Recently, LoRaWAN has emerged as a promising technology for the Internet of things (IoT), owing its ability to support low-power and long-range communications. However, real-world deployment and network optimization require accurate path-loss (PL) modeling, so as to estimate network coverage, performance, and profitability. For that reason, in this work, LoRaWAN radio channel is investigated in the 868 MHz band. Extensive measurement campaigns were carried out in both indoor and outdoor environments at urban and rural locations in Lebanon (Saint Joseph University of Beirut campus, Beirut city, and Bekaa valley). Based on empirical results, PL models are developed for LoRaWAN communications and compared with widely used empirical models.Moreover, the performance and the coverage of LoRaWAN deployment are evaluated based on real measurements. The results show that the proposed PL models are accurate and simple to be applied in Lebanon and other similar locations. Coverage ranges up to 8 km and 45 km were obtained in urban and rural areas, respectively. This reveals the reliability of this promising technology for long-range IoT communications.

When several radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users. Mobile users aim to selfishly maximize their utility. However, as they do not cooperate, their decisions may lead to performance inefficiency. In this paper, to overcome this limitation, we propose a network-assisted approach. The network provides information for the mobiles to make more accurate decisions. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. Deriving network information is formulated as a semi-Markov decision process (SMDP), and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies, such as blocking probability and average throughput, are analyzed. When tuning thresholds are pertinently set, our heuristic achieves performance very close to the optimal solution. Moreover, although it provides lower performance, our learning-based algorithm has the crucial advantage of requiring no prior parameterization.

Dense deployment of small cells operating on different frequency bands based on multiple technologies provides a fundamental way to face the imminent thousand-fold traffic augmentation. This heterogeneous network (HetNet) architecture enables efficient traffic offloading among different tiers and technologies. However, research on multi-tier HetNets where various tiers share the same microwave spectrum has been well-addressed over the past years. Therefore, our work is targeted towards novel multi-tier HetNets with disparate spectrum (microwave and millimeter wave). In fact, despite the huge capacity brought by millimeter-wave technology, the latter will fail to provide universal coverage, especially indoor, and so mmWave will inevitably co-exist with a traditional sub-6GHz cellular network. In this work, we propose a coordinated user association and spectrum allocation by resorting to non-cooperative game theory. In fact, in such an arduous context, efficient distributed solutions are imperative. Extensive simulation results show the precedence of our coordinated approach in comparison with state-of-the-art heuristics. Moreover, we evaluate the impact of various network parameters, such as mmWave density, cell load, and user distribution and density, offering valuable guidelines into practical 5G HetNet design. Finally, we assess the benefit brought by massive MIMO for mmWave in such a highly heterogeneous setting.

Cloud Radio Access Network (C-RAN) offers an evolution in base stations architecture. The base station is broken down into a Base Band Unit (BBU) and a Remote Radio Head (RRH). While BBUs are pooled in a single geographical point, RRHs are distributed across multiple sites. In conventional architectures, a one-to-one logical mapping exists between BBUs and RRHs. One BBU is assigned to one RRH, so as to maximize network capacity. However, in the C-RAN architecture, a one-to-many logical mapping may be established. One BBU is assigned to many RRHs, so as to reduce network power consumption. In this paper, we investigate the BBU-RRH mapping, also known as the RRH clustering problem, and formulate it as a bin packing problem. Optimal and heuristic solutions are derived to reduce network power consumption, without compromising user quality of service. Simulation results illustrate the benefits from clustering RRHs, and prove that our heuristic achieves close performance to the optimal solution.

LoRaWAN has imposed itself as a promising and suitable technology for massive machine-type communications. With the acceleration of deployment, improving the energy efficiency of LoRaWAN networks has become paramount, especially with the limitations of throughput and battery resources. However, LoRaWAN suffers from the Aloha access scheme, which leads to a high probability of collision at large scales, especially in dense environments such as cities. In this paper, we propose EE-LoRa, an algorithm to improve the energy efficiency of LoRaWAN networks with multiple gateways via spreading factor selection and power control. We proceed in two steps, where we first optimize the energy efficiency of the network, defined as the ratio between the throughput and consumed energy. Solving this problem involves determining the optimal node distribution among different spreading factors. Then, in the second step, power control is applied to minimize the transmission power at nodes without jeopardizing the reliability of communications. The simulation results show that our proposed algorithm greatly improves the energy efficiency of LoRaWAN networks compared to legacy LoRaWAN and relevant state-of-the-art algorithms.