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The IoT paradigm holds the promise to revolutionize the way we live and work through seamless interactions between a large amount of heterogeneous devices. In such a network, connecting a large number of physical objects like humans, animals, plants, smart phones, etc. equipped with sensors to the Internet generates what is called “big data”. The main traits that differentiate IoT from other variations of wireless systems are the massive number of devices participating in the network and the stringent latency requirements for their communications. Therefore, the biggest challenge for IoT pertains in facilitating the data transmission from a tremendous number of devices as quickly as possible.
Despite the vast number of devices in the network, a notable feature of the IoT is the sparse user activity as shown in the above figure, i.e., at any particular time only a small subset of these devices are transmitting data to the BS, while the other devices are silent. Exploiting fruitfully this sparse user activity as priori information, I am interested in investigating how to apply the advanced compressed sensing techniques for grant-free user access schemes.
Publications:
- L. Liu, and W. Yu, “Massive device communication with massive MIMO,” in Proc. IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, 2017.
- L. Liu, and W. Yu, “Massive connectivity with massive MIMO-Part I: Device activity detection and channel estimation,” IEEE Trans. Signal Process., vol. 66, no. 11, pp. 2933-2946, Jun. 2018.
- L. Liu, and W. Yu, “Massive connectivity with massive MIMO-Part II: Achievable rate characterization,” IEEE Trans. Signal Process., vol. 66, no. 11, pp. 2947-2959, Jun. 2018.
- L. Liu, E. G. Larsson, W. Yu, P. Popovski, C. Stefanovic, and E. de Carvalho, “Sparse signal processing for grant-free massive connectivity: A future paradigm for random access protocols in the Internet of Things,” IEEE Signal Process. Mag., vol. 35, no. 5, pp. 88-99, Sep. 2018.
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