Research

Error control (error detection and correction) is a technique that enables reliable delivery of digital data over unreliable communication channels. Communication channels are subject to channel noise, by which errors may be introduced during transmission from the source to a receiver. Error detection technique allows detecting such errors, while error correction enables reconstruction of the original data.

We focus on the following issues:

• Advanced error correction codes : LDPC codes, turbo codes, polar codes, etc.

• Hybrid-ARQ : combination of error correction codes and ARQ

• Architecture design for efficient encoder/decoder of error correction codes for wireless/wired communication systems

In radio, multiple-input and multiple-output, or MIMO is a method for multiplying the capacity of a radio link using multiple transmit and receive antennas to exploit multipath propagation. MIMO has become an essential element of wireless communication standards including IEEE 802.11n (Wi-Fi), IEEE 802.11ac (Wi-Fi), HSPA+ (3G), WiMAX (4G), and LTE (4G).

We focus on the following issues:

• Error control for MIMO transmission

• Massive MIMO system

• Spatial Modulation

User Equipments in proximity can communicate with each other directly without base station (BS) intervention or with reduced BS intervention. In the scenario of D2D underlay cellular networks, D2D links are allowed to share channels already occupied by cellular users without imposing heavy load to the network and violating QoS of cellular links.

We focus on the following issues:

• Multi-Band Resource Allocation

• Interference Management

• Group D2D and Data Dissemination

Learning algorithm can be applied to design and operate communication systems. Deep Neural Networks can be used to identify complex functions representing operations of communication systems. (Deep) Reinforcement Learning can be used to allocate communication resources to User Equipments in complex networks.

We focus on the following issues:

• Reinforcement Learning (RL)/Deep Reinforcement Learning (DRL)

• AI-based Resource Allocation for D2D and V2X

• Deep Learning based massive MIMO