Lingjia Liu mainly focuses on Computer network, Telecommunications link, Base station, Wireless network and Electronic engineering. Computer network is closely attributed to Wireless in his research. His Telecommunications link study incorporates themes from MIMO, Throughput and Cellular network.
His work carried out in the field of MIMO brings together such families of science as Real-time computing and Direction of arrival. His Base station research is multidisciplinary, relying on both Transmitter and Antenna. The various areas that Lingjia Liu examines in his Electronic engineering study include Scheduling and Spectral efficiency.
His main research concerns Computer network, Wireless, MIMO, Base station and Telecommunications link. His research in Computer network intersects with topics in Wireless network and Throughput. His work deals with themes such as Markov process, Energy consumption, Transmission, Communication channel and Communications system, which intersect with Wireless.
His MIMO research is multidisciplinary, incorporating perspectives in Algorithm, Electronic engineering, Channel state information and Orthogonal frequency-division multiplexing. His Base station research focuses on Channel and how it relates to Subframe. His study on Telecommunications link also encompasses disciplines like
Lingjia Liu mainly investigates Artificial neural network, Reservoir computing, Recurrent neural network, Wireless and Artificial intelligence. Lingjia Liu interconnects Electronic engineering, Reduction and Computer engineering in the investigation of issues within Artificial neural network. His Reservoir computing research includes themes of Training set, Perceptron, Algorithm, Channel state information and MIMO-OFDM.
He combines subjects such as Cellular network, Computer network, Base station, Beamforming and Reinforcement learning with his study of Wireless. His Routing protocol study, which is part of a larger body of work in Computer network, is frequently linked to Scheme, bridging the gap between disciplines. His Base station research also works with subjects such as
Lingjia Liu spends much of his time researching Wireless, Artificial neural network, Artificial intelligence, Algorithm and Wireless network. His study looks at the relationship between Wireless and fields such as Reinforcement learning, as well as how they intersect with chemical problems. His Artificial neural network research includes themes of Network architecture, Electronic engineering and Reduction.
The Wireless network study combines topics in areas such as Interference, Swarm behaviour, Real-time computing and Base station. His research investigates the connection between Real-time computing and topics such as Transmission that intersect with issues in Cellular network. He has researched Symbol in several fields, including Minimum mean square error, Telecommunications link and Orthogonal frequency-division multiplexing.
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Downlink MIMO in LTE-advanced: SU-MIMO vs. MU-MIMO
Lingjia Liu;Runhua Chen;S. Geirhofer;K. Sayana.
IEEE Communications Magazine (2012)
Automated Residential Demand Response: Algorithmic Implications of Pricing Models
Ying Li;Boon Loong Ng;M. Trayer;Lingjia Liu.
IEEE Transactions on Smart Grid (2012)
Big Data Meet Cyber-Physical Systems: A Panoramic Survey
Rachad Atat;Lingjia Liu;Jinsong Wu;Guangyu Li.
IEEE Access (2018)
Enabling cyber-physical communication in 5G cellular networks: challenges, spatial spectrum sensing, and cyber-security
Rachad Atat;Lingjia Liu;Hao Chen;Jinsong Wu.
IET Cyber-Physical Systems: Theory & Applications (2017)
Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G
Rubayet Shafin;Lingjia Liu;Vikram Chandrasekhar;Hao Chen.
IEEE Wireless Communications (2020)
Resource Allocation and Quality of Service Evaluation for Wireless Communication Systems Using Fluid Models
Lingjia Liu;P. Parag;Jia Tang;Wei-Yu Chen.
IEEE Transactions on Information Theory (2007)
Inter-cell interference avoidance for downlink transmission
Lingjia Liu;Jianzhong Zhang;Zhouyue Pi.
Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach
Hao-Hsuan Chang;Hao Song;Yang Yi;Jianzhong Zhang.
IEEE Internet of Things Journal (2019)
Reservoir Computing Meets Smart Grids: Attack Detection Using Delayed Feedback Networks
Kian Hamedani;Lingjia Liu;Rachad Atat;Jinsong Wu.
IEEE Transactions on Industrial Informatics (2018)
Method and apparatus for uplink transmissions and CQI reports with carrier aggregation
Jianzhong Zhang;Lingjia Liu;Juho Lee;Young-Han Nam.
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