This paper investigates the influence of employing diverse operating algorithms in Base Stations (BSs) to distribute network traffic. The study focuses on a wireless system where half of the BSs are powered by Renewable Energy Sources (RES) such as Photovoltaic Panels (PVPs) and Wind Turbines (WTs). The aim is to diminish the energy demand from conventional infrastructure reliant on Conventional Energy Sources (CES) like fossil fuels. The research evaluates the Energy Efficiency (EE) factors of the entire wireless system using two User Association (UA) approaches. The first approach, the RES-aware Association Algorithm (RAA), prioritizes connecting mobile users togreenBSs. The second approach, the SNR-based (SNR – Signal-to-Noise Ratio) Association Algorithm (SAA), assigns users to BSs based on radio signal distribution. Results indicate a reduction in conventional energy usage and renewable energy loss, ultimately enhancing the EE of the examined Radio Access Network (RAN).

Reconfigurable Intelligent Surfaces (RISs) are recognized as a promising solution for enhancing the energy efficiency (EE) of next-generation wireless networks, attributable to their low power consumption and signal enhancement capabilities. To address the EE maximization in RIS-aided multi-user wireless networks, we propose an innovative graph neural network (GNN)-based framework for the joint optimization of base station (BS) beamforming and RISs phase shifts. The framework models the network as a heterogeneous graph, enabling the GNN to capture complex interactions between RIS and user equipment (UE) nodes. We introduce two distinct feature initialization methods—Vertex-initialized GNN (VIGNN) and Edge-initialized GNN (EIGNN)—and develop specialized loss functions to guide the learning process, with theoretical analyses confirming their convergence. Extensive numerical simulations for both single-cell and cell-free scenarios validate the effectiveness of our approach, achieving up to a 5% improvement in EE over conventional methods and showcasing its scalability and adaptability in dynamic network environments.

In spite of promising outcomes in optimizing energy usage for Radio Access Network (RAN) Base Station (BS) hardware, deployment, and resource management, existing methods frequently lack flexibility for scenarios involving multiple frequencies and technologies of BSs. This investigation presents a comprehensive BS switching strategy based on a threshold, tailored for real-world multi-frequency and multi-technology BSs within the RAN. The proposed approach strategically deactivates BSs using a threshold parameter that determines the maximum allowable growth in transmission power for active BSs, ensuring both coverage for users affected by BS deactivation and energy saving . Simulations conducted on a realistic multi-technology 5G New Radio (NR) RAN in an urban environment validate the efficacy of the proposed strategy, achieving up to 73% of energy saving. The study assesses the influence of the frequency order of BS deactivation and examines user re-association strategies aimed at minimizing either path loss or transmission power.

In this work, the impact of using Renewable Energy Source (RES) generators in next-generation (5G) cellular systems on total power consumption (PC) has been investigated. The paper highlights the gain related to the use of photovoltaic (PV) panels and wind turbines (WTs) in the form of two factors - the average extension of battery lifetime (AEBL) powering a single network cell and the average reduction in energy consumption (AREC) within the whole network. The examination has been conducted for four different seasons of the year and various configurations of available power sources. Provided system scenario was based on real data on weather conditions, buildings placement, and implemented mobile networks for the city of Poznan in Poland. Used RES generators were designed in accordance with the specifications of real devices.

In the next generation of Radio Access Networks (RANs), Multi-access Edge Computing (MEC) is considered a promising solution to reduce the latency and the traffic load of backhaul links. It consists of the placement of servers, which provide computing platforms and storage, directly at each Base Station (BS) of these networks. In this paper, the caching feature of this paradigm is considered in a portion of a RAN, powered by a renewable energy generator system, energy batteries and the power grid. The performance of the caching in the RAN is analysed for different traffic characteristics, as well as for different capacity of the caches and different spread of it. Finally, we verify that the usage of a strategy that aims at reducing the energy consumption does not impact the benefits provided by the mobile edge caching.

