Grant-free non-orthogonal multiple access has been regarded as a viable approach to accommodate access for a massive number of machine-type devices with small data packets. The sporadic activation of the devices creates a multiuser setup where it is suitable to use compressed sensing in order to detect the active devices and decode their data. We consider asynchronous access of machine-type devices that send data packets of different frame sizes, leading todata length diversity. We address the composite problem of activity detection, channel estimation, and data recovery by posing it as a structured sparse recovery, having three-level sparsity caused by sporadic activity, symbol delay, and data length diversity. We approach the problem through approximate message passing with a backward propagation algorithm (AMP-BP), tailored to exploit the sparsity, and in particular the data length diversity. Moreover, we unfold the proposed AMP-BP into a network, termed learned AMP-BP (LAMP-BP), which enhances detection performance. The results show that the proposed LAMP-BP outperforms existing methods in activity detection and data recovery accuracy.

Wireless random access (RA) faces huge challenges under the explosive growth of the Internet of Things devices to be connected to the base station. This leads to inevitable RA collisions. In this paper, we consider the RA in massive multiple-input multiple-output (MIMO) systems, and propose an activity detection and channel estimation algorithm based on dictionary learning. By exploiting the sporadic feature of massive connected devices that a small fraction of them being active, we exploit compressive sensing for simultaneous channel estimation and activity detection. More importantly, the proposed algorithm utilizes dictionary learning to abstract the sparse characteristics of the spatial channel in the massive MIMO system. A dictionary is learned by using historical channel information of each cell, and thus is appropriate for the specific cell. Simulation results demonstrate the superiority of the proposed method compared with existing methods.

In massive machine-type communications (mMTC), the conflict between millions of potential access devices and limited channel freedom leads to a sharp decrease in spectrum efficiency. The nature of sporadic activity in mMTC provides a solution to enhance spectrum efficiency by employing compressive sensing (CS) to perform multiuser detection (MUD). However, CS-MUD suffers from high computation complexity and fails to meet the strict latency requirement in some critical applications. To address this problem, in this paper, we propose a novel deep learning (DL) based framework for grant-free non-orthogonal multiple access (GF-NOMA), where we utilize the information distilled from the initial data recovery phase to further enhance channel estimation, which in turn improves data recovery performance. Besides, we design an interpretable and structured Model-driven Prior Information Aided Network (M-PIAN) and provide theoretical analysis that demonstrates the proposed M-PIAN can converge faster and support more users. Experiments show that the proposed method outperforms existing CS algorithms and DL methods in both computation complexity and reconstruction accuracy.

In massive machine-type communications (mMTC), the conflict between millions of potential access devices and limited channel freedom leads to a sharp decrease in spectral efficiency. The sparse nature of mMTC provides a solution by using compressive sensing (CS) to perform multiuser detection (MUD) but suffers conflict between the high computation complexity and low latency requirements. In this paper, we propose a novel Dual-network for joint channel estimation and data recovery. The proposed Dual-Net utilizes the sparse consistency between the channel vector and data matrix of all users. Experimental results show that the proposed Dual-Net outperforms existing CS algorithms and general neural networks in computation complexity and accuracy, which means reduced access delay and more supported devices.

The B5G is expected to support multiple massive machine-type communication (mMTC) services. However, limited resources impede the access of a large number of machine-type devices, and existing access frameworks are not efficient for transmitting small data packets. In this paper, we propose a compressive sensing (CS) approach for contention based random access scheme to support massive connections (≥ 106devices) within a certain time-frequency resource. Different from the four-step contention based access scheme in LTE, we adopt a one-step access scheme to save the energy and spectrum resources. It breaks the bottleneck of existing CS multiuser detection methods which have poor scalability (e.g., assuming ≤ 104devices in relevant literatures) due to the high computational complexity of CS. The improved performance of the newly proposed method is observed in our experimental results.

Massive machine-type communication (MTC) with sporadically transmitted small packets and low data rate requires new designs on the PHY and MAC layer with light transmission overhead. Compressive sensing based multiuser detection (CS-MUD) is designed to detect active users through random access with low overhead by exploiting sparsity, i.e., the nature of sporadic transmissions in MTC. However, the high computational complexity of conventional sparse reconstruction algorithms prohibits the implementation of CS-MUD in real communication systems. To overcome this drawback, in this paper, we propose a fast Deep learning based approach for CS-MUD in massive MTC systems. In particular, a novel block restrictive activation nonlinear unit, is proposed to capture the block sparse structure in wide-band wireless communication systems (or multi-antenna systems). Our simulation results show that the proposed approach outperforms various existing algorithms for CS-MUD and allows for ten-fold decrease of the computing time.