Small data transmission (SDT) is a novel technique that allows IoT devices to transmit their payload data without a pre-allocated radio resource. However, unresolvable SDT collisions occur due to the unpredictability of IoT traffic. In this paper, we propose a semi-contention-free transmission scheme that combines dedicated time-slot allocation through hashing and random access with access control through a dueling deep Q-network. Focusing on uplink IoT traffic for SDT in a NOMA-enabled network with two clusters of devices, we implement the proposed scheme and demonstrate its effectiveness and performance with respect to throughput, latency, and energy consumption through extensive simulations.

The unprecedented surge of massive Internet of things (mIoT) traffic in beyond fifth generation (B5G) communication systems calls for transformative approaches for multiple access and data transmission. While classical model-based tools have been proven to be powerful and precise, an imminent trend for resource management in B5G networks is promoting solutions towards data-driven design. Considering an IoT network with devices spread in clusters covered by a base station, we present in this paper a novel model-free multiple access and data transmission framework empowered by reinforcement learning, designed for power-domain non-orthogonal multiple access networks to facilitate uplink traffic of small data packets. The framework supports two access modes referred to as contention-based and semi-contention-free, with its core component being a policy gradient algorithm executed at the base station. The base station performs access control and optimal radio resource allocation by periodically broadcasting two control parameters to each cluster of devices that considerably reduce data detection failures with a minimum computation requirement on devices. Numerical results, in terms of system and cluster throughput, throughput fairness, access delay, and energy consumption, demonstrate the efficiency and scalability of the framework as network size and traffic load vary.

To obtain benefits for non-orthogonal multiple access (NOMA) based concurrent transmissions for uplink Internet of things (IoT) traffic in multi-antenna enabled networks, device clustering has been a challenging task. Most previous studies focused on grouping devices into clusters that are served by different beams in multiple input multiple output (MIMO)-NOMA networks. In this paper, we perform an exploratory study on the impact of both intra- and inter-cluster interference on the performance of uplink concurrent transmissions considering that multiple clusters are served by a single beam. For performance assessment, we define two metrics, cluster throughput and transmission latency, and evaluate network performance with various network configurations. The study provides insight on how to configure device clusters and perform access control in order to maximize performance and improve fairness. As devices closer to the base station experience less path attenuation, we introduce distinct access control mechanisms to improve transmissions fairness for concurrent transmissions from difference clusters.

To address the challenges faced by non-orthogonal multiple access (NOMA) for multiple access in beyond fifth gen-eration (B5G) networks, rate-splitting multiple access (RSMA) has newly emerged as a promising approach. In addition to achieving high data rates, RSMA may be used for massive machine-type communication (mMTC)/massive Internet of things (mIoT) applications as well. Although RSMA addresses the user coupling and scheduling overhead problems met by NOMA, it is not clear whether and how RSMA can be applied to uplink mMTC/mIoT traffic. In this study, we explore the applicability of RSMA for uplink traffic and investigate whether there is any benefit to employ RSMA and if yes how this benefit can be achieved. To this end, we propose a novel uplink RSMA scheme with two constituent components, known as inter-device decoding and intra -device decoding, respectively. Through user pairing for inter-device decoding and power allocation for intra-device decoding, we reveal under which circumstances RSMA can bring benefits for uplink traffic.

A receive diversity powerline communication system subject to uncorrelated Rayleigh distributed channel gains and corrupted by background noise modeled by Nakagami-m distribution is considered in this paper. For the transmitter utilizing multi-level amplitude-shift keying (ASK) modulation for data transmission and the receiver employing the optimal noncoherent decision rule for symbol estimation, the closed-form expression for the symbol error probability (SEP) of the system with an even number of diversity branches is derived using the union bound approach. Further, the constrained optimization problem for obtaining the optimal ASK signaling levels minimizing the SEP of the system with constrained total energy at the transmitter is formulated and solved numerically. It is observed that the optimal ASK constellation thus obtained outperforms the traditional equally-spaced ASK constellation for higher values of signal-to-noise ratio and diversity branches and at lower values of the shape parameter of the noise.