In this paper, we investigate the coexistence of cognitive radio networks on TV white spaces for rural and suburban connectivity. Although experimental models and laboratory measurements defined the maximum interference threshold for TV white space technologies for general use cases, our research found that in real wireless rural and suburban scenarios, severe interference to the broadcasting services might occur. This is particularly relevant when the traffic load of the telecom base stations (BSs) exceeds 80% of their maximum capacity. We propose a dynamic management algorithm for minimizing the interference, based on a centralized access control architecture for cognitive radio wireless networks. In an experimental emulation for assessing the impact of cognitive radio interference on the broadcasting service’s QoE, our method reduced the perceived video distortion by the broadcasting users by at least 50% and 27.5% in a rural and suburban scenario, respectively, while the spectrum usage is increased by just 8%.

When a disaster occurs, the land-based cellular network could go offline for some days. Using an Unmanned Aerial Base Station (UABS) network is a promising solution to serve unconnected ground users. In this article, we propose a multifrequency backhaul architecture, which considers power and capacity constraints, to support the UABS network in a realistic 3D scenario in the city of Ghent, Belgium. Simulations results show that at the optimal flight height (80 m), up to 87% of the users could be supported using the multifrequency scenario compared with single frequency scenarios where coverage is about 70%.

This paper is the design of a Radio Environment Map (REM) with a real-time tool to sense the radiofrequency spectrum and optimally places with Surrogate Modelling and Sequential Experimental Design tools a total of 72 SDR sensors in the selected area, using LoRa and/or NB-IoT technologies for networking. It permits the regulatory body to check the correct use of the assigned spectrum and constitutes a communication alternative in case of a catastrophic event, such as a hurricane or an earthquake, where radio and TV broadcasting play an important role in keeping people informed after such meteorological event. The radiobroadcast services use large antennas and high towers, making them vulnerable to such events. Regardless of the chosen technology, the IoT monitoring network will be more robust, since it uses small antennas and lower towers, and often a given area is covered by multiple base stations. The tool can be used to deploy new services in the nonserved area (e.g., 4G in the 700 MHz band at a lower cost or using TVWS techniques to provide communications and internet connection) and optimal interference management.

In the next generation of wireless communication networks, Massive MIMO remains the appropriate technology to provide higher throughput to the users with incredible gains in terms of power consumption and energy efficiency. However, current research rarely considers the electromagnetic field exposure during the network design phase. In this paper, we propose a simulation-based method that enables an optimal design of the massive MIMO 5G networks with respect to both power consumption and electromagnetic field exposure. The results of the simulations show that the massive MIMO network achieves the same performance in terms of users coverage with 10 times less power consumption and an electric field strength 17 times weaker in comparison with the traditional 4G network.

Beamforming is one of the key features enabled in the fifth generation (5G) of wireless communications networks to accommodate the higher throughput demanded by the users for their data-intensive applications. This paper simulates energy-efficient 5G networks with beamforming capabilities deployed on a realistic area in Ghent, Belgium to respond to the instantaneous bit rate needed by the users. Various beamforming architectures have been investigated and the results are compared with the 4G reference network. When beamforming is enabled, the results of the simulations show that under the same coverage performance, 5G networks require 15% more base stations to provide more capacity to the users and are 3 times more energy-efficient than the 4G reference network. Moreover, the hybrid beamforming architecture provides good trade-off between the higher capacity and the low-power consumption requirements and needs to be considered when designing 5G cellular networks.

We investigate the feasibility of Internet of Things (IoT) technology to monitor and improve the energy efficiency and spectrum usage efficiency of broadcasting networks in the Ultra‐High Frequency (UHF) band. Traditional broadcasting networks are designed with a fixed radiated power to guarantee a certain service availability. However, excessive fading margins often lead to inefficient spectrum usage, higher interference, and power consumption. We present an IoT‐based management platform capable of dynamically adjusting the broadcasting network radiated power according to the current propagation conditions. We assess the performance and benchmark two IoT solutions (i.e., LoRa and NB‐IoT). By means of the IoT management platform the broadcasting network with adaptive radiated power reduces the power consumption by 15% to 16.3% and increases the spectrum usage efficiency by 32% to 35% (depending on the IoT platform). The IoT feedback loop power consumption represents less than 2% of the system power consumption. In addition, white space spectrum availability for secondary wireless telecommunications services is increased by 34% during 90% of the time.

