding for SISO-OFDM Channel Estimation

In modern communication systems, channel state information is of paramount importance to achieve capacity. It is then crucial to accurately estimate the channel. It is possible to perform SISO-OFDM channel estimation using sparse recovery techniques. However, this approach relies on the use of a physical wave propagation model to build a dictionary, which requires perfect knowledge of the system's parameters. In this paper, an unfolded neural network is used to lighten this constraint. Its architecture, based on a sparse recovery algorithm, allows SISO-OFDM channel estimation even if the system's parameters are not perfectly known. Indeed, its unsupervised online learning allows to learn the system's imperfections in order to enhance the estimation performance. The practicality of the proposed method is improved with respect to the state of the art in two aspects: constrained dictionaries are introduced in order to reduce sample complexity and hierarchical search within dictionaries is proposed in order to reduce time complexity. Finally, the performance of the proposed unfolded network is evaluated and compared to several baselines using realistic channel data, showing the great potential of the approach

Efficient Deep Unfolding for SISO-OFDM Channel Estimation

In modern communication systems, channel state information is of paramount importance to achieve capacity. It is then crucial to accurately estimate the channel. It is possible to perform SISO-OFDM channel estimation using sparse recovery techniques. However, this approach relies on the use of a physical wave propagation model to build a dictionary, which requires perfect knowledge of the system's parameters. In this paper, an unfolded neural network is used to lighten this constraint. Its architecture, based on a sparse recovery algorithm, allows SISO-OFDM channel estimation even if the system's parameters are not perfectly known. Indeed, its unsupervised online learning allows to learn the system's imperfections in order to enhance the estimation performance. The practicality of the proposed method is improved with respect to the state of the art in two aspects: constrained dictionaries are introduced in order to reduce sample complexity and hierarchical search within dictionaries is proposed in order to reduce time complexity. Finally, the performance of the proposed unfolded network is evaluated and compared to several baselines using realistic channel data, showing the great potential of the approach

PHY+MAC channel sounding interval analysis for IEEE 802.11ac MU-MIMO

International audienc

Analyse des performances de la méthode de négociation adaptative de la session Block Acknowledgment pour le standard IEEE 802.11ax

International audienc

Cross-layer multichannel aggregation for future WLAN systems

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SU/MU-MIMO in IEEE 802.11ac: PHY + MAC performance comparison for single antenna stations

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Novel cross-layer simulation platform to include realistic channel modelling in system simulations

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