The recently introduced 5G New Radio is the first wireless standard natively designed to support critical and massive machine type communications (MTC). However, it is already becoming evident that some of the more demanding requirements for MTC cannot be fully supported by 5G networks. Alongside, emerging use cases and applications towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general and MTC in particular. Next generation wireless networks, namely 6G, should therefore be an agile and efficient convergent network designed to meet the diverse and challenging requirements anticipated by 2030. This paper explores the main drivers and requirements of MTC towards 6G, and discusses a wide variety of enabling technologies. More specifically, we first explore the emerging key performance indicators for MTC in 6G. Thereafter, we present a vision for an MTC-optimized holistic end-to-end network architecture. Finally, key enablers towards (1) ultra-low power MTC, (2) massively scalable global connectivity, (3) critical and dependable MTC, and (4) security and privacy preserving schemes for MTC are detailed. Our main objective is to present a set of research directions considering different aspects for an MTC-optimized 6G network in the 2030-era.

In this paper, the average achievable rate and error probability of a reconfigurable intelligent surface (RIS) aided systems is investigated for the finite blocklength (FBL) regime. The performance loss due to the presence of phase errors arising from limited quantization levels as well as hardware impairments at the RIS elements is also discussed. First, the composite channel containing the direct path plus the product of reflected channels through the RIS is characterized. Then, the distribution of the received signal-to-noise ratio (SNR) is matched to a Gamma random variable whose parameters depend on the total number of RIS elements, phase errors and the channels’ path loss. Next, by considering the FBL regime, the achievable rate expression and error probability are identified and the corresponding average rate and average error probability are elaborated based on the proposed SNR distribution. Furthermore, the impact of the presence of phase error due to either limited quantization levels or hardware impairments on the average rate and error probability is discussed. The numerical results show that Monte Carlo simulations conform to matched Gamma distribution to received SNR for sufficiently large number of RIS elements. In addition, the system reliability indicated by the tightness of the SNR distribution increases when RIS is leveraged particularly when only the reflected channel exists. This highlights the advantages of RIS-aided communications for ultra-reliable and low-latency systems. The difference between Shannon capacity and achievable rate in FBL regime is also discussed. Additionally, the required number of RIS elements to achieve a desired error probability in the FBL regime will be significantly reduced when the phase shifts are performed without error.

Ultra-reliable low latency communication requires innovative resource management solutions that can guarantee high reliability at low latency. Grant-free random access, where channel resources are accessed without undergoing assignment through a handshake process, is proposed in 5G New Radio as an important latency reducing solution. However, this comes at an increased likelihood of collisions resulting from uncoordinated channel access. Novel reliability enhancement techniques are therefore needed. This article provides an overview of grant-free random access in 5G New Radio focusing on the ultra-reliable low latency communication service class, and presents two reliability-enhancing solutions. The first proposes retransmissions over shared resources, whereas the second proposal incorporates grant-free transmission with non-orthogonal multiple access where overlapping transmissions are resolved through the use of advanced receivers. Both proposed solutions result in significant performance gains, in terms of reliability as well as resource efficiency. For example, the proposed non-orthogonal multiple access scheme can support a normalized load of more than 1.5 users/slot at packet loss rates of ~ 10 ⁻⁵ a significant improvement over conventional grant-free schemes like slotted-ALOHA.

Uplink grant-free schemes have the promise of reducing the latency of an User Equipment (UE)-initiated transmission by avoiding the hand-shaking procedure for acquiring a dedicated scheduling grant. However, the possibility of successfully delivering a payload within a latency constraint may be severely compromised in case of grant-free operations over shared radio resources. In this paper, we study the performance of two different uplink grant-free schemes over shared resources recently discussed within the 5th Generation (5G) new radio (NR) standardization, namely a solution based on a Stop-and- Wait (SAW) protocol and a blind retransmission approach. Performance is evaluated assuming Rayleigh fading channels with a Maximum Ratio Combining (MRC) multi-antenna receiver. Analytical results show the benefits of grant-free transmission with respect to the traditional grant-based approach for a tight latency constraint. A high order receive diversity is beneficial to leverage the MRC gain and enables the possibility of achieving the 10–5 outage probability target set for Ultra-Reliable Low- Latency Communication (URLLC) services. The blind retransmission approach is significantly penalized by identification and signaling errors, while a SAW solution with potentially scheduled retransmissions out of the shared bandwidth leads to the lowest outage probability, at least for frequent packet arrivals.

Future industrial applications will encompass compelling new use cases requiring stringent performance guarantees over multiple key performance indicators (KPI) such as reliability, dependability, latency, time synchronization, security, etc. Achieving such stringent and diverse service requirements necessitates the design of a special-purpose Industrial Internet of Things (IIoT) network comprising a multitude of specialized functionalities and technological enablers. This article proposes an innovative architecture for such a special-purpose 6 G IIoT network incorporating seven functional building blocks categorized into: special-purpose functionalities and enabling technologies. The former consists of Wireless Environment Control, Traffic/Channel Prediction, Proactive Resource Management and End-to-End Optimization functions; whereas the latter includes Synchronization and Coordination, Machine Learning and Artificial Intelligence Algorithms, and Auxiliary Functions. The proposed architecture aims at providing a resource-efficient and holistic solution for the complex and dynamically challenging requirements imposed by future 6 G industrial use cases. Selected test scenarios are provided and assessed to illustrate cross-functional collaboration and demonstrate the applicability of the proposed architecture in a wireless IIoT network.

In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption. The solution accounts for the UAVs' battery lifetime and flight time to recharging depots to ensure the UAVs' green operation . The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).