Massive MIMO techniques are expected to deliver significant performance gains for the future wireless communication networks by improving the spectral and the energy efficiencies. In this paper, we propose a method to optimize the positions, the coverage, and the energy consumption of the massive MIMO base stations within a suburban area in Ghent, Belgium, while meeting the low power requirements. The results reveal that massive MIMO provides better performances for the crowded scenario where users’ mobility is limited. With 256 antennas, a massive MIMO base station can simultaneously multiplex 18 users at the same time‐frequency resource while consuming 8 times less power and providing 200 times more capacity than a 4G reference network for the same coverage. Moreover, a pilot reuse pattern of 3 is recommended in a multiuser multicell environment to obtain a good tradeoff between the high spectral efficiency and the low power requirement.

Using an Unmanned Aerial Base Station (UABS) i.e., a base station carried by a UAV (Unmanned Aerial Vehicle) or drone, is a promising approach to offer coverage and capacity to those users that are not being served by the base stations of the terrestrial network. In this paper, we propose an approach to the design of the drone's trajectory to account for the quickly varying user traffic and pattern. This approach is based on the identification of clusters made of nearby users to be served. The decision on which cluster to visit next by the UABS depends on a cost-function considering the distance to the next cluster, the user density and spread in the cluster, and the direction compared to the previously visited cluster. Furthermore, we propose a radio resource assignment algorithm to minimize the interference from the UABS to the terrestrial network when both are operating in the same frequency band. The potential improvements in terms of network capacity (sum throughput) and user satisfaction are estimated in this study.

In this study, the potential of applying different strategies to reduce the energy consumption of a wireless access network, powered by a photovoltaic panel system, during energy shortages is investigated. The goal is to reduce the amount of energy that should be bought from the traditional energy grid during a renewable energy shortage. Three different strategies are compared and the results are looking very promising. Depending on the strategy, up to 72% less energy should be bought compared to the fully operational network for a worst case scenario and a time period of 1 week. However, applying such a strategy has also its influence on the network performance. The influence on the user coverage is limited, with a reduction of 3% at maximum, but the capacity offered by the network decreases significantly with 51% up to even 71%.

In case of a large scale disaster, the wireless access network can become quickly saturated. This is of course undesirable because for this kind of situations we actually need a reliable wireless connectivity. In this study, the potential of mounting LTE femtocell base stations on drones to offer an alternative for the saturated existing wireless infrastructure is investigated. Our preliminary results show that this a very promising approach although a high amount of drones are needed to cover all users in the city center of Ghent, Belgium during a 1h intervention. The number of drones can be significantly reduced (up to 64%) by using a more advanced type of drone, by decreasing the user coverage requirement (11% less drones when requiring 80% instead of 90%) or by increasing the fly height of the drones (about 10% less drones needed when increasing the fly height by 10 m). This study shows that it is interesting to further investigate the use of drones to provide an emergency wireless access network.

The goal of this paper is to investigate the performance of a mobile WiMAX system for various settings of its physical-layer parameters and for realistic propagation channels. For this, a physical layer model of IEEE 802.16e is developed in software. Different propagation channels are implemented, such as the Rayleigh channel and the SUI channel models. Moreover, a tapped delay line channel model is developed for a specific area in Ghent (Belgium) using raytracing software. For this area, the maximum achievable range of a realistic mobile WiMAX system is found to be 2.7 km. Additionally, the performance gain of MIMO systems with diversity is investigated. The diversity gain of a 2x2 MIMO Alamouti diversity scheme is found to vary between 3 and 5.5 dB.