Research

The stringent requirements on reliability and processing delay in the fifth-generation (5G) and beyond cellular networks introduce considerable challenges in the design of massive multiple-input-multiple-output (M-MIMO) receivers.  To improve the trade-off between reliability and complexity, a Bayesian concept has been considered a promising approach that enhances classical detectors, e.g. minimum-mean square-error detector. This work proposes a new Bayesian M-MIMO detector that can achieve a near ML performance with a ratio of transmit-to-receive antennas of up to 50%. The complexity increases linearly with the number of receive antennas by employing the matched filter-based parallel interference cancellation and the Bayesian concept.  

US Patent: US 2022/0393726 A1 

Presented in  2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea (South)

Published in IEEE Transaction on Communications (June 2021)

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) technology plays a prominent role in the beyond fifth-generation (5G) networks. However, designing a high-performance CF M-MIMO detector is a challenging task due to the presence of pilot contamination which appears when the number of pilot sequences is smaller than the number of users. This work proposes a CF M-MIMO detector referred to as CF expectation propagation (CF-EP) that incorporates the pilot contamination when calculating the posterior belief. The simulation results show that the proposed detector achieves significant improvements in terms of the bit-error-rate and sum spectral efficiency performances as compared to the ones of the state-of-the-art CF detectors.

Presented in 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), Norman, OK, USA

Multiuser massive multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. In an uplink MU-MIMO system, a base station is serving a large number of users, leading to a strong multi-user interference (MUI). Designing a high-performance detector in the presence of a strong MUI is a challenging problem. This work proposes a novel detector based on the concepts of expectation propagation (EP) and graph neural network, referred to as the GEPNet detector, addressing the limitation of the independent Gaussian approximation in EP. The simulation results show that the proposed GEPNet detector significantly outperforms the state-of-the-art MU-MIMO detectors in strong MUI scenarios with an equal number of transmitting and receive antennas.

Presented in 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, USA

Published in IEEE Journal on Selected Areas in Communications (JSAC) Machine Learning Series 

github link 

The orthogonal time frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications. The existing low complexity OTFS detectors’ performance is suboptimal in rich scattering environments where there are a large number of moving reflectors that reflect the transmitted signal towards the receiver. In this letter, we propose an OTFS detector, referred to as the BPICNet OTFS detector that integrates NN, Bayesian inference, and parallel interference cancellation concepts. Simulation results show that the proposed detector outperforms the state-of-the-art.