In this paper, a new stochastic channel model (SCM) is proposed for fifth-generation (5G) systems. By means of the sum-of-sinusoids (SoS) method to generate spatially consistent random variables (SCRVs), the proposed model extends the 3rd Generation Partnership Project (3GPP)-SCM by considering three important features for accurate simulations in 5G, i.e., support for dual mobility, spatial correlation at both ends of the link and considerable reductions of the required memory consumption when compared with existing models. A typical problem presented in existing channel models, namely the generation of uncorrelated large scale parameters (LSPs) and small scale parameters (SSPs) for close base stations (BSs), is solved, then allowing for more realistic numerical evaluations in most of the 5G scenarios characterized by a large density of BSs and user equipments (UEs) per unit of area, such as ultra-dense networks (UDNs), indoor environments, device-to-device (D2D) and vehicular-to-vehicular (V2V). The proposed model emerges as the first SCM, and therein lower complexity when compared with ray-tracing (RT)based models, that comprises all the following features: support for single and dual mobility with spatial consistency, smooth time evolution, dynamic modeling, large antenna array, frequency range up 100 GHz and bandwidth up to 2 GHz. Some of the features are calibrated for single mobility in selected scenarios and have shown a good agreement with the calibration results found in the literature.

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Relaying and cooperative communications are key technologies present in 4G (4 th Generation) and probably in 5G (5 th Generation) networks. Efficient RRA (Radio Resource Allocation) applied in relay networks has the potential to fully exploit the performance gains in terms of spatial diversity, coverage and spectral/energy efficiency. Previous works in the literature have studied RRA for relay networks considering the unrealistic assumption of continuous mapping between SNR (Signal-to-Noise Ratio) and transmit data rate. In fact, the mapping between channel quality state and transmit data rates in real systems is discrete and depends on the employed MCSs (Modulation and Coding Schemes). In this work we reformulate the total data rate maximization problem with this new assumption, show an approach to obtain the optimal solution and propose lowcomplexity quasi-optimal solutions to the considered scenario.

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Besides the conventional spectral efficiency metric, energy efficiency (EE) has been adopted as one of the new mandatory performance metrics for green future mobile networks. In this sense, in this article we study energy-efficient radio resource allocation for dual-hop orthogonal frequency division multiple access relay networks considering multiple relays and users. Resource allocation in our scenario is comprised of relay selection, subcarrier pairing, subcarrier assignment, and transmit power allocation. Particularly, in the context of EE with quality of service constraints, three different problems, namely, power consumption minimization, global EE maximization, and minimum individual EE maximization are addressed. For tackling them, first, we demonstrate a property that exploits the use of the decode and forward protocol employed in the relays and we show how it can be applied to simplify the investigated problems. Although some of these problems are nonconvex due to the fractional objective functions and integer optimization domain, optimal solutions are provided using generalized fractional programming theory to transform the fractional objective functions to the subtractive forms. Then, by employing Dinkelbach or bisection methods, we propose iterative algorithms where a parametric problem is solved in each iteration until reaching convergence. By introducing a penalty function to handle integer variables and applying majorization-minimization approach, we make the parametric problem convex and we propose suboptimal iterative solutions with reduced computational cost. Regarding obtained simulation results, we discuss the performance of the involved solutions in terms of global EE and fairness with respect to different parameters of the system.