This paper deals with the performance evaluation of different channel resource management techniques in LEO satellite systems based on an earth-fixed cell concept. Furthermore, in order to reduce the handover failure probability, we assumed that handover attempts can be queued. Both fixed and mobile users have been considered resulting in several classes of users. Each class requires a given Quality of Service (QoS) and thus a fixed part of the shared resource. Two channel allocation techniques are investigated: fixed channel allocation (FCA) and dynamic channel allocation (DCA). An analytical model is derived to analyze the performance of the FCA scheme supporting different kinds of users. A second analytical approch is proposed, in the FCA case, where a handover queuing strategy is taken into account. Implementation aspects for FCA and DCA strategies are discussed and compared in terms of blocking probabilities relative to each type of users.

Many research efforts are being focused upon the design of dynamic Inter-Cell Interference Coordination (ICIC) schemes for macrocell/picocell heterogeneous networks employing Cell Range Expansion (CRE). In order to protect the expanded Pico User Equipments (ePUEs) located in the CRE region from severe Macro Base Station (MBS) interference in downlink, the conventional methods reduce the transmit power of the MBS in the Almost Blank Subframes (ABSs), where ePUEs can be scheduled. However, this severely limits the amount of usable resources/power for the MBS as compared to Resource Block (RB)-based dynamic allocation. Instead, we propose a self-organized RB-based dynamic resource allocation method. Based on the proposed partial Channel State Information (CSI) sharing, the MBS obtains ePUEs' CSI and predicts their RB allocation. Then, the MBS reduces its transmit power in RBs where the ePUEs' allocation probability is estimated to be high. The simulation results show that the proposed scheme achieves excellent macrocell/picocell performance trade-offs, even when taking into account the overhead increase due to the partial CSI sharing.

By incorporating cloud computing capabilities to provide radio access functionalities, Cloud Radio Access Networks (CRANs) are considered to be a key enabling technology of future 5G and beyond communication systems. In CRANs, centralized radio resource allocation optimization is performed over a large number of small cells served by simple access points, the Remote Radio Heads (RRHs). However, the fronthaul links connecting each RRH to the cloud introduce delays and entail imperfect Channel State Information (CSI) knowledge at the cloud processors. In order to satisfy the stringent latency requirements envisioned for 5G applications, the concept of Fog Radio Access Networks (FogRANs) has recently emerged for providing cloud computing at the edge of the network. Although FogRAN may alleviate the latency and CSI quality issues of CRAN, its distributed nature degrades network interference mitigation and global system performance. Therefore, we investigate the design of tailored user pre-scheduling and beamforming for FogRANs. In particular, we propose a hybrid algorithm that exploits both the centralized feature of the cloud for globally-optimized pre-scheduling using imperfect global CSIs, and the distributed nature of FogRAN for accurate beamforming with high quality local CSIs. The centralized phase enables the interference patterns over the global network to be considered, while the distributed phase allows for latency reduction, in line with the requirements of FogRAN applications. Simulation results show that our proposed algorithm outperforms the baseline algorithm under imperfect CSIs, jointly in terms of throughput, energy efficiency, as well as delay.