This paper aims to provide a partial-DFT pilot sequence assisted joint channel estimation and user activity detection scheme for massive connectivity, in which a large number of devices with sporadic transmission communicate with a base station (BS) in the uplink. The joint channel estimation and device detection problem can be formulated as a compressed sensing single measurement vector (SMV) or multiple measurement vector (MMV) problem depending on whether the BS is equipped with single or large number of antennas. Due to high hardware cost and power consumption in massive multiple-input multiple-output (MIMO) systems, a mixed analog-to-digital converter (ADC) architecture is considered. In order to accommodate a large number of simultaneously transmitting devices, the joint channel estimation and active user detection are formulated as a MMV problem for the massive connectivity scenario; and the proposed GTurbo-MMV algorithm can precisely estimate the channel state information (CSI) and detect active devices with relatively low overhead. Furthermore, we study the state evolution (SE) for the MMV problem to obtain achievable bounds on channel estimation and device detection performance, in which both the missing and false detection probabilities can be made tend to zero in the massive MIMO regime. Simulation results confirm the theoretical accuracy of our analysis.

In this paper, a model-driven signal detection method with deep transfer learning (DTL) is proposed for downlink multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Specifically, we first introduce some learnable parameters to an unfolded iterative algorithm for MIMO detection and improve it through a preconditioned process to speed up its convergence. Then we combine this modified algorithm with the successive interference cancellation (SIC) structure in NOMA detection to propose our learned preconditioned conjugate gradient descent network with SIC (LPCG-SIC). The proposed network has fewer parameters and lower computational complexity in which only few trainable parameters need to be optimized, and there are only addition and multiplication operations in online detection. Furthermore, to overcome the drawback that most of the existing deep learning-based detectors only focus on signal detection in a given environment and cannot adapt to changes in feature spaces or distributions, we propose a DTL-based detection algorithm and three model-driven transfer strategies for our LPCG-SIC detector to improve the reusability of the trained network. Simulation results show that our proposed method can perform signal detection under multi-user interference and outperforms conventional detectors. The transfer strategies can obtain significant performance gain with lower training cost compared to no-transfer methods.

Channel estimation is significant for extra-large aperture array (ELAA) massive multiple-input multiple-output (MIMO) systems to fully fulfill their potential. However, hybrid-field propagation environment appears due to adopting ELAA, which thereby severely degrades the performance of existing channel estimation algorithms designed based on the channel sparsity property in traditional beam/angle domains. To tackle this problem, we propose a gridless hybrid-field channel estimation algorithm in this paper by excavating the hybrid-field channel sparsity in the fractional Fourier domain. The hybrid-field channel estimation problem is first formulated, and then the discrete fractional Fourier transform (DFrFT) is introduced to reveal the channel sparsity in the fractional Fourier domain. After that, the DFrFT-based Newtonized orthogonal matching pursuit algorithm is proposed without prior knowledge of the number of propagation paths. Numerical results show that the proposed algorithm greatly outperforms the existing algorithms.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems can significantly reduce the number of radio frequency (RF) chains by using lens antenna arrays, because it is usually the case that the number of RF chains is often much smaller than the number of antennas, so channel estimation becomes very challenging in practical wireless communication. In this paper, we investigated channel estimation for mmWave massive MIMO system with lens antenna array, in which we use a mixed (low/high) resolution analog-to-digital converter (ADC) architecture to trade-off the power consumption and performance of the system. Specifically, most antennas are equipped with low-resolution ADC and the rest of the antennas use high-resolution ADC. By utilizing the sparsity of the mmWave channel, the beamspace channel estimation can be expressed as a sparse signal recovery problem, and the channel can be recovered by the algorithm based on compressed sensing. We compare the traditional channel estimation scheme with the deep learning channel-estimation scheme, which has a better advantage, such as that the estimation scheme based on deep neural network is significantly better than the traditional channel-estimation algorithm.

For pretreating waste-activated sludge, the combined alkali and microwave pretreatment is a promising hybrid method. To make this pretreatment more economical, moderate conditions were applied for central composite design of the experiments. A quadratic model was established to describe the influence of NaOH dosage and microwave processing time on methane production. The optimal condition was 0.12 g NaOH/g TS treatment for 24 h and 240 W microwave treatment for 10 min. Either single or hybrid pretreatment can significantly accelerate the hydrolysis, but the degree of degradation increases positively as correlated with treatment intensity. Comparing to untreated sludge, the methane yields of microwave-, alkali-, and combined-treated increased to 1.9, 3.4, and 4.6 times, respectively. The SCOD removal rates of the three kinds of treated sludge were 20.6%, 23.0%, and 36.5%, respectively. The single microwave pretreatment efficiently broke the sludge flocs and promoted the release of biodegradable organics as well as nutrients, but there was no advantage in methane production. The existence of NaOH not only eliminated the inhibition caused by microwave, but also improved the degree of degradation.