Massive Non-Orthogonal Multiple Access for Cellular IoT: Potentials and Limitations

The Internet of Things (IoT) promises ubiquitous connectivity of everything everywhere, which represents the biggest technology trend in the years to come. It is expected that by 2020 over 25 billion devices will be connected to cellular networks; far beyond the number of devices in current wireless networks. Machine-to-Machine (M2M) communications aims at providing the communication infrastructure for enabling IoT by facilitating the billions of multi-role devices to communicate with each other and with the underlying data transport infrastructure without, or with little, human intervention. Providing this infrastructure will require a dramatic shift from the current protocols mostly designed for human-to-human (H2H) applications. This article reviews recent 3GPP solutions for enabling massive cellular IoT and investigates the random access strategies for M2M communications, which shows that cellular networks must evolve to handle the new ways in which devices will connect and communicate with the system. A massive non-orthogonal multiple access (NOMA) technique is then presented as a promising solution to support a massive number of IoT devices in cellular networks, where we also identify its practical challenges and future research directions.Comment: To appear in IEEE Communications Magazin

Reliable machine-to-machine multicast services with multi-radio cooperative retransmissions

The final publication is available at Springer via http://dx.doi.org/10.1007/s11036-015-0575-6The 3GPP is working towards the definition of service requirements and technical solutions to provide support for energy-efficient Machine Type Communications (MTC) in the forthcoming generations of cellular networks. One of the envisioned solutions consists in applying group management policies to clusters of devices in order to reduce control signaling and improve upon energy efficiency, e.g., multicast Over-The-Air (OTA) firmware updates. In this paper, a Multi-Radio Cooperative Retransmission Scheme is proposed to efficiently carry out multicast transmissions in MTC networks, reducing both control signaling and improving energy-efficiency. The proposal can be executed in networks composed by devices equipped with multiple radio interfaces which enable them to connect to both a cellular access network, e.g., LTE, and a short-range MTC area network, e.g., Low-Power Wi-Fi or ZigBee, as foreseen by the MTC architecture defined by ETSI. The main idea is to carry out retransmissions over the M2M area network upon error in the main cellular link. This yields a reduction in both the traffic load over the cellular link and the energy consumption of the devices. Computer-based simulations with ns-3 have been conducted to analyze the performance of the proposed scheme in terms of energy consumption and assess its superior performance compared to non-cooperative retransmission schemes, thus validating its suitability for energy-constrained MTC applications.Peer ReviewedPostprint (author's final draft

Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resource allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, there exists an important challenge in "how to determine the configuration that maximizes the long-term average number of served IoT devices at each Transmission Time Interval (TTI) in an online fashion". Given the complexity of searching for optimal configuration, we first develop real-time configuration selection based on the tabular Q-learning (tabular-Q), the Linear Approximation based Q-learning (LA-Q), and the Deep Neural Network based Q-learning (DQN) in the single-parameter single-group scenario. Our results show that the proposed reinforcement learning based approaches considerably outperform the conventional heuristic approaches based on load estimation (LE-URC) in terms of the number of served IoT devices. This result also indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve almost the same performance with much less training time. We further advance LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario, thereby solve the problem that Q-learning agents do not converge in high-dimensional configurations. In this scenario, the superiority of the proposed Q-learning approaches over the conventional LE-URC approach significantly improves with the increase of configuration dimensions, and the CMA-DQN approach outperforms the other approaches in both throughput and training efficiency