Faced with the limitations of the Aloha random access scheme and spread spectrum techniques, LoRaWAN is yet to realize its potential as the flagship technology for large-scale Internet of Things applications. LoRaWAN allows for low power and long range communications. Nonetheless, concurrent transmissions on the same spreading factors, increased with the inevitable densification of device deployment, will lead to collisions and degradation in performance. The problem is further amplified due to the shortage in radio resources, with multiple operators utilizing the same unlicensed frequency bands. In this paper, we investigate different inter-operator cooperation schemes and devise multiple algorithms for spreading factor assignment in a multi-operator LoRaWAN deployment scenario. We start by proposing a proportional fair optimal formulation for the assignment with the objective of maximizing the logarithmic sum of the normalized throughput per spreading factor. Under the assumption of partial operator cooperation, we propose a gradient ascent based iterative algorithm for solving the spreading factor assignment problem, and a game theory based approach, wherein each network operator seeks to maximize its own normalized throughput. Finally, and with cooperation between different operators bound to be limited, we use recurrent neural networks to enable the prediction of the success rate per spreading factor. This prediction allows the different operators to assign spreading factors with minimum cooperation. We simulate our proposals and compare them to the legacy LoRaWAN approach as well as others in the state-of-the-art, highlighting the gains they produce in terms of total normalized throughput and packet delivery ratios.

This paper explores the use of multi-agent deep learning as well as learning to cooperate principles to meet strict service level agreements, in terms of throughput and end-to-end delay, for a set of classified network flows. We consider agents built on top of a weighted fair queuing algorithm that continuously set weights for three flow groups: gold, silver, and bronze. We rely on a novel graph-convolution based, multi-agent reinforcement learning approach known as DGN. As benchmarks, we propose centralized and distributed deep Q-network algorithms and evaluate their performances in different network, traffic, and routing scenarios, highlighting both the effectiveness of our proposals and the importance of agent cooperation. We show that our DGN-based approach meets stringent throughput and delay requirements across different scenarios, decreasing silver and bronze flow median waiting delays by more than 50 % and reducing the SLA violations of the latter by nearly 60 %, with respect to a classic priority queuing approach.

In this paper, we aim to tackle the challenges of resource block scheduling and power allocation in the context of multi-cell full-duplex wireless networks. This is a more realistic setting than the single cell scenario, and it better envisions how full-duplex wireless communications could eventually be implemented. We propose an optimal queue-aware joint scheduling and power allocation algorithm for full-duplex wireless networks in a multi-cell scenario. Because of its mathematical intractability, we decouple the problem and solve it for scheduling first, and for power allocation second. We consider both indoor and outdoor scenarios and show that the gains of multi-cell full-duplex wireless networks, with respect to their half-duplex counter-parts, are not always prevalent. Furthermore, we highlight the importance of inter-cell cooperation when it comes to scheduling resources and show that depending on the scenario at hand, interference mitigation from inter-cell cooperation can improve the performance of user equipment in terms of throughput and waiting delay. Finally, we show that power allocation can improve user equipment throughput with its efficiency being tied to the deployment scenario at hand.

LoRaWAN is a popular internet of things (IoT) solution over the unlicensed radio band. It sustains low-cost, durable, and long range IoT wireless communications. Nonetheless, with over 24 billion connected IoT devices being expected by the end of the year, and over 50 billion by 2025, the concurrent and legacy approaches to spreading factor and channel assignment in LoRaWAN networks can no longer keep up. This is exacerbated with the growing densification of IoT device deployments and, with the increasing requirements for better throughput and packet delivery ratios. In this paper, we propose a proportional fair-based joint optimal formulation for spreading factor and channel assignment in multi-operator LoRaWAN deployments. The objective of this problem is to maximize the total sum of the logarithmic normalized throughput. We split the problem into two subproblems, and propose a game theoretic approach to solving them. We prove that our games converge towards a pure Nash equilibrium and, afterwards, solve the optimization problems using both semi-distributed and completely distributed algorithms. Via simulations, we show that our algorithms greatly improve the total normalized throughput for LoRaWAN as well as the packet success rate, in comparison to the legacy approaches.

In Half-Duplex (HD) systems, at a given time instant, a radio resource is exclusively assigned to one User Equipment (UE) either for transmission or for reception. Full-Duplex (FD) networks promise to increase the system's capacity by allocating resources simultaneously to two UEs: one uplink UE and one downlink UE. In order to enhance the network's spectral efficiency, the system needs to deal with two major types of interference: self-interference and co-channel interference. This is one of the main challenges of scheduling in FD systems. In this article, we propose two algorithms for scheduling in FD Orthogonal Frequency Division Multiple Access Systems (FD-OFDMA). First, we propose an FD Maximum Signal-to-Interference-plus-Noise ratio (FD Max-SINR) algorithm, which allocates resources in FD depending on the SINR values of the UEs. Second, we propose a Hybrid Max-SINR scheduling algorithm. This hybrid algorithm chooses astutely between allocating the resources either in HD or FD, in a manner that maximizes the SINR. We evaluate these algorithms and compare them to HD Max-SINR in terms of UE throughput and average waiting delay. According to the simulation results, FD Max-SINR provides, in comparison with its HD counterpart, increased throughput for uplink UEs. It also almost doubles the throughput for the downlink ones. FD Max-SINR reduces the waiting delay that UEs undergo. Furthermore, for relatively low values of Self-Interference Cancellation (SIC), our Hybrid Max-SINR algorithm can still provide higher network throughput compared to HD Max-SINR.