Low-Complexity Receiver for Massive MIMO-GFDM Communications

Abstract

OFDM has two disadvantages. The first is high peak-to-average power ratio (PAPR), and the second is high out-of-band (OOB) radiated power. In the future communication applications, the diversified scenarios such as Internet of Things, inter-machine communication and telemedicine make the fourth-generation mobile communication no longer applicable. The generalized frequency division multiplexing (GFDM) has a pulse-shaping filter, which has less out-of-band radiated power and peak-to-average power ratio and fewer cyclic prefixes (CP) than OFDM. In order to meet high- data-transmission rate, it is an inevitable trend to install massive multi-input multi-output (massive MIMO) antennas. As the number of antennas increases, so does its complexity. This paper employs time reversal (TR) technology to reduce the computational complexity. Although the number of base station (BS) antennas has increased to eliminate interference, there is still residual interference. In order to eliminate the interference one step further, we deploy a zero forcing equalization (ZF equalization) after the time reversal combination.

Advanced lifting wavelet transform and V‐degree polynomial‐based channel estimation in MIMO‐GFDM

“…In the transmitter section, the proposed Ex-HWT-LWT-based GFDM scheme randomly produces the input serial bits and is matched using the CPM modulation technique. The spreading of a CPM-modulated signal is depicted mathematically in Equation (11). The inverse LWT is applied to the modulated spread data, and it can be mathematically formulated as follows:…”

“…GFDM is a multicarrier modulation technique based on blocks. Each block can easily change the number of subsymbols, subcarriers, and pulse-shaping filter types to satisfy various needs, including low out-of-band (OOB) radiation, high data speeds, and low latency [10][11][12]. By reducing its block size, GFDM can achieve the low latency required for the tactile Internet.…”

GFDM Transceiver Based on Ann Channel Estimation

“…Turhan et al, (2019), presented a deep neural network to enhance the performance of GFDM transceivers (Turhan 2019). Tsai et al, (2021) used the Time Reversal Zero Forcing algorithm to improve the performance and reduce the complexity of the GFDM transceiver (Tsai 2021). Ssimbwa et al, (2022), enhanced by reducing the BER of the GFDM system by using coded GFDM (Ssimbwa 2022).…”