Massive Machine-Type Communication (mMTC) is a critical Internet of Things (IoT) scenario in 5G and beyond 5G (B5G) wireless networks, characterized by a vast number of devices, smaller data packets, sporadic transmission, and diverse latency requirements. Massive multiple-input-multiple-output (MIMO) technology allows multiple user equipments (UEs) to transmit their data simultaneously over the same resource block, making it a promising technology to support mMTC. However, when massive UEs attempt to access the massive MIMO network simultaneously, the network will experience severe overload. To address this challenge, we propose a Deep Deterministic Policy Gradient (DDPG)-based delay-aware Access Class Barring (ACB) dynamic access control scheme for mMTC in massive MIMO networks. In this scheme, we model the access blocking probability as a function of latency sensitivity for each active UE with a shared parameter, ensuring that the closer the current delay is to a UE’s delay budget, the higher the access priority of that UE. We then propose a DDPG-based algorithm to optimize the access blocking probability and the access blocking time in ACB. Simulation studies demonstrate that, compared with the baseline methods, the proposed scheme significantly increases the number of successful access UEs while maintaining access delays within the budget constraints.

Unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) satellite Internet of Things (IoT) networks face severe physical-layer security challenges in the presence of eavesdroppers. To address this issue, this paper proposes a secure graph neural network (Sec-GNN) based multi-dimensional anti-eavesdropping optimization method. The approach models the node-spatial relationships among the UAV, legitimate users, and eavesdroppers as a graph structure. By leveraging the message-passing mechanism of graph neural networks—sequently performing message generation, message aggregation, and node update—it dynamically integrates network topology information and node interaction features. This enables end-to-end joint optimization of the UAV’s three-dimensional position, beamforming vectors, and multi-user power allocation strategies. The method does not rely on explicit channel state information and directly generates near-optimal resource allocation schemes based on node location information and observable signal features. Experimental results demonstrate the superiority of Sec-GNN across various scenarios, and ablation studies confirm that partial optimization leads to significant performance degradation, thereby verifying the necessity of multi-dimensional joint design. The proposed framework provides an efficient and scalable solution for secure resource management in dynamic space-air-ground integrated networks.

Intelligent Reflecting Surfaces (IRS) enhance spectral efficiency by adjusting reflection phase shifts, while Non-Orthogonal Multiple Access (NOMA) increases system capacity. Consequently, IRS-assisted NOMA communications have garnered significant research interest. However, the passive nature of the IRS, lacking authentication and security protocols, makes these systems vulnerable to external eavesdropping due to the openness of electromagnetic signal propagation and reflection. NOMA’s inherent multi-user signal superposition also introduces internal eavesdropping risks during user pairing. This paper investigates secure transmissions in IRS-assisted NOMA systems with heterogeneous resource configuration in wireless networks to mitigate both external and internal eavesdropping. To maximize the sum secrecy rate of legitimate users, we propose a combinatorial optimization graph neural network (CO-GNN) approach to jointly optimize beamforming at the base station, power allocation of NOMA users, and phase shifts of IRS for dynamic heterogeneous resource allocation, thereby enabling the design of dual-link or multi-link secure transmissions in the presence of eavesdroppers on the same or heterogeneous links. The CO-GNN algorithm simplifies the complex mathematical problem-solving process, eliminates the need for channel estimation, and enhances scalability. Simulation results demonstrate that the proposed algorithm significantly enhances the secure transmission performance of the system.

A novel grant-free random access scheme with a high success rate is proposed to support massive access for machine-to-machine communications in massive multiple-input–multiple-output (MIMO) systems. This scheme allows active user equipments (UEs) to transmit their modulated uplink messages and super pilots consisting of multiple subpilots to a base station (BS). Then, the BS performs channel state information (CSI) estimation and uplink message decoding by utilizing a proposed graph combined clustering independent component analysis (GCICA) decoding algorithm and then employs the estimated CSIs to detect active UEs by using the characteristic of asymptotic favorable propagation of massive MIMO channel. We call this proposed scheme as the GCICA-based random access (GCICA-RA) scheme. We analyze the successful access probability, missed detection probability, and uplink throughput of the GCICA-RA scheme. Numerical results show that the GCICA-RA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability, and provides low CSI estimation error at the same time.

