Channel state information (CSI) is important to reap the full benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The traditional channel estimation methods using pilot frames (PF) lead to excessive overhead. To reduce the demand for PF, data frames (DF) can be adopted for joint channel estimation and data recovery. However, the computational complexity of the DF-based methods is prohibitively high. To reduce the computational complexity, we propose a joint channel estimation and data recovery (JCD) method assisted by a small number of PF for mmWave massive MIMO systems. The proposed method has two stages. In Stage 1, differing from the traditional PF-based methods used for precise estimation of channel parameters, the proposed PF-assisted method is utilized to narrow down the search range for the angle of arrival (AoA) of principal components (PC) of channels. In Stage 2, JCD is designed for parallel implementation based on the multi-user decoupling strategy. The theoretical analysis demonstrates that the PF-assisted JCD method can achieve equivalent performance to the Bayesian-optimal DF-based method, while greatly reducing the computational complexity. Simulation results are also presented to validate the analytical results.

Channel state information (CSI) is important to reap the full benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The traditional channel estimation methods using pilot frames (PF) lead to excessive overhead. To reduce the demand for PF, data frames (DF) can be adopted for joint channel estimation and data recovery. However, the computational complexity of the DF-based methods is prohibitively high. To reduce the computational complexity, we propose a joint channel estimation and data recovery (JCD) method assisted by a small number of PF for mmWave massive MIMO systems. The proposed method has two stages. In Stage 1, differing from the traditional PF-based methods, the proposed PF-assisted method is utilized to capture the angle of arrival (AoA) of principal components (PC) of channels. In Stage 2, JCD is designed for parallel implementation based on the multi-user decoupling strategy. The simulation results show that the PF-assisted JCD method can achieve near the same performance as the Bayesian-optimal DF-based method, while greatly reducing the computational complexity.

Pulse-based integrated sensing and communication (ISAC) systems have the advantages of high ranging resolution and strong resistance to self-interference, compared with continuous wave (CW) based systems. However, for pulse-based ISAC systems, multi-path channels pose various challenges to data recovery and ranging by providing diversity gain for data recovery but incurring the interference to the identification of the first path in ranging. In this paper, we design a pulse-based ISAC receiver for multi-path channels. The designed receiver can obtain the diversity gain by correlating the received signal with the estimated template signal. Meanwhile, it can detect the arrival of the first path by using a threshold detection method based on a constant false alarm rate (CFAR). Furthermore, we extend the pulse-based ISAC design to a low-resolution analog-to-digital converter (ADC) scenario. A low-cost receiver design is provided for the pulse-based ISAC system that can recover data and estimate range simultaneously considering the non-linear effect caused by the low-resolution ADC. Simulation results show that compared with the generalized maximum likelihood (GML) based receiver, the proposed full-resolution pulse-based ISAC receiver has 1dB signal-to-noise ratio (SNR) loss in bit error rate (BER) and almost the same mean squared error (MSE) performance with the significantly reduced computational complexity. Also, compared with the full-resolution ISAC receiver, the ISAC receiver with 3-level quantization incurs only 0.8dB SNR loss in BER and 1dB SNR loss in MSE.