Macrocells are expected to be densely overlaid by small cells (SCs) to meet increasing capacity demands. Due to their dense deployment, some SCs will not be connected directly to the core network, and thus, they may forward their traffic to the neighboring SCs until they reach it, thereby forming a multihop backhaul (BH) network. This is a promising solution, since the expected short length of BH links enables the use of millimeter-wave (mmWave) frequencies to provide high-capacity BH. In this context, user association becomes challenging due to the multihop BH architecture, and therefore, new optimal solutions should be developed. Thus, in this paper, we study the user association problem aiming at the joint maximization of network energy efficiency (EE) and spectrum efficiency (SE), without compromising the user quality of service (QoS). The problem is formulated as an ε-constraint problem, which considers the transmit energy consumption both in the access network, i.e., the links between the users and their serving cells, and the BH links. The optimal Pareto-front solutions of the problem are analytically derived for different BH technologies, and insights are gained into the EE and SE tradeoff. The proposed optimal solutions, despite their high complexity, can be used as a benchmark for the performance evaluation of user association algorithms. We also propose a heuristic algorithm, which is compared with reference solutions under different traffic scenarios and BH technologies. Our results motivate the use of mmWave BH, whereas the proposed algorithm achieves near-optimal performance.

Due to the ever increasing data traffic demands, which are directly connected to increased energy consumption, it becomes challenging for operators to achieve capacity enhancement while limiting their electric bill. To that end, exploiting the context awareness of future cognitive networks is expected to play a key role. Next generation cellular networks are about to include a plethora of small cells, with users being able to communicate via multiple bands. Given that small cells are expected to be eventually as close as 50 m apart, not all of them will have a direct connection to the core network; thus, multihop communication through neighboring small cells may be required. In such architectures, the user association problem becomes challenging, with backhaul energy consumption being a definitive parameter. Thus, in this article, we study the user association problem in cognitive heterogeneous networks. We evaluate the existing approaches in terms of energy efficiency and show the potential of exploiting the available context-aware information (i.e., users' measurements and requirements, knowledge of the network architecture, and the available spectrum resources of each base station) to associate the users in an energy-efficient way, while maintaining high spectrum efficiency. Our study considers both the access network and backhaul energy consumption, while the performance of the association algorithms is evaluated under two different case study scenarios.

To meet the ever-increasing traffic demands, future cellular networks are about to include a plethora of small cells (SCs), with user equipments (UEs) being able of communicating via multiple bands. Given that SCs are expected to be eventually as close as 50 m apart, some of them will not have a direct connection to the core network, and thus will forward their traffic to the neighboring SCs until they reach it. In such architectures, the user association problem becomes challenging with backhaul (BH) energy consumption playing a key role. Thus, in this paper, we study the user association problem aiming at maximizing the network energy efficiency. The problem is formulated as an optimization problem, which is NP-hard. Therefore, we propose a cognitive heuristic algorithm that exploits context-aware information (i.e., UE measurements and requirements, the HetNet architecture knowledge and the available spectrum resources of each base station (BS)) to associate the UEs in an energy-efficient way, while considering both the access and the BH energy consumption. We evaluate the performance of the proposed algorithm under two study-case scenarios and we prove that it achieves significantly higher energy efficiency than the reference algorithms, while maintaining high spectral efficiency.

The escalating demands of next-generation, beyond5G services necessitate innovative approaches to dynamic resource management, critical for satisfying end-user expectations and maintaining quality of service (QoS). This study leverages the Open Radio Access Network (O-RAN) architecture's flexibility and programmability to introduce a novel deep reinforcement learning (DRL) strategy for QoS-aware intraslice resource allocation. By employing intelligent agents and a Deep Q-Network (DQN)-based framework, our approach precisely tailors resource distribution within O-RAN, optimizing for enhanced mobile broadband (eMBB) and ultra-reliable lowlatency communications (URLLC) slices. The proposed method, featuring intelligent QoS-aware resource allocation (IQRA) and its low-complexity variant (LIQRA), demonstrates significant throughput improvements for eMBB by 11.5% compared to state-of-the-art (SOTA) methods and reduces URLLC latency by 19.94% and 16.54%, achieving up to 45.5% lower latency than baseline. A streamlined algorithm effectively reduces computational complexity, ensuring robust performance under resource constraints. Simulation results underscore the algorithm's ability to substantially enhance 5G network slice performance, offering a parameterized solution for user association in O-RAN networks using DRL. This research not only meets high key performance indicators (KPIs) but also advances edge intelligence, fostering a more responsive network ecosystem.

5G mobile networks are envisioned to substantiate new vertical services with diverse performance requirements. Slicing in the radio access network (RAN) promises an efficient solution for these diversified needs of 5G networks, which foresees the separation of the base station functionality between the central unit (CU) and the distributed remote radio heads. In this article, we formulate a mixed integer programming (MIP) framework that maximizes the throughput by jointly selecting the optimal functional split and the routing path from a connected user equipment to the CU, while satisfying the agreed service level agreements (SLAs) of each service. Furthermore, we propose an effective heuristic, SlicedRAN, which creates isolated RAN slices premised on the service requirements connected through a fronthaul/backhaul (FH/BH) network and obtains near-optimal solutions in a short computing time compared to the MIP framework. Our results show that there is a tradeoff between the architecture of the FH/BH network and the minimum SLA of each slice, which provides a solution to efficiently design a virtualized network infrastructure. According to the results, the SlicedRAN outperforms existing state-of-the-art up to 112% gain in throughput. Results are shown close to the optimal results, with a loss below 5%.