Ιn this review article, a comprehensive study is provided regarding the latest achievements in simulation techniques and platforms for fifth generation (5G) wireless cellular networks. In this context, the calculation of a set of diverse performance metrics, such as achievable throughput in uplink and downlink, the mean Bit Error Rate, the number of active users, outage probability, the handover rate, delay, latency, etc., can be a computationally demanding task due to the various parameters that should be incorporated in system and link level simulations. For example, potential solutions for 5G interfaces include, among others, millimeter Wave (mmWave) transmission, massive multiple input multiple output (MIMO) architectures and non-orthogonal multiple access (NOMA). Therefore, a more accurate and realistic representation of channel coefficients and overall interference is required compared to other cellular interfaces. In addition, the increased number of highly directional beams will unavoidably lead to increased signaling burden and handovers. Moreover, until a full transition to 5G networks takes place, coexistence with currently deployed fourth generation (4G) networks will be a challenging issue for radio network planning. Finally, the potential exploitation of 5G infrastructures in future electrical smart grids in order to support high bandwidth and zero latency applications (e.g., semi or full autonomous driving) dictates the need for the development of simulation environments able to incorporate the various and diverse aspects of 5G wireless cellular networks.

The 5G radio networks have introduced major changes in terms of service requirements and bandwidth allocation compared to cellular networks to date and hence, they have made the fundamental radio planning problem even more complex. In this work, the focus is on providing a generic analysis for this problem with the help of a proper multi-objective optimization algorithm that considers the main constraints of coverage, capacity and cost for high-capacity scenarios that range from dense to ultra-dense mmWave 5G standalone small-cell network deployments. The results produced based on the above analysis demonstrate that the denser the small-cell deployment, the higher the area throughput, and that a sectored microcell configuration can double the throughput for ultra-dense networks compared to dense networks. Furthermore, dense 5G networks can actually have cell radii below 400 m and down to 120 m for the ultra-dense sectored network that also reached spectral efficiency 9.5 bps/Hz/Km² with no MIMO or beamforming.

Growing and diverse user needs, along with the need for continuous access with minimal delay in densely populated machine-type networks, have led to a significant overhaul of modern mobile communication systems. Within this realm, the integration of advanced physical layer techniques such as relaying-assisted transmission in beyond fifth-generation (B5G) networks aims to not only enhance network performance but also extend coverage across multicellular orientations. However, in cellular environments, the increased interference levels and the complex channel representations introduce a notable rise in the computational complexity associated with radio resource management (RRM) tasks. Machine and deep learning (ML/DL) have been proposed as an efficient way to support the enhanced user demands in densely populated environments since ML/DL models can relax the traffic load that is associated with RRM tasks. There is, however, in these solutions the need for distributed execution of training tasks to accelerate the decision-making process in RRM tasks. For this purpose, federated learning (FL) schemes are considered a promising field of research for next-generation (NG) networks’ RRM. This paper proposes an FL approach to tackle the joint relay node (RN) selection and resource allocation problem subject to power management constraints when in B5G networks. The optimization objective of this approach is to jointly elevate energy (EE) and spectral efficiency (SE) levels. The performance of the proposed approach is evaluated for various relaying-assisted transmission topologies and through comparison with other state-of-the-art ones (both ML and non-ML). In particular, the total system energy efficiency (EE) and spectral efficiency (SE) can be improved by up to approximately 10–20% compared to a state-of-the-art centralized ML scheme. Moreover, achieved accuracy can be improved by up to 10% compared to state-of-the-art non-ML solutions, while training time is reduced by approximately 50%.

The issue of user privacy is constantly brought to the spotlight since an ever increasing number of online services collects and processes personal information from users, in the context of personalized service provision. Although technology makes the collection of data easy, their protection against abuse is left to data protection legislation. However, the privacy requirements, other than being general and abstract terms to be regarded as legislature issues, should be brought down in the technological reality and carefully accounted for in devising technical solutions. In order to limit the disclosure and avoid the misuse of personal data, this paper discusses an architectural proposal for a middleware system that will enforce protection of user privacy through technical means. This goal is facilitated by a combination of a policy framework, a sensible interpretation of regulations into policies and the introduction of a privacy broker, named the discreet box.

Terrestrial digital video broadcasting (DVB-T) network planning is the main interest of this article, along with a case study for Greece. The basic principles and the guidelines of the DVB-T planning process are presented, in conjunction with their application to the establishment of the Greek DVB-T allotment plan. The procedures described in this article were followed by the authors during the International Telecommunication Union (ITU) DVB-T planning project that concluded with the ITU's Regional Radiocommunication Conference 2006 (RRC-06), and the Geneva (GE-06) frequency plan for terrestrial digital radio broadcasting.