Dr Gaojie Chen


Lecturer in Satellite Communications
PhD, FHEA, SMIEEE

About

Research

Research interests

Grants list

  • Co-investigator– 2021-2023, EU H2020 project on “Bring Reinforcement-learning Into Radio Light Network for Massive Connections”(6G BRAINS), Overall project funding over €5.7m.
  • Co-investigator – 2018-2021, EPSRC project on “Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)”, EP/R006377/1, £330K.
  • Co-investigator – 2018-2019, University Infrastructure Funding on “5G massive MIMO testbed” (£244,000).
  • College-funded Stand-Alone PhD Studentship (Primary Supervisor) 2019-2023, College of Science and Engineering, School of Engineering, University of Leicester. “Develop and analyse Physical Layer Security for Massive Unmanned Aerial Vehicle Communication Networks” Budget £50K.
  • Attend as RA  – 2015-2018, EPSRC project on “Spatially Embedded Networks”, EP/N002350/1.
  • Attend as RA  – 2014-2015, EU 7 Framework Programme project on “Full-duplex Radios for Local Access”, FP7/2007-2013 No.316369.
  • Attend as RA  – 2013-2014, EPSRC project on “Audio and Video-Based Speech Separation for Multiple Moving Sources Within a Room Environment”, EP/H049665/1.

Publications

Qu Luo, Zilong Liu, Gaojie Chen, Pei Xiao, Yi Ma, Amine Maaref (2023)A Design of Low-Projection SCMA Codebooks for Ultra-Low Decoding Complexity in Downlink IoT Networks, In: IEEE Transactions on Wireless Communications Institute of Electrical and Electronics Engineers (IEEE)

This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise error probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization, where the resultant multi-dimensional constellation is called LP-GAM. Our analysis and simulation results show the superiority of the proposed LP codebooks (LPCBs) including one-shot decoding convergence and excellent error rate performance. In particular, the proposed LPCBs lead to decoding complexity reduction by at least 97% compared to that of the conventional codebooks, whilst owning large minimum Euclidean distance. Some examples of the proposed LPCBs are available at https://github.com/ethanlq/SCMA-codebook.

—Non-orthogonal multiple access (NOMA) is a promising candidate radio access technology for future wireless communication systems, which can achieve improved connectivity and spectral efficiency. Without sacrificing error rate performance , link adaptation combining with adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) can provide better spectral efficiency and reliable data transmission by allowing both power and rate to adapt to channel fading and enabling re-transmissions. However, current AMC or HARQ schemes may not be preferable for NOMA systems due to the imperfect channel estimation and error propagation during successive interference cancellation (SIC). To address this problem , a reinforcement learning based link adaptation scheme for downlink NOMA systems is introduced in this paper. Specifically, we first analyze the throughput and spectrum efficiency of NOMA system with AMC combined with HARQ. Then, taking into account the imperfections of channel estimation and error propagation in SIC, we propose SINR and SNR based corrections to correct the modulation and coding scheme selection. Finally, reinforcement learning (RL) is developed to optimize the SNR and SINR correction process. Comparing with a conventional fixed look-up table based scheme, the proposed solutions achieve superior performance in terms of spectral efficiency and packet error performance. Index Terms—Non-orthogonal multiple access (NOMA), adap-tive modulation and coding (AMC), hybrid automatic repeat request (HARQ), reinforcement learning (RL).

Wannian Du, Zheng Chu, Gaojie Chen, Pei Xiao, Zihuai Lin, Cheng Huang, Wanming Hao (2022)Hybrid Beamforming Design for ITS-assisted Wireless Networks, In: IEEE Wireless Communications Letterspp. 1-1 Institute of Electrical and Electronics Engineers (IEEE)

This letter proposes a hybrid beamforming design for an intelligent transmissive surface (ITS)-assisted transmitter wireless network. We aim to suppress the sidelobes and optimize the mainlobes of the transmit beams by minimizing the proposed cost function based on the least squares (LS) for the digital beamforming vector of the base station (BS) and the phase shifts of the ITS. To solve the minimization problem, we adopt an efficient algorithm based on the alternating optimization (AO) method to design the digital beamforming vector and the phase shifts of the ITS in an alternating manner. In particular, the alternating direction method of multipliers (ADMM) algorithm is utilized to obtain the optimal phase shifts of the ITS. Finally, we verify the improvement achieved by the proposed algorithm in terms of the beam response compared to the benchmark schemes by the simulation results.

Jing Zhu, Pengyu Gao, Gaojie Chen, Pei Xiao, Atta Ul Quddus (2022)Index Modulation for STAR-RIS Assisted NOMA System, In: IEEE Communications Letters Institute of Electrical and Electronics Engineers (IEEE)

In this letter, we first incorporate the concept of index modulation (IM) into simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided non-orthogonal multiple access (NOMA) system to improve the spectral efficiency. Specifically, the proposed IM aided STAR-RIS-NOMA system enables extra information bits to be transmitted by allocating subsurfaces to different users in a pre-defined subsurface allocation pattern. Furthermore, an approximate closed form expression on bit error rate (BER) is derived. Simulation results demonstrate that the proposed IM aided STAR-RIS-NOMA system is able to acquire transmission rate improvement compared to the conventional STAR-RIS NOMA.