Abstract A new random access scheme is proposed to solve the intra‐cell pilot collision for M2M communication in crowded asynchronous massive multiple‐input multiple‐output systems. The proposed scheme utilizes the proposed estimation method of signal parameters to estimate the effective timing offsets, and then active user equipments obtain their timing errors from the effective timing offsets for uplink message transmission. The mean squared error of the estimated effective timing offsets of user equipments and the uplink throughput are analysed. Simulation results show that, compared to the exiting random access scheme for the crowded asynchronous massive multiple‐input multiple‐output systems, the proposed scheme can improve the uplink throughput and estimate the effective timing offsets accurately at the same time.

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless communication systems, enhances the spectral and energy efficiency by intelligently adjusting the propagation conditions between base stations (BSs) and mobile equipments (MEs). An RIS consists of many low-cost passive reflecting elements that are optimized to improve the quality of the received signal. In this paper, we study the problem of power control at the BS and RIS optimization for application to physical-layer broadcasting. Our goal is to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BS and the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposed optimization methods, we derive lower bounds to quantify the average transmit power at the BS as a function of the number of MEs, the number of RIS elements, and the number of antennas at the BS. The simulation results demonstrate that the average transmit power at the BS is close to the lower bound in an RIS-aided system, and is significantly lower than the average transmit power in conventional schemes without an RIS.

A novel grant-free random access scheme is proposed to support massive connectivity with low access delay and overhead for machine-to-machine communication in massive multiple-input-multiple-output systems. This scheme allows all active user equipments (UEs) to transmit their pilots and uplink messages via the same time-frequency resource and performs the joint active UEs detection and uplink message decoding without channel estimation in one shot by utilizing the proposed ensemble independent component analysis (EICA) decoding algorithm. We call the proposed scheme the EICA-based pilot random access (EICA-PA). We analyze the successful access probability, probability of missed detection, and uplink throughput of the EICA-PA scheme. Numerical results show that the EICA-PA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability and provides low-frame error rate at the same time.

A carrier synchronisation technique for multiple symbol Trellis‐coded continuous phase frequency shift keying (MSTC‐CPFSK) system in burst‐mode transmission is proposed. For the synchronisation technique, a joint data‐aided (DA) acquisition and phase‐locked loop (PLL) tracking algorithm is presented. First, an all‐zero sequence is utilised as a pilot for DA acquisition, where the modulated pilot waveform is a direct current (DC) signal, eliminating the operation of modulation removal; and then, a second‐order PLL structure is introduced for tracking processing, and we make an analysis of the tracking performance by calculating the equivalent Cramer–Rao bound (ECRB) for the second‐order PLL structure; subsequently, we extend the obtained result to any order PLL structures; moreover, the ECRB for the MSTC‐CPFSK system is also derived through arithmetical calculation; finally, some numerical results are given to verify the performance of the authors presented synchronisation algorithm, and the simulations illustrate that their algorithm can improve the throughput compared with the classical DA‐only algorithm.

Performance analysis is presented for the joint estimation of symbol timing, frequency offset, and phase offset with continuous‐phase modulation. In this study, undesirable influences on parameters to be estimated, which arise from the inaccuracy of the already estimated parameters, are computed mathematically. Based on performance analysis, a conditional Cramer‐Rao bound (CRB) is put forward, and a hypothesise is made that phase offset variance with frequency error present should match with the conditional CRB for any digital phase modulation. Moreover, the hypothesis is manifested by analysing the log‐likelihood function in a more general mathematical manner. To alleviate the influences, a data‐aided acquisition and phase‐locked loop tracking algorithm is proposed. The results of Monte Carlo simulations are presented, showing good agreement with theoretical analysis. Simulation results also show that the proposed algorithm can improve performance in terms of phase error variance at the expense of a high signal‐to‐noise ratio threshold.